Workshop
We will carry out several steps
- Import data and merge statistical results with normalised values -
- Add gene information from xenbase +
- Add gene information from Xenbase (Fisher et al. 2023) + +
- log2 transform the data
- Write the significant genes to file
- View the relationship between samples using PCA
- Visualise the expression of the most significant genes using a heatmap @@ -390,123 +393,279 @@
I got the information from the Xenbase information pages under Data Reports | Gene Information
+This is listed: Xenbase Gene Product Information [readme] gzipped gpi (tab separated)
+Click on the readme link to see the file format and columns
+I downloaded xenbase.gpi.gz, unzipped it, removed header lines and the Xenopus tropicalis (taxon:8364) entries and saved it as xenbase_info.xlsx
+- Visual all the results with a volcano plot
Workshop
We need to import both the normalised counts and the statistical results. We will need all of these for the visualisation and interpretation.
🎬 Import files saved from last week from the results folder: S30_normalised_counts.csv and S30_results.csv. I used the names s30_count_norm and s30_results for the dataframes.
🎬 Remind yourself what is in the rows and columns and the structure of the dataframes (perhaps using glimpse())
+
+
+
+
+
+
+
+
+
+
+
+#---CODING ANSWER---
+#| include: false
+glimpse(s30_count_norm)
+
+Rows: 10,136
+Columns: 7
+$ S30_C_5 <dbl> 228.092879, 480.016357, 111.209463, 48.795989, 439.163…
+$ S30_C_6 <dbl> 222.114104, 498.170204, 81.441838, 45.480507, 551.0545…
+$ S30_C_A <dbl> 238.647198, 668.212153, 83.141604, 53.118247, 282.5274…
+$ S30_F_5 <dbl> 251.794947, 453.047112, 98.328684, 70.759894, 427.3162…
+$ S30_F_6 <dbl> 239.567397, 481.986786, 81.757127, 44.681221, 539.9773…
+$ S30_F_A <dbl> 245.10704, 607.96159, 62.47827, 36.04515, 279.95069, 1…
+$ xenbase_gene_id <chr> "XB-GENE-1000007", "XB-GENE-1000023", "XB-GENE-1000062…
+
+
+
+
+
+
+#---CODING ANSWER---
+#| include: false
+glimpse(s30_results)
+
+Rows: 10,136
+Columns: 7
+$ baseMean <dbl> 237.553928, 531.565700, 86.392830, 49.813502, 419.9983…
+$ log2FoldChange <dbl> 0.096601855, -0.089588528, -0.192811203, -0.008858703,…
+$ lfcSE <dbl> 0.2079396, 0.1557384, 0.3253216, 0.4342614, 0.1685420,…
+$ stat <dbl> 0.46456683, -0.57525007, -0.59267874, -0.02039947, -0.…
+$ pvalue <dbl> 0.64224169, 0.56512218, 0.55339617, 0.98372471, 0.8699…
+$ padj <dbl> 0.9998970, 0.9998970, 0.9998970, 0.9998970, 0.9998970,…
+$ xenbase_gene_id <chr> "XB-GENE-1000007", "XB-GENE-1000023", "XB-GENE-1000062…It is useful to have this information in a single dataframe to which we will add the gene information from xenbase. Having all the information together will make it easier to interpret the results and select genes of interest.
🎬 Merge the two dataframes:
-
+
+
# merge the results with the normalised counts
+# merge the results with the normalised counts
s30_results <- s30_count_norm |>
left_join(s30_results, by = "xenbase_gene_id")
This means you have the counts for each sample along with the statistical results for each gene.
-Add gene information from xenbase
-This information comes from the Xenbase information pages
---xenbase Gene Product Information [readme]gzipped gpi (tab separated)
-
The xenbase.gpi.gz file contains gene product information for species specific Xenbase genes.
-If you click on the readme link you can see information telling you that the file is in the Gene Product information 2.1 format and is provided with gzip compression. gene product information for both Xenopus tropicalis (taxon:8364) and Xenopus laevis (taxon:8355)
-🎬 If you want to
-gunzip xenbase.gpi.gz
-less xenbase.gpi
-qAdd gene information from Xenbase
+-
+
If you want to emulate what I did you can use the following commands in the terminal after downloading the file:
+gunzip xenbase.gpi.gz
+less xenbase.gpi
+qgunzip unzips the file and less allows you to view the file. q quits the viewer. You will see the header lines and that the file contains both Xenopus tropicalis and Xenopus laevis. I read the file in with read_tsv (skipping the first header lines) then filtered out the Xenopus tropicalis entries, dropped some columns and saved the file as an excel file.
However, I have already done this for you and saved the file as xenbase_info.xlsx in the meta folder. We will import this file and join it to the results dataframe.
🎬 Load the readxl (Wickham and Bryan 2023) package:
🎬 Import the Xenbase gene information file:
-
+
+gene_info <- read_excel("meta/xenbase_info.xlsx") gene_info <- read_excel("meta/xenbase_info.xlsx") You should view the resulting dataframe to see what information is available. You can use glimpse() or View().
🎬 Merge the gene information with the results:
-
+
# join the gene info with the results
+# join the gene info with the results
s30_results <- s30_results |>
left_join(gene_info, by = "xenbase_gene_id")
We will also find it useful to import the metadata that maps the sample names to treatments. This will allow us to label the samples in the visualisations.
+🎬 Import the metadata that maps the sample names to treatments:
-
-
# Import metadata that maps the sample names to treatments
+# Import metadata that maps the sample names to treatments
meta <- read_table("meta/frog_meta_data.txt")
-row.names(meta) <- meta$sample_id
-We only need the s30
-
-
-log2 transformed normalised counts needed for data viz
-log2 transform the counts in s30_count_norm add a tiny amount to avoid log(0)
+log2 transform the data
+We use the normalised counts for data visualisations so that the comparisons are meaningful. Since the fold changes are given is log2 it is useful to log2 transform the normalised counts too. We will add columns to the dataframe with these transformed values. Since we have some counts of 0 we will add a tiny amount to avoid -Inf values.
🎬 log2 transform the normalised counts:
-
-
# log2 transform the counts in s30_count_norm
-# add a tiny amount to avoid log(0)
+# log2 transform the counts plus a tiny amount to avoid log(0)
s30_results <- s30_results |>
mutate(across(starts_with("s30"),
\(x) log2(x + 0.001),
.names = "log2_{.col}"))
read more about across() and anonymous functions from my posit::conf(2023) workshop
We now have datatframe with all the info we need, normalised counts, log2 normalised counts, statistical comparisons with fold changes and p values, information about the gene other than just the id
+This is a wonderful bit or R wizardy. We are using the across() function to apply a transformation to multiple columns. We have selected all the columns that start with s30. The \(x) is an “anonymous” function that takes the value of the column and adds 0.001 to it before applying the log2() function. The .names = "log2_{.col}" argument tells across() to name the new columns with the prefix log2_ followed by the original column name. You can read more about across() and anonymous functions from my posit::conf(2023) workshop
I recommend viewing the dataframe to see the new columns.
+We now have dataframe with all the information we need: normalised counts, log2 normalised counts, statistical comparisons with fold changes and p values, and information about the gene other than just the id.
Write the significant genes to file
+We will create dateframe of the signifcant genes and wrte them to file. These are the files you want to examine in more detail along with the visualisations to select your genes of interest.
+🎬 Create a dataframe of the genes significant at the 0.01 level:
-
+
+s30_results_sig0.01 <- s30_results |>
- filter(padj <= 0.01)
-# 59 geness30_results_sig0.01 <- s30_results |>
+ filter(padj <= 0.01)🎬 Write the dataframe to file
-
+
# write to csv file
+#---CODING ANSWER---
+#| include: false
+# write to csv file
write_csv(s30_results_sig0.01,
file = "results/s30_results_sig0.01.csv")
🎬 Create a dataframe of the genes significant at the 0.05 level and write to file:
-
-
s30_results_sig0.05 <- s30_results |>
+#---CODING ANSWER---
+#| include: false
+s30_results_sig0.05 <- s30_results |>
filter(padj <= 0.05)
-# 117 genes
-
-
+# write to csv file
+# write to csv file
write_csv(s30_results_sig0.05,
file = "results/s30_results_sig0.05.csv")❓How many genes are significant at the 0.01 and 0.05 levels?
+ + +View the relationship between samples using PCA
-we do this on the log2 transformed normalised counts or the regularized the log transformed counts
-transpose the data we are reducing he number of dimensions from 10136 to 6
+We have 10,136 genes in our dataset. PCA will allow us to plot our samples in the “gene expression” space so we can see if FGF-treated sample cluster together and control samples cluster together as we would expect. We do this on the log2 transformed normalised counts.
+Our data have genes in rows and samples in columns which is a common organisation for gene expression data. However, PCA expects samples in rows and genes, the variables, in columns. We can transpose the data to get it in the correct format.
+🎬 Transpose the log2 transformed normalised counts:
-
+
s30_log2_trans <- s30_results |>
+s30_log2_trans <- s30_results |>
select(starts_with("log2_")) |>
t() |>
data.frame()
We have used the select() function to select all the columns that start with log2_. We then use the t() function to transpose the dataframe. We then convert the resulting matrix to a dataframe using data.frame(). If you view that dataframe you’ll see it has default column name which we can fix using colnames() to set the column names to the Xenbase gene ids.
🎬 Set the column names to the Xenbase gene ids:
-
+
-colnames(s30_log2_trans) <- s30_results$xenbase_gene_idcolnames(s30_log2_trans) <- s30_results$xenbase_gene_idperform PCA using standard functions
+🎬 Perform PCA on the log2 transformed normalised counts:
+The rank. argument tells prcomp() to only calculate the first 4 principal components. This is useful for visualisation as we can only plot in 2 or 3 dimensions. We can see the results of the PCA by viewing the summary() of the pca object.
-
+
-summary(pca)summary(pca)
-
Importance of components:
- PC1 PC2 PC3 PC4 PC5 PC6
-Standard deviation 62.3983 45.6748 44.5555 33.3601 32.5295 2.405e-13
-Proportion of Variance 0.3841 0.2058 0.1959 0.1098 0.1044 0.000e+00
-Cumulative Proportion 0.3841 0.5899 0.7858 0.8956 1.0000 1.000e+00Importance of first k=4 (out of 6) components:
+ PC1 PC2 PC3 PC4
+Standard deviation 64.0124 47.3351 38.4706 31.4111
+Proportion of Variance 0.4243 0.2320 0.1532 0.1022
+Cumulative Proportion 0.4243 0.6562 0.8095 0.9116Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 Standard deviation 57.1519 50.3285 43.8382 35.4644 33.9440 2.403e-13 Proportion of Variance 0.3224 0.2500 0.1897 0.1241 0.1137 0.000e+00 Cumulative Proportion 0.3224 0.5724 0.7621 0.8863 1.0000 1.000e+00
-remove log2 from row.names(s30_log2_trans) to label the pca results
+The Proportion of Variance tells us how much of the variance is explained by each component. We can see that the first component explains 0.4243 of the variance, the second 0.2320, and the third 0.1532. Together the first three components explain nearly 81% of the total variance in the data. Plotting PC1 against PC2 will capture about 66% of the variance which is likely much better than we would get plotting any two genes against each other. To plot the PC1 against PC2 we will need to extract the PC1 and PC2 score from the pca object and add labels for the samples.
+🎬 Remove log2 from the row names:
-
+
+sample_id <- row.names(s30_log2_trans) |> str_remove("log2_")sample_id <- row.names(s30_log2_trans) |> str_remove("log2_")🎬 Create a dataframe of the PC1 and PC2 scores which are in pca$x and add the sample ids:
-
-
pca_labelled <- data.frame(pca$x,
+pca_labelled <- data.frame(pca$x,
sample_id)
merge with metadata so we can label points by treatment and sib group
+🎬 Merge with the metadata so we can label points by treatment and sibling pair:
-
+
pca_labelled <- pca_labelled |>
+pca_labelled <- pca_labelled |>
left_join(meta_s30,
by = "sample_id")
Since the metadata contained the sample ids, it was especially important to remove the log2_ from the row names so that the join would work. The dataframe should look like this:
+
+
+
+
+
+| PC1 | +PC2 | +PC3 | +PC4 | +sample_id | +stage | +treatment | +sibling_rep | +
|---|---|---|---|---|---|---|---|
| -76.38391 | +0.814699 | +-60.728327 | +-5.820669 | +S30_C_5 | +stage_30 | +control | +five | +
| -67.02571 | +25.668563 | +51.476835 | +28.480254 | +S30_C_6 | +stage_30 | +control | +six | +
| -14.02772 | +-78.474054 | +15.282058 | +-9.213076 | +S30_C_A | +stage_30 | +control | +A | +
| 47.60726 | +49.035510 | +-19.288753 | +20.928290 | +S30_F_5 | +stage_30 | +FGF | +five | +
| 26.04954 | +32.914201 | +20.206072 | +-55.752818 | +S30_F_6 | +stage_30 | +FGF | +six | +
| 83.78054 | +-29.958919 | +-6.947884 | +21.378020 | +S30_F_A | +stage_30 | +FGF | +A | +
🎬 Plot PC1 against PC2 and colour by sibling pair and shape by treatment:
-
+
-pca <- pca_labelled |>
+pca <- pca_labelled |>
ggplot(aes(x = PC1, y = PC2,
colour = sibling_rep,
shape = treatment)) +
@@ -517,11 +676,16 @@ Workshop
scale_shape_manual(values = c(21, 19),
name = NULL,
labels = c("Control", "FGF-Treated")) +
- theme_classic()
+ theme_classic()
+pca
+
There is a bit of separation between treatments on PCA2 not that it isn’t easy to draw strong conclusions on the basis of 3 points
+There is a good separation between treatments on PCA1. The sibling pairs do not seem to cluster together.
+🎬 Save the plot to file:
-
ggsave("figures/frog-s30-pca.png",
+ggsave("figures/frog-s30-pca.png",
plot = pca,
height = 3,
width = 4,
@@ -531,22 +695,22 @@ Workshop
Visualise the expression of the most significant genes using a heatmap
only should do on sig genes. but use the log 2 normalised values
-mat <- s30_results_sig0.01 |>
+mat <- s30_results_sig0.01 |>
select(starts_with("log2_")) |>
as.matrix()
-rownames(mat) <- s30_results_sig0.01$xenbase_gene_symbol
+rownames(mat) <- s30_results_sig0.01$xenbase_gene_symbol
-n_treatment_clusters <- 2
+n_treatment_clusters <- 2
n_gene_clusters <- 2
-heatmaply(mat,
+heatmaply(mat,
scale = "row",
hide_colorbar = TRUE,
k_col = n_treatment_clusters,
@@ -557,23 +721,23 @@ Workshop
labRow = rownames(mat),
heatmap_layers = theme(axis.line = element_blank()))
-
-
+
+
Visualise all the results with a volcano plot
colour the points if padj < 0.05 and log2FoldChange > 1
-s30_results <- s30_results |>
+
-vol <- s30_results |>
+vol <- s30_results |>
ggplot(aes(x = log2FoldChange,
y = log10_padj,
colour = interaction(sig, bigfc))) +
@@ -599,7 +763,7 @@ Workshop
theme(legend.position = "none")
-ggsave("figures/frog-s30-volcano.png",
+ggsave("figures/frog-s30-volcano.png",
plot = vol,
height = 4.5,
width = 4.5,
@@ -617,54 +781,1586 @@ Workshop
Import
-🎬 Import
-🎬 Combine the two dataframes (minus the gene ids) into one dataframe called prog_hspc:
+We need to import both the normalised counts and the statistical results. We will need all of these for the visualisation and interpretation.
+🎬 Import the normalised counts for the Prog and HSPC cell types. I used the names prog and hspc for the dataframes.
+🎬 Combine the two dataframes (minus one set of gene ids) into one dataframe called prog_hspc:
-# combine into one dataframe dropping one of the gene id columns
+# combine into one dataframe dropping one of the gene id columns
prog_hspc <- bind_cols(prog, hspc[-1])
-🎬 …
+🎬 Import the statistical results in results/prog_hspc_results.csv. I used the name prog_hspc_results for the dataframe.
+🎬 Remind yourself what is in the rows and columns and the structure of the dataframes (perhaps using glimpse())
-# import the DE results
-prog_hspc_results <- read_csv("results/prog_hspc_results.csv")
-
-🎬 …
+#---CODING ANSWER---
+#| include: false
+glimpse(prog_hspc)
+
+Rows: 280
+Columns: 1,500
+$ ensembl_gene_id <chr> "ENSMUSG00000004730", "ENSMUSG00000027962", "ENSMUSG00…
+$ Prog_001 <dbl> 0.000000, 0.000000, 2.447692, 0.000000, 2.447692, 1.07…
+$ Prog_002 <dbl> 0.0000000, 0.7859542, 9.8669873, 0.0000000, 7.5139828,…
+$ Prog_003 <dbl> 0.000000, 1.049924, 9.466541, 0.000000, 7.636827, 1.04…
+$ Prog_004 <dbl> 1.032808, 0.000000, 2.639234, 0.000000, 2.639234, 0.00…
+$ Prog_006 <dbl> 0.0000000, 0.9376688, 8.9509200, 0.0000000, 0.5437090,…
+$ Prog_007 <dbl> 0.0000000, 0.7008173, 2.2487025, 0.0000000, 2.0451378,…
+$ Prog_008 <dbl> 0.0000000, 0.0000000, 9.8216688, 0.0000000, 7.9747826,…
+$ Prog_009 <dbl> 0.0000000, 0.0000000, 10.3888553, 0.0000000, 4.4437936…
+$ Prog_010 <dbl> 0.000000, 0.000000, 3.277715, 1.453985, 6.670995, 1.45…
+$ Prog_011 <dbl> 0.000000, 0.000000, 9.329603, 0.000000, 1.756729, 1.13…
+$ Prog_012 <dbl> 1.909353, 1.909353, 3.588040, 0.000000, 0.000000, 0.00…
+$ Prog_013 <dbl> 0.0000000, 1.2104051, 0.7285849, 4.6985794, 7.8677891,…
+$ Prog_014 <dbl> 0.0000000, 0.0000000, 8.6212214, 0.0000000, 2.4391238,…
+$ Prog_015 <dbl> 0.0000000, 0.9646149, 1.5375869, 0.0000000, 0.0000000,…
+$ Prog_016 <dbl> 0.000000, 0.000000, 9.604794, 0.000000, 8.631105, 0.00…
+$ Prog_017 <dbl> 0.0000000, 0.6852295, 1.4976754, 0.0000000, 5.5232130,…
+$ Prog_018 <dbl> 0.000000, 0.000000, 8.815843, 0.000000, 1.775663, 0.00…
+$ Prog_019 <dbl> 0.0000000, 1.2988288, 0.7908933, 0.0000000, 4.5175504,…
+$ Prog_020 <dbl> 0.000000, 0.000000, 10.912794, 0.000000, 1.588903, 1.0…
+$ Prog_021 <dbl> 0.000000, 0.000000, 7.638658, 0.000000, 0.000000, 1.08…
+$ Prog_022 <dbl> 0.000000, 0.000000, 8.671409, 0.000000, 0.000000, 0.00…
+$ Prog_023 <dbl> 0.0000000, 2.1551569, 1.6194142, 0.0000000, 1.9120124,…
+$ Prog_024 <dbl> 1.717498, 0.000000, 9.023824, 0.000000, 7.928356, 0.00…
+$ Prog_025 <dbl> 0.000000, 2.368099, 8.553185, 0.000000, 5.413663, 1.62…
+$ Prog_026 <dbl> 0.000000, 0.000000, 3.648527, 1.190477, 1.190477, 0.00…
+$ Prog_027 <dbl> 0.0000000, 0.0000000, 0.8557008, 0.0000000, 1.3891677,…
+$ Prog_028 <dbl> 0.0000000, 0.0000000, 9.9095248, 0.0000000, 8.1128539,…
+$ Prog_029 <dbl> 1.152871, 1.152871, 5.870544, 0.000000, 3.060282, 0.00…
+$ Prog_030 <dbl> 0.000000, 2.598365, 9.181785, 2.127853, 8.651444, 0.00…
+$ Prog_031 <dbl> 0.0000000, 0.8413201, 9.2314616, 0.8413201, 2.5235954,…
+$ Prog_032 <dbl> 0.000000, 1.327820, 9.568213, 0.000000, 7.984181, 0.00…
+$ Prog_033 <dbl> 0.0000000, 1.3828699, 9.7312399, 0.0000000, 1.3828699,…
+$ Prog_035 <dbl> 0.0000000, 1.3171343, 9.3029109, 0.0000000, 6.4998843,…
+$ Prog_036 <dbl> 0.000000, 1.499263, 9.081582, 0.000000, 3.054489, 0.00…
+$ Prog_037 <dbl> 0.000000, 1.042273, 7.977819, 0.000000, 8.941148, 0.00…
+$ Prog_038 <dbl> 0.000000, 1.602107, 10.272361, 0.000000, 1.012878, 1.0…
+$ Prog_039 <dbl> 0.0000000, 0.0000000, 8.7123610, 0.0000000, 7.5301015,…
+$ Prog_040 <dbl> 0.000000, 0.000000, 2.054034, 8.442082, 2.054034, 1.36…
+$ Prog_042 <dbl> 0.000000, 0.000000, 10.345104, 1.507712, 1.507712, 0.0…
+$ Prog_043 <dbl> 0.0000000, 0.8515947, 10.7190561, 0.0000000, 1.3834907…
+$ Prog_044 <dbl> 0.000000, 1.139041, 1.767524, 1.139041, 5.258244, 0.00…
+$ Prog_045 <dbl> 0.6854953, 0.6854953, 9.9645500, 0.0000000, 8.6240290,…
+$ Prog_046 <dbl> 0.000000, 0.000000, 9.450093, 0.000000, 1.866185, 0.91…
+$ Prog_047 <dbl> 0.000000, 8.678573, 2.850378, 1.165030, 3.874419, 1.16…
+$ Prog_048 <dbl> 0.000000, 0.000000, 2.794052, 0.000000, 1.825335, 1.82…
+$ Prog_049 <dbl> 0.0000000, 0.6579981, 9.3946513, 0.0000000, 4.7857083,…
+$ Prog_050 <dbl> 0.0000000, 0.8386825, 2.0543847, 0.0000000, 6.6143445,…
+$ Prog_051 <dbl> 0.000000, 0.000000, 10.468034, 0.000000, 1.066147, 2.0…
+$ Prog_052 <dbl> 0.000000, 2.361305, 4.133679, 0.000000, 8.233765, 1.61…
+$ Prog_053 <dbl> 0.9224419, 0.9224419, 9.2857331, 0.0000000, 1.8820411,…
+$ Prog_055 <dbl> 0.0000000, 0.0000000, 3.4913807, 0.0000000, 6.6568051,…
+$ Prog_056 <dbl> 0.000000, 0.000000, 9.725035, 0.000000, 8.116732, 0.00…
+$ Prog_057 <dbl> 0.5967097, 0.0000000, 2.3499624, 0.0000000, 2.0262980,…
+$ Prog_058 <dbl> 0.000000, 0.000000, 10.114434, 0.000000, 5.739715, 0.0…
+$ Prog_059 <dbl> 0.000000, 2.038654, 8.871380, 0.000000, 7.819265, 1.02…
+$ Prog_060 <dbl> 0.000000, 1.776961, 9.102002, 0.000000, 2.549440, 1.14…
+$ Prog_061 <dbl> 0.0000000, 0.8198795, 10.4280461, 0.0000000, 1.3394322…
+$ Prog_062 <dbl> 0.000000, 0.000000, 9.964751, 0.000000, 5.990110, 0.00…
+$ Prog_063 <dbl> 0.000000, 8.002331, 2.337480, 1.597934, 3.477092, 0.00…
+$ Prog_064 <dbl> 0.000000, 1.007715, 10.123899, 1.007715, 6.687664, 0.0…
+$ Prog_065 <dbl> 0.0000000, 0.7570004, 2.1537620, 0.0000000, 8.4055113,…
+$ Prog_066 <dbl> 0.000000, 0.000000, 6.895361, 0.000000, 2.160890, 0.00…
+$ Prog_067 <dbl> 0.000000, 0.000000, 9.037415, 0.000000, 1.747210, 2.18…
+$ Prog_068 <dbl> 0.000000, 0.000000, 1.706107, 0.000000, 7.498985, 0.00…
+$ Prog_069 <dbl> 0.0000000, 0.0000000, 2.6246170, 0.8962338, 8.5297976,…
+$ Prog_070 <dbl> 0.000000, 1.475962, 3.022681, 1.475962, 9.416561, 0.00…
+$ Prog_071 <dbl> 0.000000, 1.028372, 0.000000, 1.028372, 1.622670, 0.00…
+$ Prog_072 <dbl> 0.000000, 1.478766, 10.580032, 0.000000, 8.863092, 2.1…
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+$ Prog_754 <dbl> 2.5350590, 0.9045812, 1.2066442, 0.0000000, 7.1346333,…
+$ Prog_757 <dbl> 0.000000, 0.000000, 8.521070, 0.000000, 3.008178, 0.00…
+$ Prog_758 <dbl> 0.000000, 1.027425, 10.650493, 0.000000, 6.577320, 0.0…
+$ Prog_759 <dbl> 0.0000000, 0.0000000, 1.1091425, 0.0000000, 6.6345901,…
+$ Prog_760 <dbl> 0.0000000, 7.3793575, 1.9694755, 0.7899915, 1.6722863,…
+$ Prog_761 <dbl> 0.000000, 1.623226, 2.856367, 0.000000, 8.266286, 1.02…
+$ Prog_762 <dbl> 0.0000000, 0.6583874, 1.1087820, 0.0000000, 7.1799196,…
+$ Prog_763 <dbl> 0.5774134, 0.5774134, 0.5774134, 0.0000000, 8.7733244,…
+$ Prog_764 <dbl> 0.0000000, 0.0000000, 2.9788233, 0.0000000, 3.5627696,…
+$ Prog_765 <dbl> 0.4887321, 6.8867263, 1.5926631, 0.0000000, 7.2306270,…
+$ Prog_767 <dbl> 0.0000000, 0.0000000, 9.0647725, 0.0000000, 9.5066596,…
+$ Prog_768 <dbl> 0.0000000, 0.8028968, 1.6932313, 0.0000000, 7.9467204,…
+$ Prog_769 <dbl> 0.0000000, 0.0000000, 1.8636357, 0.0000000, 2.8396004,…
+$ Prog_770 <dbl> 1.022676, 0.000000, 10.140358, 1.022676, 5.744589, 1.0…
+$ Prog_771 <dbl> 0.000000, 0.000000, 7.102581, 0.000000, 4.293110, 0.00…
+$ Prog_772 <dbl> 0.9806377, 0.9806377, 2.7740015, 0.0000000, 5.4537430,…
+$ Prog_774 <dbl> 0.000000, 0.000000, 1.939114, 0.000000, 6.398148, 1.09…
+$ Prog_775 <dbl> 0.0000000, 1.2167722, 1.8673123, 0.0000000, 2.4943937,…
+$ Prog_776 <dbl> 0.6768304, 0.0000000, 9.7760819, 0.0000000, 4.9786447,…
+$ Prog_777 <dbl> 0.0000000, 0.9423675, 1.5075959, 0.0000000, 0.9423675,…
+$ Prog_778 <dbl> 0.000000, 0.000000, 8.986217, 0.000000, 8.551634, 0.00…
+$ Prog_779 <dbl> 0.4211472, 0.4211472, 2.1341941, 0.0000000, 1.4302634,…
+$ Prog_780 <dbl> 0.5131073, 0.5131073, 9.3775848, 0.8908289, 7.1492370,…
+$ Prog_781 <dbl> 0.000000, 1.080345, 0.000000, 0.000000, 2.448417, 1.08…
+$ Prog_782 <dbl> 0.000000, 0.774577, 2.578895, 5.923573, 2.186957, 1.64…
+$ Prog_783 <dbl> 1.074572, 0.000000, 10.556887, 0.000000, 7.781406, 1.6…
+$ Prog_784 <dbl> 0.000000, 0.000000, 2.729708, 4.454398, 9.011330, 0.00…
+$ Prog_785 <dbl> 0.000000, 0.000000, 7.680402, 0.000000, 3.650143, 0.00…
+$ Prog_786 <dbl> 0.8739354, 0.0000000, 0.8739354, 0.8739354, 1.4143035,…
+$ Prog_787 <dbl> 1.738193, 0.000000, 3.370817, 0.000000, 9.072318, 1.73…
+$ Prog_788 <dbl> 0.7368005, 1.2221566, 9.1819824, 0.0000000, 8.2710517,…
+$ Prog_789 <dbl> 0.0000000, 6.2993373, 3.2689842, 0.0000000, 2.9839948,…
+$ Prog_790 <dbl> 0.641878, 0.000000, 11.027752, 0.641878, 1.926681, 1.6…
+$ Prog_791 <dbl> 7.354611, 0.888815, 3.115182, 0.000000, 4.584642, 0.00…
+$ Prog_792 <dbl> 0.0000000, 0.5602293, 1.7538261, 0.0000000, 7.1441796,…
+$ Prog_793 <dbl> 0.0000000, 0.6100055, 0.6100055, 0.6100055, 8.3024400,…
+$ Prog_794 <dbl> 0.0000000, 1.2839055, 7.7226264, 1.6564775, 9.9829054,…
+$ Prog_795 <dbl> 0.0000000, 0.8871069, 2.1367998, 0.0000000, 7.9894805,…
+$ Prog_796 <dbl> 0.000000, 0.000000, 2.296474, 0.000000, 6.034682, 0.00…
+$ Prog_797 <dbl> 0.000000, 0.000000, 1.464793, 2.176476, 3.531249, 0.00…
+$ Prog_798 <dbl> 0.0000000, 0.8285245, 1.7344840, 8.4849731, 2.1671674,…
+$ Prog_799 <dbl> 0.000000, 4.860757, 0.000000, 0.000000, 7.665351, 1.26…
+$ Prog_800 <dbl> 0.000000, 8.803266, 2.829021, 0.000000, 2.829021, 0.00…
+$ Prog_801 <dbl> 0.5808097, 0.0000000, 1.5766449, 3.4480774, 8.7052117,…
+$ Prog_802 <dbl> 0.000000, 1.898890, 8.412752, 0.000000, 8.778230, 0.00…
+$ Prog_803 <dbl> 0.000000, 2.543597, 7.878204, 0.000000, 3.708852, 0.00…
+$ Prog_804 <dbl> 0.000000, 0.000000, 9.348673, 1.233011, 2.954727, 0.00…
+$ Prog_805 <dbl> 0.000000, 0.000000, 8.315664, 0.000000, 9.369724, 0.00…
+$ Prog_806 <dbl> 0.000000, 0.935748, 0.935748, 0.935748, 7.896934, 0.00…
+$ Prog_807 <dbl> 0.0000000, 0.0000000, 8.6811767, 0.0000000, 8.1350692,…
+$ Prog_808 <dbl> 0.0000000, 0.0000000, 3.0377707, 0.0000000, 1.6615960,…
+$ Prog_809 <dbl> 0.6287654, 0.0000000, 2.2701368, 0.6287654, 1.6712775,…
+$ Prog_810 <dbl> 0.0000000, 0.5049969, 1.1746370, 0.0000000, 7.3167564,…
+$ Prog_811 <dbl> 0.000000, 1.329155, 9.629477, 0.000000, 5.626490, 0.00…
+$ Prog_812 <dbl> 0.000000, 3.245813, 9.354092, 0.000000, 7.095686, 0.00…
+$ Prog_813 <dbl> 0.0000000, 0.8803957, 10.2579542, 0.0000000, 5.5099063…
+$ Prog_814 <dbl> 0.0000000, 0.0000000, 9.3443362, 0.8521854, 2.7304517,…
+$ Prog_815 <dbl> 0.000000, 0.000000, 9.145702, 0.000000, 1.640454, 1.04…
+$ Prog_816 <dbl> 0.0000000, 1.5005517, 7.5725424, 0.0000000, 8.4454565,…
+$ Prog_817 <dbl> 0.000000, 0.617522, 1.876247, 0.000000, 1.876247, 0.61…
+$ Prog_818 <dbl> 0.000000, 0.000000, 8.817434, 0.000000, 5.110650, 0.00…
+$ Prog_819 <dbl> 0.0000000, 0.0000000, 9.9789196, 0.0000000, 7.2226335,…
+$ Prog_820 <dbl> 0.0000000, 0.0000000, 7.8202471, 0.6094958, 6.5794013,…
+$ Prog_821 <dbl> 0.000000, 0.527402, 9.347671, 0.000000, 7.134023, 0.00…
+$ Prog_822 <dbl> 0.000000, 0.000000, 10.830559, 0.000000, 5.611126, 0.7…
+$ Prog_823 <dbl> 0.000000, 2.368033, 6.108218, 0.000000, 3.421181, 0.00…
+$ Prog_824 <dbl> 0.0000000, 0.0000000, 3.2970935, 0.0000000, 7.2393470,…
+$ Prog_825 <dbl> 0.614008, 0.000000, 9.102087, 0.000000, 7.067777, 0.61…
+$ Prog_826 <dbl> 0.0000000, 0.0000000, 10.0184296, 0.0000000, 0.8930072…
+$ Prog_827 <dbl> 0.000000, 1.153240, 9.651103, 0.000000, 2.224160, 0.00…
+$ Prog_828 <dbl> 0.0000000, 0.0000000, 1.4730404, 0.0000000, 7.1366525,…
+$ Prog_829 <dbl> 0.000000, 0.000000, 2.002737, 0.679441, 6.402776, 0.00…
+$ Prog_830 <dbl> 0.0000000, 0.0000000, 9.9350866, 0.7122405, 2.0675466,…
+$ Prog_831 <dbl> 0.0000000, 0.7518682, 8.8581337, 0.0000000, 1.2436351,…
+$ Prog_832 <dbl> 0.0000000, 0.9591086, 1.9382199, 0.0000000, 7.2304444,…
+$ Prog_833 <dbl> 0.0000000, 0.8376881, 3.0151862, 0.0000000, 3.0151862,…
+$ Prog_834 <dbl> 0.0000000, 0.0000000, 2.4274006, 0.0000000, 5.4373805,…
+$ Prog_835 <dbl> 0.0000000, 0.9809832, 10.5344381, 0.0000000, 2.5531271…
+$ Prog_836 <dbl> 0.0000000, 0.5020465, 1.2967651, 0.5020465, 1.5225555,…
+$ Prog_837 <dbl> 0.0000000, 0.9850818, 9.8963349, 0.0000000, 0.0000000,…
+$ Prog_838 <dbl> 0.000000, 0.000000, 9.547417, 0.000000, 1.987452, 1.31…
+$ Prog_839 <dbl> 0.0000000, 0.0000000, 8.0095143, 0.0000000, 6.1786824,…
+$ Prog_840 <dbl> 0.0000000, 0.6485416, 2.4700583, 0.0000000, 8.4397289,…
+$ Prog_841 <dbl> 0.000000, 2.018049, 9.332316, 0.000000, 6.751711, 0.00…
+$ Prog_842 <dbl> 0.0000000, 0.0000000, 8.9760410, 0.0000000, 6.6732008,…
+$ Prog_843 <dbl> 0.0000000, 0.0000000, 2.4085725, 0.0000000, 3.5947323,…
+$ Prog_844 <dbl> 1.153513, 1.153513, 10.215305, 0.000000, 2.832433, 0.0…
+$ Prog_845 <dbl> 0.0000000, 0.0000000, 1.5265633, 0.0000000, 5.0433788,…
+$ Prog_846 <dbl> 0.000000, 0.000000, 2.924854, 2.924854, 7.842922, 1.67…
+$ Prog_847 <dbl> 0.0000000, 0.8935156, 2.1475639, 0.8935156, 8.6586321,…
+$ Prog_848 <dbl> 0.000000, 0.000000, 9.603595, 0.000000, 3.859249, 0.00…
+$ Prog_849 <dbl> 1.653308, 2.403562, 4.344108, 0.000000, 2.403562, 1.65…
+$ Prog_850 <dbl> 0.000000, 0.000000, 9.300996, 0.000000, 7.843614, 0.00…
+$ Prog_851 <dbl> 0.000000, 1.120675, 9.108092, 0.000000, 7.050312, 1.12…
+$ Prog_852 <dbl> 0.000000, 0.000000, 9.579540, 0.000000, 3.070503, 0.00…
+$ HSPC_001 <dbl> 0.0000000, 0.0000000, 0.7390988, 0.7390988, 1.2254389,…
+$ HSPC_002 <dbl> 0.000000, 0.000000, 2.238601, 0.000000, 2.238601, 0.00…
+$ HSPC_003 <dbl> 0.0000000, 0.0000000, 0.9929154, 0.9929154, 1.5755086,…
+$ HSPC_004 <dbl> 0.000000, 0.000000, 2.465632, 0.000000, 8.073635, 0.00…
+$ HSPC_006 <dbl> 0.000000, 0.000000, 1.941849, 0.000000, 1.941849, 0.00…
+$ HSPC_008 <dbl> 0.000000, 1.395221, 0.000000, 0.000000, 0.000000, 0.00…
+$ HSPC_009 <dbl> 0.000000, 1.684562, 2.934193, 0.000000, 5.635680, 0.00…
+$ HSPC_011 <dbl> 1.237406, 0.000000, 2.685036, 0.000000, 1.237406, 2.34…
+$ HSPC_012 <dbl> 0.000000, 0.000000, 2.517338, 0.000000, 7.378370, 1.74…
+$ HSPC_014 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.857888, 0.00…
+$ HSPC_015 <dbl> 0.000000, 2.398869, 0.000000, 7.717134, 4.177566, 2.39…
+$ HSPC_016 <dbl> 0.0000000, 0.9487581, 2.2390441, 0.0000000, 0.9487581,…
+$ HSPC_017 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.146951, 0.00…
+$ HSPC_018 <dbl> 0.000000, 1.248476, 2.701241, 0.000000, 8.633940, 1.24…
+$ HSPC_020 <dbl> 0.000000, 0.000000, 2.277106, 0.000000, 2.277106, 6.73…
+$ HSPC_021 <dbl> 0.000000, 0.000000, 2.996123, 0.000000, 8.043618, 2.49…
+$ HSPC_022 <dbl> 0.000000, 1.211071, 1.860045, 1.211071, 7.334868, 8.67…
+$ HSPC_023 <dbl> 0.000000, 0.000000, 1.539553, 0.000000, 9.321612, 1.53…
+$ HSPC_024 <dbl> 0.000000, 0.000000, 1.692191, 0.000000, 9.143541, 0.00…
+$ HSPC_025 <dbl> 1.571690, 0.000000, 3.152201, 8.738315, 1.571690, 0.00…
+$ HSPC_026 <dbl> 0.000000, 0.000000, 1.384631, 0.000000, 1.384631, 0.00…
+$ HSPC_027 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.9302884, 7.0772133,…
+$ HSPC_028 <dbl> 0.0000000, 0.8288203, 0.8288203, 0.0000000, 1.7349575,…
+$ HSPC_030 <dbl> 0.000000, 1.118674, 1.118674, 0.000000, 5.508301, 0.00…
+$ HSPC_031 <dbl> 0.0000000, 0.0000000, 2.9918679, 0.0000000, 9.6855069,…
+$ HSPC_033 <dbl> 0.000000, 0.000000, 1.303195, 0.000000, 1.303195, 0.00…
+$ HSPC_034 <dbl> 0.0000000, 1.3093499, 2.6262158, 0.7983817, 8.3884282,…
+$ HSPC_035 <dbl> 0.000000, 2.175011, 0.000000, 0.000000, 2.649348, 0.00…
+$ HSPC_036 <dbl> 0.0000000, 0.0000000, 2.1381879, 5.5782959, 8.5678887,…
+$ HSPC_037 <dbl> 0.000000, 1.516975, 2.239950, 0.000000, 3.365905, 0.00…
+$ HSPC_038 <dbl> 0.000000, 0.000000, 1.364250, 1.364250, 0.000000, 0.00…
+$ HSPC_040 <dbl> 0.0000000, 1.5383598, 1.9474646, 9.3621654, 8.6231804,…
+$ HSPC_041 <dbl> 0.000000, 0.000000, 2.579199, 2.579199, 6.743574, 0.00…
+$ HSPC_042 <dbl> 0.0000000, 1.4051125, 1.7960064, 0.0000000, 0.8672577,…
+$ HSPC_043 <dbl> 0.000000, 0.000000, 3.553494, 1.341668, 6.491814, 7.93…
+$ HSPC_044 <dbl> 0.000000, 0.000000, 2.582992, 0.000000, 0.000000, 0.00…
+$ HSPC_045 <dbl> 0.000000, 0.000000, 1.346994, 0.000000, 0.000000, 2.03…
+$ HSPC_046 <dbl> 0.0000000, 0.8452114, 1.3746533, 0.0000000, 6.7513398,…
+$ HSPC_047 <dbl> 0.000000, 8.428296, 4.239537, 0.000000, 3.552610, 2.19…
+$ HSPC_048 <dbl> 0.000000, 1.108133, 1.727411, 8.235885, 6.865010, 2.98…
+$ HSPC_049 <dbl> 0.000000, 0.000000, 2.394418, 6.252069, 1.274540, 1.94…
+$ HSPC_050 <dbl> 0.000000, 0.000000, 3.086078, 0.000000, 6.466053, 0.00…
+$ HSPC_051 <dbl> 0.0000000, 0.0000000, 1.9634358, 0.0000000, 0.0000000,…
+$ HSPC_052 <dbl> 0.000000, 7.749301, 2.709016, 1.509063, 1.509063, 0.00…
+$ HSPC_053 <dbl> 0.0000000, 0.0000000, 2.4796614, 0.0000000, 5.3728144,…
+$ HSPC_054 <dbl> 0.000000, 0.000000, 1.158501, 0.000000, 2.231666, 1.15…
+$ HSPC_055 <dbl> 0.000000, 1.797473, 3.075245, 0.000000, 3.446842, 0.00…
+$ HSPC_056 <dbl> 0.000000, 2.016637, 3.720889, 0.000000, 8.325851, 6.21…
+$ HSPC_057 <dbl> 0.000000, 0.000000, 1.847003, 0.000000, 8.239896, 7.42…
+$ HSPC_058 <dbl> 1.398955, 0.000000, 3.199362, 0.000000, 2.916174, 0.00…
+$ HSPC_060 <dbl> 0.000000, 1.080287, 2.118815, 0.000000, 6.512085, 1.08…
+$ HSPC_061 <dbl> 0.000000, 1.725558, 2.985726, 0.000000, 1.725558, 2.48…
+$ HSPC_062 <dbl> 0.000000, 0.000000, 2.211545, 0.000000, 0.000000, 0.00…
+$ HSPC_063 <dbl> 0.000000, 1.514955, 2.237502, 0.000000, 1.514955, 2.23…
+$ HSPC_064 <dbl> 0.000000, 0.000000, 1.718347, 0.000000, 8.222925, 1.10…
+$ HSPC_065 <dbl> 0.0000000, 0.0000000, 0.9703625, 0.0000000, 2.2742114,…
+$ HSPC_066 <dbl> 0.000000, 0.000000, 2.564196, 5.716981, 9.399772, 1.78…
+$ HSPC_067 <dbl> 0.000000, 0.000000, 2.147724, 0.000000, 9.138862, 5.93…
+$ HSPC_068 <dbl> 0.000000, 1.870770, 2.658681, 0.000000, 0.000000, 0.00…
+$ HSPC_069 <dbl> 0.000000, 1.116169, 1.737865, 7.866496, 3.791268, 8.80…
+$ HSPC_070 <dbl> 0.0000000, 0.0000000, 1.4430580, 0.8949015, 4.5972951,…
+$ HSPC_071 <dbl> 0.0000000, 0.9271506, 0.0000000, 0.0000000, 1.8892984,…
+$ HSPC_072 <dbl> 1.744738, 0.000000, 0.000000, 0.000000, 7.077098, 0.00…
+$ HSPC_073 <dbl> 0.000000, 0.000000, 4.134335, 0.000000, 3.387315, 0.00…
+$ HSPC_074 <dbl> 0.000000, 0.000000, 1.703855, 0.000000, 3.326366, 1.70…
+$ HSPC_075 <dbl> 1.674178, 0.000000, 4.030303, 0.000000, 9.381928, 8.86…
+$ HSPC_076 <dbl> 0.000000, 0.000000, 2.821284, 0.000000, 6.891389, 1.14…
+$ HSPC_077 <dbl> 0.000000, 0.000000, 1.114630, 0.000000, 8.460932, 0.00…
+$ HSPC_078 <dbl> 0.000000, 2.209396, 4.387948, 0.000000, 6.627462, 2.68…
+$ HSPC_079 <dbl> 0.000000, 0.000000, 4.095390, 0.000000, 2.076810, 0.00…
+$ HSPC_080 <dbl> 0.000000, 1.012596, 2.828881, 0.000000, 1.012596, 0.00…
+$ HSPC_081 <dbl> 0.0000000, 1.2652832, 1.2652832, 0.0000000, 5.3328809,…
+$ HSPC_082 <dbl> 0.000000, 0.000000, 4.421943, 0.000000, 1.970627, 0.00…
+$ HSPC_083 <dbl> 1.895620, 0.000000, 4.114776, 0.000000, 10.864775, 7.8…
+$ HSPC_084 <dbl> 0.000000, 0.000000, 3.216417, 0.000000, 4.036024, 0.00…
+$ HSPC_085 <dbl> 0.000000, 0.000000, 1.934463, 0.000000, 0.000000, 1.93…
+$ HSPC_087 <dbl> 0.000000, 0.000000, 1.859256, 0.000000, 8.387078, 7.44…
+$ HSPC_088 <dbl> 0.000000, 0.000000, 2.091761, 1.061818, 1.666812, 0.00…
+$ HSPC_089 <dbl> 0.000000, 0.000000, 1.542804, 0.000000, 5.429591, 0.00…
+$ HSPC_090 <dbl> 0.0000000, 0.9583485, 0.0000000, 0.9583485, 7.8106423,…
+$ HSPC_094 <dbl> 0.000000, 1.001307, 3.002286, 0.000000, 1.001307, 0.00…
+$ HSPC_095 <dbl> 0.000000, 2.055264, 3.768162, 0.000000, 5.497828, 0.00…
+$ HSPC_096 <dbl> 0.000000, 0.000000, 3.020317, 0.000000, 4.694719, 0.00…
+$ HSPC_098 <dbl> 0.000000, 0.000000, 4.033084, 0.000000, 3.353704, 0.00…
+$ HSPC_099 <dbl> 0.000000, 0.000000, 1.355566, 8.533241, 1.355566, 7.54…
+$ HSPC_100 <dbl> 0.000000, 1.117108, 2.504994, 0.000000, 1.117108, 0.00…
+$ HSPC_101 <dbl> 0.000000, 1.007084, 3.182495, 0.000000, 6.058315, 0.00…
+$ HSPC_102 <dbl> 0.000000, 1.110882, 2.992525, 0.000000, 1.110882, 6.01…
+$ HSPC_103 <dbl> 0.000000, 0.000000, 3.444022, 3.444022, 3.444022, 6.84…
+$ HSPC_104 <dbl> 0.000000, 0.000000, 2.720362, 0.000000, 5.326705, 0.00…
+$ HSPC_105 <dbl> 0.000000, 0.000000, 2.953351, 0.000000, 2.128535, 0.00…
+$ HSPC_106 <dbl> 0.000000, 0.000000, 2.917053, 8.830809, 10.056331, 0.0…
+$ HSPC_107 <dbl> 0.000000, 1.539035, 2.266637, 1.539035, 2.266637, 0.00…
+$ HSPC_108 <dbl> 0.000000, 0.000000, 2.342599, 1.237188, 9.967793, 0.00…
+$ HSPC_109 <dbl> 0.000000, 1.595174, 3.183524, 1.595174, 6.584986, 0.00…
+$ HSPC_110 <dbl> 0.000000, 0.000000, 2.667372, 0.000000, 9.520268, 2.66…
+$ HSPC_111 <dbl> 0.000000, 1.495539, 2.691330, 3.780086, 1.495539, 0.00…
+$ HSPC_114 <dbl> 0.0000000, 0.0000000, 2.1872025, 0.0000000, 0.9172819,…
+$ HSPC_115 <dbl> 0.000000, 2.348960, 4.119213, 6.801590, 2.348960, 2.34…
+$ HSPC_117 <dbl> 0.000000, 0.000000, 2.662801, 0.000000, 2.662801, 0.00…
+$ HSPC_118 <dbl> 0.000000, 0.000000, 2.731892, 0.000000, 7.192924, 1.26…
+$ HSPC_119 <dbl> 0.000000, 2.432480, 3.827272, 0.000000, 2.432480, 8.53…
+$ HSPC_120 <dbl> 0.000000, 0.000000, 1.186672, 0.000000, 1.186672, 9.49…
+$ HSPC_121 <dbl> 0.000000, 0.000000, 4.264196, 0.000000, 2.822082, 0.00…
+$ HSPC_122 <dbl> 0.000000, 1.435059, 2.140080, 0.000000, 0.000000, 0.00…
+$ HSPC_123 <dbl> 0.000000, 0.000000, 2.244368, 0.000000, 6.093587, 0.00…
+$ HSPC_125 <dbl> 0.000000, 0.000000, 1.285018, 0.000000, 5.553951, 6.70…
+$ HSPC_126 <dbl> 0.0000000, 0.0000000, 1.5017883, 0.0000000, 2.6995085,…
+$ HSPC_127 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.822566, 0.00…
+$ HSPC_130 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.443009, 0.00…
+$ HSPC_131 <dbl> 0.000000, 0.000000, 2.384852, 0.000000, 4.161211, 0.00…
+$ HSPC_132 <dbl> 0.0000000, 0.7827224, 0.4438051, 0.0000000, 1.6604369,…
+$ HSPC_133 <dbl> 2.234359, 3.072289, 0.000000, 2.234359, 8.065004, 0.00…
+$ HSPC_134 <dbl> 0.000000, 2.768568, 0.000000, 0.000000, 6.641594, 0.00…
+$ HSPC_135 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.600652, 8.08…
+$ HSPC_136 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.962552, 0.00…
+$ HSPC_138 <dbl> 0.0000000, 0.7062338, 0.7062338, 0.0000000, 1.8180968,…
+$ HSPC_139 <dbl> 0.000000, 0.000000, 4.345514, 0.000000, 0.000000, 4.09…
+$ HSPC_140 <dbl> 0.000000, 0.000000, 4.660038, 0.000000, 0.000000, 0.00…
+$ HSPC_141 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.543589, 0.00…
+$ HSPC_142 <dbl> 0.000000, 0.000000, 2.523431, 0.000000, 0.000000, 0.00…
+$ HSPC_143 <dbl> 0.000000, 1.958674, 2.760027, 0.000000, 8.379422, 0.00…
+$ HSPC_144 <dbl> 0.000000, 0.000000, 1.075288, 0.000000, 0.000000, 0.00…
+$ HSPC_146 <dbl> 0.000000, 2.277948, 2.277948, 3.120975, 1.548407, 0.00…
+$ HSPC_148 <dbl> 0.000000, 0.000000, 2.428270, 0.000000, 0.000000, 0.00…
+$ HSPC_149 <dbl> 0.000000, 0.000000, 0.000000, 7.370990, 9.039081, 0.00…
+$ HSPC_151 <dbl> 0.000000, 0.000000, 1.942220, 0.000000, 6.477871, 0.00…
+$ HSPC_152 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.948497, 0.00…
+$ HSPC_153 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
+$ HSPC_154 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 6.567172, 0.00…
+$ HSPC_155 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 5.981102, 2.73…
+$ HSPC_156 <dbl> 0.0000000, 0.5544683, 0.5544683, 0.0000000, 1.2665659,…
+$ HSPC_157 <dbl> 0.000000, 1.992960, 3.313476, 0.000000, 6.056835, 2.79…
+$ HSPC_158 <dbl> 0.000000, 1.681202, 1.681202, 0.000000, 2.436668, 1.68…
+$ HSPC_159 <dbl> 0.000000, 0.000000, 0.000000, 4.189894, 9.099914, 9.05…
+$ HSPC_161 <dbl> 0.000000, 0.000000, 1.701367, 0.000000, 3.323133, 8.08…
+$ HSPC_162 <dbl> 0.715249, 0.715249, 1.191268, 0.000000, 6.603848, 1.54…
+$ HSPC_164 <dbl> 0.000000, 1.780116, 4.173269, 0.000000, 3.053739, 0.00…
+$ HSPC_165 <dbl> 0.000000, 0.000000, 2.521243, 0.000000, 1.752913, 2.52…
+$ HSPC_166 <dbl> 0.000000, 1.680351, 3.830900, 0.000000, 2.435661, 0.00…
+$ HSPC_168 <dbl> 0.000000, 0.000000, 2.587724, 0.000000, 1.587172, 0.00…
+$ HSPC_169 <dbl> 0.000000, 2.231654, 2.231654, 0.000000, 2.840202, 1.15…
+$ HSPC_170 <dbl> 0.000000, 2.323845, 3.702651, 0.000000, 0.000000, 0.00…
+$ HSPC_171 <dbl> 0.000000, 2.249267, 0.000000, 0.000000, 6.986101, 0.00…
+$ HSPC_172 <dbl> 0.0000000, 0.0000000, 1.2663678, 1.6361385, 3.8432543,…
+$ HSPC_173 <dbl> 0.000000, 0.000000, 2.202687, 0.000000, 2.202687, 0.00…
+$ HSPC_174 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.25…
+$ HSPC_175 <dbl> 0.000000, 0.000000, 2.214143, 0.000000, 3.959792, 1.49…
+$ HSPC_176 <dbl> 0.000000, 0.000000, 2.023933, 0.000000, 6.539273, 2.02…
+$ HSPC_177 <dbl> 0.000000, 1.971046, 1.971046, 0.000000, 3.664721, 0.00…
+$ HSPC_178 <dbl> 0.000000, 1.002574, 1.002574, 0.000000, 8.129245, 0.00…
+$ HSPC_179 <dbl> 0.0000000, 0.8360365, 1.7464921, 0.0000000, 2.3003274,…
+$ HSPC_180 <dbl> 0.000000, 0.000000, 2.753903, 0.000000, 6.624564, 6.08…
+$ HSPC_181 <dbl> 0.000000, 0.000000, 5.739487, 0.000000, 5.969896, 0.00…
+$ HSPC_182 <dbl> 0.000000, 0.000000, 3.778793, 0.000000, 3.245451, 3.24…
+$ HSPC_183 <dbl> 0.000000, 0.000000, 3.206129, 1.020477, 7.802033, 0.00…
+$ HSPC_185 <dbl> 0.000000, 0.000000, 5.514029, 6.498157, 9.703008, 0.00…
+$ HSPC_186 <dbl> 0.000000, 1.605849, 9.300294, 0.000000, 3.729280, 0.00…
+$ HSPC_187 <dbl> 0.000000, 0.000000, 3.016892, 0.000000, 8.206576, 2.18…
+$ HSPC_189 <dbl> 2.651507, 1.465231, 0.000000, 0.000000, 1.465231, 2.17…
+$ HSPC_190 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
+$ HSPC_191 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.406826, 0.00…
+$ HSPC_192 <dbl> 0.000000, 0.000000, 2.111369, 0.000000, 0.000000, 1.41…
+$ HSPC_193 <dbl> 0.0000000, 2.2721907, 2.9455106, 0.0000000, 9.1712813,…
+$ HSPC_195 <dbl> 0.0000000, 0.0000000, 2.4446955, 0.0000000, 1.4738612,…
+$ HSPC_196 <dbl> 0.000000, 1.379440, 2.888830, 0.000000, 0.000000, 0.00…
+$ HSPC_198 <dbl> 2.155150, 0.000000, 3.506202, 1.105265, 1.105265, 0.00…
+$ HSPC_199 <dbl> 1.676720, 0.000000, 2.102827, 4.774209, 2.102827, 1.06…
+$ HSPC_200 <dbl> 1.572132, 2.306518, 0.000000, 0.000000, 2.790837, 1.57…
+$ HSPC_202 <dbl> 1.3178909, 0.0000000, 0.0000000, 0.0000000, 1.6957804,…
+$ HSPC_203 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
+$ HSPC_204 <dbl> 0.000000, 1.342435, 1.342435, 0.000000, 2.487324, 1.34…
+$ HSPC_205 <dbl> 0.000000, 1.996780, 0.000000, 0.000000, 2.803674, 4.95…
+$ HSPC_206 <dbl> 1.076411, 0.000000, 1.076411, 0.000000, 1.076411, 0.00…
+$ HSPC_207 <dbl> 0.000000, 0.000000, 1.482035, 0.000000, 9.592779, 0.00…
+$ HSPC_208 <dbl> 0.000000, 0.000000, 3.104133, 0.000000, 2.262852, 2.26…
+$ HSPC_210 <dbl> 0.000000, 1.021329, 1.021329, 0.000000, 1.613332, 1.02…
+$ HSPC_211 <dbl> 0.000000, 0.000000, 3.567889, 1.351210, 6.755381, 2.03…
+$ HSPC_212 <dbl> 0.000000, 0.000000, 2.570024, 0.000000, 0.000000, 0.00…
+$ HSPC_213 <dbl> 0.000000, 1.935211, 1.935211, 0.000000, 1.270332, 1.27…
+$ HSPC_214 <dbl> 1.4766124, 0.0000000, 1.8775155, 0.0000000, 1.4766124,…
+$ HSPC_215 <dbl> 0.000000, 0.000000, 3.551326, 0.000000, 2.403099, 6.60…
+$ HSPC_216 <dbl> 0.0000000, 1.3146495, 0.8021597, 0.8021597, 6.2917837,…
+$ HSPC_218 <dbl> 0.000000, 2.567330, 3.440186, 0.000000, 3.980149, 0.00…
+$ HSPC_219 <dbl> 0.000000, 1.620715, 2.040160, 0.000000, 2.364726, 0.00…
+$ HSPC_220 <dbl> 0.000000, 0.000000, 4.645420, 0.000000, 6.439808, 0.00…
+$ HSPC_221 <dbl> 0.000000, 1.932925, 3.617433, 1.268520, 8.054842, 1.26…
+$ HSPC_222 <dbl> 0.000000, 2.630291, 3.270905, 0.000000, 2.157369, 1.44…
+$ HSPC_223 <dbl> 0.000000, 3.195293, 3.195293, 0.000000, 3.195293, 0.00…
+$ HSPC_224 <dbl> 0.000000, 0.000000, 3.235910, 0.000000, 2.381448, 3.23…
+$ HSPC_225 <dbl> 0.000000, 1.892444, 0.000000, 0.000000, 0.000000, 1.89…
+$ HSPC_227 <dbl> 0.000000, 0.000000, 2.269803, 0.000000, 1.541657, 0.00…
+$ HSPC_228 <dbl> 0.000000, 0.000000, 2.193480, 0.000000, 2.193480, 3.02…
+$ HSPC_229 <dbl> 0.000000, 2.483864, 4.276180, 0.000000, 3.348781, 0.00…
+$ HSPC_230 <dbl> 0.000000, 0.000000, 2.376256, 0.000000, 9.125464, 6.25…
+$ HSPC_231 <dbl> 0.000000, 2.442191, 3.838346, 0.000000, 10.045009, 2.4…
+$ HSPC_232 <dbl> 0.000000, 1.868824, 3.834078, 0.000000, 4.080153, 0.00…
+$ HSPC_233 <dbl> 0.000000, 0.000000, 3.219022, 0.000000, 3.219022, 0.00…
+$ HSPC_235 <dbl> 0.000000, 0.000000, 6.066406, 0.000000, 3.382760, 0.00…
+$ HSPC_236 <dbl> 0.000000, 0.000000, 3.747550, 0.000000, 5.733689, 3.08…
+$ HSPC_237 <dbl> 0.000000, 2.058995, 2.058995, 0.000000, 2.874584, 2.05…
+$ HSPC_239 <dbl> 0.000000, 2.206777, 3.951001, 0.000000, 3.951001, 0.00…
+$ HSPC_240 <dbl> 0.0000000, 0.0000000, 1.8347816, 0.0000000, 1.4390643,…
+$ HSPC_243 <dbl> 0.000000, 1.118272, 0.000000, 0.000000, 1.740598, 0.00…
+$ HSPC_244 <dbl> 0.7871557, 0.7871557, 0.7871557, 0.0000000, 8.1826024,…
+$ HSPC_245 <dbl> 0.000000, 1.459488, 0.000000, 0.000000, 1.459488, 0.00…
+$ HSPC_246 <dbl> 0.000000, 0.000000, 1.629406, 0.000000, 0.000000, 0.00…
+$ HSPC_247 <dbl> 0.000000, 0.000000, 4.129612, 0.000000, 6.233964, 3.74…
+$ HSPC_248 <dbl> 0.000000, 0.000000, 3.844645, 0.000000, 0.000000, 1.69…
+$ HSPC_249 <dbl> 0.000000, 0.000000, 1.595836, 0.000000, 1.595836, 2.33…
+$ HSPC_250 <dbl> 0.000000, 1.277977, 3.022356, 0.000000, 4.572877, 7.32…
+$ HSPC_251 <dbl> 0.0000000, 0.0000000, 2.2758529, 0.0000000, 1.9568480,…
+$ HSPC_253 <dbl> 0.000000, 0.000000, 1.265413, 0.000000, 8.470854, 0.00…
+$ HSPC_254 <dbl> 0.000000, 2.017054, 2.826829, 2.017054, 5.231539, 0.00…
+$ HSPC_255 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.8624297,…
+$ HSPC_256 <dbl> 0.824835, 1.346341, 9.308939, 0.000000, 1.346341, 0.82…
+$ HSPC_257 <dbl> 0.000000, 0.000000, 1.897882, 0.000000, 6.591455, 0.00…
+$ HSPC_258 <dbl> 0.0000000, 0.8526372, 0.0000000, 0.0000000, 7.0828772,…
+$ HSPC_261 <dbl> 0.0000000, 0.9300646, 0.9300646, 0.0000000, 1.4909411,…
+$ HSPC_263 <dbl> 1.537503, 0.000000, 1.537503, 0.000000, 0.000000, 2.26…
+$ HSPC_264 <dbl> 0.000000, 1.425741, 1.425741, 0.000000, 0.000000, 8.70…
+$ HSPC_265 <dbl> 0.000000, 2.672373, 4.308280, 0.000000, 3.554532, 7.62…
+$ HSPC_266 <dbl> 0.000000, 0.000000, 3.498974, 0.000000, 8.164818, 2.62…
+$ HSPC_267 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.9510360,…
+$ HSPC_268 <dbl> 0.0000000, 0.7786889, 3.0278105, 0.0000000, 1.9494543,…
+$ HSPC_269 <dbl> 0.0000000, 0.0000000, 1.9511222, 0.0000000, 2.5642396,…
+$ HSPC_270 <dbl> 0.0000000, 0.0000000, 2.2633366, 0.0000000, 6.3366603,…
+$ HSPC_271 <dbl> 1.351781, 1.351781, 4.404598, 1.351781, 2.037221, 6.96…
+$ HSPC_274 <dbl> 0.000000, 0.000000, 2.990962, 0.000000, 9.428474, 8.26…
+$ HSPC_275 <dbl> 0.5251597, 6.3066729, 1.8617796, 0.0000000, 6.2986480,…
+$ HSPC_276 <dbl> 0.000000, 1.155802, 2.836006, 0.000000, 0.000000, 8.56…
+$ HSPC_278 <dbl> 0.000000, 1.185387, 2.881903, 0.000000, 2.269850, 3.64…
+$ HSPC_279 <dbl> 3.563514, 1.487393, 2.204027, 0.000000, 7.109467, 1.48…
+$ HSPC_280 <dbl> 0.000000, 0.000000, 2.837358, 0.000000, 3.939531, 0.00…
+$ HSPC_281 <dbl> 3.488013, 0.000000, 3.488013, 0.000000, 3.488013, 0.00…
+$ HSPC_282 <dbl> 0.0000000, 1.1722813, 2.0476063, 0.0000000, 1.1722813,…
+$ HSPC_283 <dbl> 0.000000, 3.590649, 4.529495, 1.911441, 1.911441, 0.00…
+$ HSPC_285 <dbl> 0.0000000, 0.0000000, 3.0705949, 0.0000000, 8.1491892,…
+$ HSPC_286 <dbl> 0.000000, 0.000000, 2.259352, 0.000000, 2.259352, 0.00…
+$ HSPC_287 <dbl> 0.000000, 1.048672, 3.408389, 1.649500, 1.649500, 0.00…
+$ HSPC_288 <dbl> 0.0000000, 1.3879010, 3.0490024, 0.0000000, 8.9178718,…
+$ HSPC_289 <dbl> 0.000000, 0.000000, 3.641902, 0.000000, 3.940529, 0.00…
+$ HSPC_290 <dbl> 0.000000, 0.000000, 2.594545, 0.000000, 7.607623, 2.59…
+$ HSPC_291 <dbl> 0.000000, 1.175842, 1.175842, 0.000000, 9.326333, 0.00…
+$ HSPC_292 <dbl> 0.000000, 0.000000, 2.804302, 0.000000, 5.422785, 8.43…
+$ HSPC_293 <dbl> 1.077036, 0.000000, 2.711161, 7.608787, 4.047517, 1.07…
+$ HSPC_294 <dbl> 0.0000000, 0.0000000, 0.9161348, 0.0000000, 1.8723003,…
+$ HSPC_295 <dbl> 0.0000000, 1.8799200, 3.0264680, 0.9210673, 4.2380463,…
+$ HSPC_296 <dbl> 0.000000, 2.157001, 2.157001, 2.157001, 5.287566, 0.00…
+$ HSPC_297 <dbl> 0.000000, 1.687785, 2.444464, 0.000000, 0.000000, 0.00…
+$ HSPC_298 <dbl> 0.0000000, 0.7784226, 2.4036840, 0.0000000, 2.4036840,…
+$ HSPC_299 <dbl> 0.000000, 1.099428, 1.716067, 0.000000, 8.068194, 2.74…
+$ HSPC_300 <dbl> 0.0000000, 0.0000000, 2.0342975, 0.0000000, 2.0342975,…
+$ HSPC_301 <dbl> 0.000000, 3.073359, 6.232145, 0.000000, 4.938720, 0.00…
+$ HSPC_302 <dbl> 3.078836, 0.000000, 4.545226, 0.000000, 3.078836, 3.07…
+$ HSPC_303 <dbl> 0.000000, 0.000000, 3.009431, 0.000000, 7.544693, 0.00…
+$ HSPC_304 <dbl> 0.0000000, 1.5484583, 2.7602490, 0.0000000, 5.4055991,…
+$ HSPC_305 <dbl> 0.000000, 0.000000, 2.558256, 0.000000, 4.669580, 1.15…
+$ HSPC_306 <dbl> 0.000000, 0.000000, 3.185666, 0.000000, 5.689777, 3.33…
+$ HSPC_307 <dbl> 0.000000, 1.976472, 4.777034, 0.000000, 3.293648, 2.78…
+$ HSPC_308 <dbl> 0.000000, 0.000000, 3.975296, 6.548101, 3.526101, 0.00…
+$ HSPC_309 <dbl> 0.000000, 1.804026, 3.455193, 0.000000, 3.083349, 1.80…
+$ HSPC_310 <dbl> 1.743404, 0.000000, 2.510071, 0.000000, 2.510071, 1.74…
+$ HSPC_312 <dbl> 0.0000000, 1.4203264, 1.8133978, 0.0000000, 7.5148795,…
+$ HSPC_313 <dbl> 0.000000, 1.591589, 4.096808, 1.591589, 2.329876, 0.00…
+$ HSPC_314 <dbl> 0.000000, 0.000000, 3.527058, 1.634863, 3.236083, 7.90…
+$ HSPC_315 <dbl> 0.000000, 2.261631, 2.261631, 2.261631, 3.630638, 0.00…
+$ HSPC_317 <dbl> 0.000000, 2.335038, 3.715556, 0.000000, 2.335038, 0.00…
+$ HSPC_318 <dbl> 0.000000, 0.000000, 3.357051, 0.000000, 0.000000, 0.00…
+$ HSPC_320 <dbl> 0.000000, 0.000000, 3.189910, 2.339927, 3.479868, 3.18…
+$ HSPC_321 <dbl> 0.000000, 1.191188, 1.191188, 0.000000, 2.616741, 1.19…
+$ HSPC_322 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.9762562,…
+$ HSPC_323 <dbl> 0.0000000, 0.0000000, 0.9890293, 0.0000000, 2.5666314,…
+$ HSPC_324 <dbl> 0.000000, 1.088036, 2.728840, 0.000000, 2.728840, 0.00…
+$ HSPC_325 <dbl> 1.428402, 1.428402, 4.810635, 0.000000, 2.131908, 0.00…
+$ HSPC_326 <dbl> 0.000000, 0.000000, 5.321205, 0.000000, 6.303048, 0.00…
+$ HSPC_327 <dbl> 0.0000000, 0.6422346, 2.7240929, 0.0000000, 3.1457469,…
+$ HSPC_328 <dbl> 0.000000, 0.000000, 3.620927, 0.000000, 2.733670, 1.27…
+$ HSPC_329 <dbl> 1.954249, 0.000000, 3.800965, 0.000000, 1.954249, 0.00…
+$ HSPC_330 <dbl> 1.870221, 0.000000, 3.695045, 0.000000, 2.317254, 2.65…
+$ HSPC_331 <dbl> 0.000000, 0.000000, 2.953080, 0.000000, 2.953080, 0.00…
+$ HSPC_332 <dbl> 0.000000, 1.044806, 2.883335, 0.000000, 6.444771, 1.04…
+$ HSPC_333 <dbl> 0.8059525, 1.3199644, 1.3199644, 0.0000000, 1.9975449,…
+$ HSPC_334 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.931089, 0.00…
+$ HSPC_335 <dbl> 0.000000, 0.000000, 2.751133, 0.000000, 7.599859, 0.00…
+$ HSPC_336 <dbl> 0.000000, 0.000000, 1.959737, 0.000000, 0.000000, 3.27…
+$ HSPC_337 <dbl> 0.000000, 0.000000, 5.147478, 0.000000, 4.187618, 0.00…
+$ HSPC_338 <dbl> 0.0000000, 0.0000000, 10.1024666, 0.0000000, 7.9043216…
+$ HSPC_339 <dbl> 0.0000000, 0.9599543, 2.5175886, 5.1304754, 7.6196067,…
+$ HSPC_341 <dbl> 0.0000000, 0.0000000, 2.2497917, 7.5891885, 2.2497917,…
+$ HSPC_342 <dbl> 0.0000000, 0.7849552, 1.4893359, 0.4452176, 8.7833506,…
+$ HSPC_343 <dbl> 0.0000000, 0.7869497, 0.7869497, 0.7869497, 1.9640992,…
+$ HSPC_344 <dbl> 0.000000, 1.425890, 1.425890, 0.000000, 8.886625, 2.59…
+$ HSPC_345 <dbl> 0.000000, 0.000000, 2.093666, 1.397341, 0.000000, 1.39…
+$ HSPC_346 <dbl> 0.000000, 0.000000, 1.637347, 0.000000, 1.637347, 1.03…
+$ HSPC_348 <dbl> 0.000000, 1.733815, 2.768747, 7.794259, 2.166424, 1.73…
+$ HSPC_349 <dbl> 0.000000, 0.000000, 3.434533, 0.000000, 2.783890, 0.00…
+$ HSPC_350 <dbl> 0.000000, 0.000000, 4.536066, 5.820193, 0.000000, 0.00…
+$ HSPC_351 <dbl> 0.000000, 1.910297, 2.704369, 0.000000, 7.827990, 1.91…
+$ HSPC_352 <dbl> 0.000000, 0.000000, 1.855727, 0.000000, 0.000000, 0.00…
+$ HSPC_353 <dbl> 0.000000, 0.000000, 0.899194, 0.000000, 0.000000, 0.89…
+$ HSPC_354 <dbl> 0.000000, 2.331123, 4.869071, 0.000000, 7.191315, 0.00…
+$ HSPC_356 <dbl> 0.000000, 0.000000, 4.138465, 0.000000, 1.914037, 0.00…
+$ HSPC_358 <dbl> 0.000000, 1.322769, 3.323190, 1.322769, 3.524842, 0.00…
+$ HSPC_359 <dbl> 0.0000000, 0.9071742, 1.8584210, 0.0000000, 8.1305324,…
+$ HSPC_360 <dbl> 0.000000, 0.000000, 2.544055, 0.000000, 9.351724, 0.00…
+$ HSPC_361 <dbl> 0.000000, 0.000000, 4.073667, 0.000000, 9.554073, 2.05…
+$ HSPC_362 <dbl> 0.000000, 0.000000, 7.448528, 0.000000, 4.768353, 0.00…
+$ HSPC_363 <dbl> 0.000000, 0.000000, 2.749745, 8.987973, 7.432788, 7.45…
+$ HSPC_365 <dbl> 0.000000, 0.000000, 4.965616, 2.594802, 9.358295, 8.09…
+$ HSPC_367 <dbl> 0.000000, 2.528168, 4.327223, 0.000000, 6.909409, 5.10…
+$ HSPC_368 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.951178, 0.00…
+$ HSPC_370 <dbl> 0.000000, 0.000000, 1.307598, 0.000000, 0.797134, 0.00…
+$ HSPC_371 <dbl> 0.000000, 1.257081, 1.257081, 0.000000, 1.257081, 1.25…
+$ HSPC_372 <dbl> 0.000000, 0.000000, 2.221252, 0.000000, 8.278550, 0.00…
+$ HSPC_373 <dbl> 0.000000, 0.000000, 4.730281, 2.884763, 3.783634, 6.93…
+$ HSPC_374 <dbl> 0.000000, 0.000000, 2.862633, 0.000000, 10.146380, 7.7…
+$ HSPC_376 <dbl> 0.000000, 0.000000, 3.171475, 8.276931, 2.537496, 0.00…
+$ HSPC_377 <dbl> 0.000000, 0.000000, 3.297929, 0.000000, 8.087149, 1.30…
+$ HSPC_380 <dbl> 1.219391, 0.000000, 2.934029, 1.219391, 3.165253, 1.21…
+$ HSPC_382 <dbl> 0.0000000, 0.0000000, 1.5486445, 0.0000000, 9.8626042,…
+$ HSPC_383 <dbl> 0.000000, 0.000000, 3.684053, 0.000000, 2.521370, 1.75…
+$ HSPC_386 <dbl> 0.000000, 1.412425, 2.934964, 3.837412, 2.112261, 4.60…
+$ HSPC_387 <dbl> 0.000000, 0.000000, 2.464120, 0.000000, 2.004298, 2.00…
+$ HSPC_388 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0965225,…
+$ HSPC_389 <dbl> 0.000000, 0.000000, 8.902540, 0.000000, 1.263307, 0.00…
+$ HSPC_390 <dbl> 0.000000, 1.531571, 3.386125, 7.733304, 3.625976, 0.00…
+$ HSPC_391 <dbl> 0.000000, 1.423235, 2.125560, 0.000000, 2.125560, 0.00…
+$ HSPC_392 <dbl> 1.166291, 0.000000, 2.242760, 0.000000, 0.000000, 1.16…
+$ HSPC_393 <dbl> 0.000000, 1.110847, 8.026679, 0.000000, 7.310363, 1.11…
+$ HSPC_395 <dbl> 0.000000, 0.000000, 1.923747, 0.000000, 1.923747, 1.26…
+$ HSPC_396 <dbl> 0.0000000, 0.9279096, 2.8713748, 0.0000000, 8.4108606,…
+$ HSPC_398 <dbl> 0.000000, 1.052478, 2.405000, 0.000000, 1.654517, 7.24…
+$ HSPC_399 <dbl> 0.000000, 0.000000, 1.198818, 0.000000, 9.267652, 0.00…
+$ HSPC_400 <dbl> 0.000000, 0.000000, 2.368347, 0.000000, 1.255700, 0.00…
+$ HSPC_402 <dbl> 0.000000, 0.000000, 2.004052, 0.000000, 8.950833, 8.75…
+$ HSPC_403 <dbl> 0.000000, 0.000000, 2.797093, 0.000000, 0.000000, 0.00…
+$ HSPC_404 <dbl> 0.0000000, 0.0000000, 3.7817437, 0.0000000, 4.2260011,…
+$ HSPC_405 <dbl> 0.000000, 1.099247, 1.715831, 0.000000, 1.099247, 5.31…
+$ HSPC_406 <dbl> 0.000000, 0.000000, 2.135083, 1.430987, 3.245124, 0.00…
+$ HSPC_407 <dbl> 0.0000000, 0.5650490, 1.5448568, 0.0000000, 3.8016573,…
+$ HSPC_408 <dbl> 0.0000000, 1.5700909, 10.4839578, 0.0000000, 8.0221142…
+$ HSPC_409 <dbl> 0.000000, 0.000000, 3.044399, 0.000000, 3.044399, 0.00…
+$ HSPC_410 <dbl> 0.904751, 0.000000, 1.854660, 0.000000, 5.165464, 0.00…
+$ HSPC_411 <dbl> 0.0000000, 0.0000000, 0.9059253, 0.0000000, 7.5067273,…
+$ HSPC_412 <dbl> 0.0000000, 0.0000000, 0.6614079, 8.5121379, 7.3252186,…
+$ HSPC_413 <dbl> 0.000000, 1.169683, 1.807043, 1.169683, 9.674198, 0.00…
+$ HSPC_415 <dbl> 0.000000, 0.000000, 5.273790, 0.000000, 3.093809, 0.00…
+$ HSPC_416 <dbl> 0.000000, 1.337731, 3.110643, 0.000000, 0.000000, 0.00…
+$ HSPC_417 <dbl> 0.0000000, 0.7221578, 1.5601978, 0.0000000, 1.2011916,…
+$ HSPC_418 <dbl> 0.000000, 0.000000, 2.348200, 0.000000, 1.241207, 0.00…
+$ HSPC_419 <dbl> 0.000000, 0.000000, 1.158302, 0.692403, 0.000000, 0.69…
+$ HSPC_420 <dbl> 0.0000000, 0.7442525, 1.5970870, 0.0000000, 1.5970870,…
+$ HSPC_421 <dbl> 0.000000, 0.000000, 3.682417, 0.000000, 1.985374, 0.00…
+$ HSPC_422 <dbl> 0.000000, 0.000000, 1.630076, 1.033967, 1.630076, 7.51…
+$ HSPC_423 <dbl> 0.0000000, 0.0000000, 1.4797402, 0.0000000, 1.1326799,…
+$ HSPC_424 <dbl> 0.000000, 1.267474, 2.728919, 0.000000, 3.439860, 0.00…
+$ HSPC_425 <dbl> 0.000000, 0.000000, 1.016015, 0.000000, 6.474436, 0.00…
+$ HSPC_426 <dbl> 0.000000, 0.000000, 3.133563, 0.000000, 0.000000, 1.55…
+$ HSPC_427 <dbl> 0.000000, 2.152076, 4.835791, 0.000000, 7.220338, 0.00…
+$ HSPC_431 <dbl> 0.000000, 2.150174, 3.639547, 1.101833, 5.618926, 2.97…
+$ HSPC_432 <dbl> 0.000000, 0.000000, 1.949682, 0.000000, 3.261333, 4.18…
+$ HSPC_435 <dbl> 0.000000, 1.097512, 2.970692, 0.000000, 7.647274, 6.35…
+$ HSPC_436 <dbl> 0.0000000, 0.0000000, 1.1114827, 0.0000000, 5.7023734,…
+$ HSPC_440 <dbl> 0.000000, 1.385247, 1.385247, 0.000000, 2.545103, 5.12…
+$ HSPC_441 <dbl> 0.000000, 0.000000, 1.696075, 1.084126, 8.589432, 1.08…
+$ HSPC_442 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.6713853,…
+$ HSPC_443 <dbl> 0.000000, 1.546895, 2.276125, 0.000000, 2.276125, 1.54…
+$ HSPC_444 <dbl> 1.374089, 0.000000, 0.000000, 0.000000, 1.374089, 0.00…
+$ HSPC_446 <dbl> 0.000000, 2.439029, 1.683195, 0.000000, 1.683195, 1.68…
+$ HSPC_447 <dbl> 0.000000, 1.113089, 1.113089, 0.000000, 2.166474, 7.12…
+$ HSPC_448 <dbl> 0.000000, 1.139444, 0.000000, 0.000000, 9.257634, 0.00…
+$ HSPC_449 <dbl> 0.000000, 1.344056, 2.489522, 0.000000, 5.057946, 2.02…
+$ HSPC_450 <dbl> 0.000000, 0.000000, 2.979455, 0.000000, 5.551901, 0.00…
+$ HSPC_451 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.322231, 1.75…
+$ HSPC_453 <dbl> 0.0000000, 0.0000000, 1.4937200, 0.0000000, 2.4699783,…
+$ HSPC_454 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 9.1805553,…
+$ HSPC_455 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.204120, 0.00…
+$ HSPC_456 <dbl> 0.0000000, 0.0000000, 1.4363752, 0.0000000, 7.5943576,…
+$ HSPC_457 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.535876, 7.75…
+$ HSPC_459 <dbl> 0.000000, 1.282562, 1.282562, 1.282562, 9.214660, 1.95…
+$ HSPC_460 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.601133, 0.00…
+$ HSPC_461 <dbl> 0.000000, 0.000000, 0.000000, 7.294303, 7.224373, 0.00…
+$ HSPC_462 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 5.732804, 0.00…
+$ HSPC_463 <dbl> 0.000000, 0.000000, 1.566104, 0.725680, 0.000000, 0.00…
+$ HSPC_465 <dbl> 0.000000, 4.053991, 0.000000, 3.894882, 2.159925, 0.00…
+$ HSPC_466 <dbl> 0.000000, 1.163197, 0.000000, 0.000000, 3.274518, 0.00…
+$ HSPC_467 <dbl> 0.000000, 1.176891, 1.176891, 1.176891, 8.628113, 2.25…
+$ HSPC_468 <dbl> 0.000000, 1.297938, 1.297938, 0.000000, 10.108305, 0.0…
+$ HSPC_470 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 3.23…
+$ HSPC_471 <dbl> 0.000000, 0.000000, 1.535633, 0.000000, 5.948904, 0.00…
+$ HSPC_472 <dbl> 0.0000000, 0.0000000, 0.0000000, 3.3032627, 0.9896601,…
+$ HSPC_473 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 6.756343, 6.88…
+$ HSPC_474 <dbl> 1.040938, 1.040938, 0.000000, 0.000000, 2.386883, 0.00…
+$ HSPC_475 <dbl> 7.447608, 0.000000, 3.318667, 0.000000, 3.318667, 1.99…
+$ HSPC_477 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.3981912,…
+$ HSPC_478 <dbl> 0.000000, 0.000000, 4.083810, 0.000000, 2.884169, 0.00…
+$ HSPC_479 <dbl> 0.000000, 0.000000, 3.027550, 0.000000, 8.194229, 1.94…
+$ HSPC_480 <dbl> 0.000000, 1.034469, 3.059791, 0.000000, 2.376690, 1.03…
+$ HSPC_482 <dbl> 6.620967, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
+$ HSPC_483 <dbl> 0.000000, 0.000000, 3.006419, 0.000000, 8.010120, 2.17…
+$ HSPC_485 <dbl> 0.0000000, 1.0279164, 0.6036333, 0.6036333, 0.6036333,…
+$ HSPC_486 <dbl> 0.000000, 0.000000, 1.410572, 0.000000, 1.410572, 5.75…
+$ HSPC_488 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.802316, 0.00…
+$ HSPC_489 <dbl> 0.000000, 1.882197, 0.000000, 0.000000, 0.000000, 0.00…
+$ HSPC_490 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.790270, 1.30…
+$ HSPC_491 <dbl> 0.000000, 1.469639, 0.000000, 4.081147, 8.733647, 0.00…
+$ HSPC_492 <dbl> 1.163132, 0.000000, 0.000000, 0.000000, 9.050494, 0.00…
+$ HSPC_493 <dbl> 0.000000, 0.000000, 2.223299, 0.000000, 3.586020, 4.73…
+$ HSPC_494 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.680132, 8.44…
+$ HSPC_495 <dbl> 0.000000, 2.419005, 0.000000, 0.000000, 5.543935, 2.41…
+$ HSPC_496 <dbl> 0.000000, 0.000000, 0.000000, 1.788287, 1.788287, 10.6…
+$ HSPC_497 <dbl> 0.000000, 0.000000, 1.945149, 0.000000, 1.945149, 1.94…
+$ HSPC_498 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.967779, 0.00…
+$ HSPC_499 <dbl> 0.000000, 0.000000, 1.614671, 0.000000, 8.977937, 0.00…
+$ HSPC_500 <dbl> 0.000000, 1.779610, 1.779610, 0.000000, 7.269594, 2.55…
+$ HSPC_501 <dbl> 0.000000, 2.304226, 0.000000, 0.000000, 2.304226, 0.00…
+$ HSPC_502 <dbl> 0.0000000, 0.0000000, 1.4698900, 0.0000000, 7.2171885,…
+$ HSPC_503 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.932863, 8.79…
+$ HSPC_504 <dbl> 0.000000, 0.000000, 2.201356, 0.000000, 9.317511, 2.20…
+$ HSPC_505 <dbl> 0.000000, 0.000000, 2.180019, 0.000000, 2.180019, 0.00…
+$ HSPC_506 <dbl> 0.000000, 0.000000, 1.878328, 0.000000, 2.667432, 0.00…
+$ HSPC_507 <dbl> 0.000000, 1.878372, 2.667483, 0.000000, 3.549225, 1.87…
+$ HSPC_508 <dbl> 0.0000000, 0.2296644, 0.2296644, 0.2296644, 4.6628473,…
+$ HSPC_509 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.942680, 7.82…
+$ HSPC_510 <dbl> 0.000000, 1.166340, 1.802743, 6.721521, 1.166340, 6.59…
+$ HSPC_512 <dbl> 0.000000, 0.000000, 1.738345, 0.000000, 0.000000, 1.11…
+$ HSPC_514 <dbl> 1.237955, 0.000000, 2.962215, 0.000000, 8.674516, 0.00…
+$ HSPC_515 <dbl> 1.108707, 1.108707, 1.108707, 0.000000, 1.108707, 8.57…
+$ HSPC_516 <dbl> 0.0000000, 0.0000000, 1.4299322, 0.0000000, 9.6466954,…
+$ HSPC_518 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.617584, 0.00…
+$ HSPC_520 <dbl> 0.000000, 0.000000, 1.644413, 0.000000, 8.591871, 0.00…
+$ HSPC_521 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.290520, 0.00…
+$ HSPC_522 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.182446, 0.00…
+$ HSPC_523 <dbl> 0.0000000, 0.0000000, 0.7295826, 9.9573376, 1.8610172,…
+$ HSPC_524 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.266931, 0.00…
+$ HSPC_526 <dbl> 1.301071, 1.301071, 0.000000, 0.000000, 1.301071, 0.00…
+$ HSPC_527 <dbl> 0.0000000, 0.0000000, 0.0000000, 1.2793196, 7.6901603,…
+$ HSPC_528 <dbl> 0.000000, 0.000000, 1.669689, 7.592221, 2.423018, 0.00…
+$ HSPC_530 <dbl> 1.746336, 0.000000, 0.000000, 0.000000, 8.986799, 1.74…
+$ HSPC_532 <dbl> 0.000000, 0.000000, 0.000000, 1.176792, 10.454235, 0.0…
+$ HSPC_533 <dbl> 0.0000000, 0.0000000, 0.7536668, 0.0000000, 1.9046925,…
+$ HSPC_534 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.227399, 0.00…
+$ HSPC_535 <dbl> 0.000000, 0.000000, 3.918443, 0.000000, 8.372526, 0.00…
+$ HSPC_537 <dbl> 0.000000, 0.000000, 1.665647, 1.665647, 6.776472, 2.91…
+$ HSPC_538 <dbl> 0.000000, 0.000000, 2.242435, 0.000000, 2.851982, 8.64…
+$ HSPC_539 <dbl> 0.000000, 3.354010, 1.725182, 7.588085, 7.535255, 0.00…
+$ HSPC_540 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.9846071,…
+$ HSPC_541 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 10.016742, 0.0…
+$ HSPC_543 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.753904, 7.96…
+$ HSPC_544 <dbl> 0.0000000, 0.0000000, 0.8221823, 0.0000000, 7.1295118,…
+$ HSPC_545 <dbl> 0.000000, 0.000000, 0.000000, 1.484735, 3.764537, 2.20…
+$ HSPC_546 <dbl> 0.000000, 2.211853, 3.047085, 0.000000, 0.000000, 0.00…
+$ HSPC_547 <dbl> 0.000000, 0.000000, 0.000000, 0.654750, 9.253559, 0.65…
+$ HSPC_548 <dbl> 0.000000, 0.000000, 2.165553, 1.455855, 1.455855, 0.00…
+$ HSPC_549 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.557210, 1.55…
+$ HSPC_550 <dbl> 1.750214, 0.000000, 1.750214, 0.000000, 3.680372, 0.00…
+$ HSPC_551 <dbl> 0.000000, 0.000000, 1.287115, 0.000000, 5.908242, 8.49…
+$ HSPC_552 <dbl> 0.000000, 0.000000, 1.226425, 0.000000, 4.650625, 8.64…
+$ HSPC_553 <dbl> 0.0000000, 0.4708726, 0.4708726, 0.0000000, 7.2724626,…
+$ HSPC_554 <dbl> 1.449277, 1.449277, 1.449277, 0.000000, 1.449277, 9.03…
+$ HSPC_555 <dbl> 0.000000, 0.000000, 1.802172, 0.000000, 0.000000, 0.00…
+$ HSPC_556 <dbl> 0.0000000, 0.0000000, 0.9369457, 0.0000000, 0.9369457,…
+$ HSPC_557 <dbl> 1.141542, 0.000000, 1.141542, 0.000000, 1.770759, 0.00…
+$ HSPC_559 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.411402, 0.00…
+$ HSPC_560 <dbl> 0.000000, 2.929589, 1.680915, 0.000000, 6.116774, 6.74…
+$ HSPC_562 <dbl> 0.000000, 0.000000, 2.717569, 0.000000, 0.000000, 8.61…
+$ HSPC_563 <dbl> 0.000000, 1.199800, 1.845653, 0.000000, 1.845653, 0.00…
+$ HSPC_566 <dbl> 0.000000, 0.000000, 2.959197, 0.000000, 2.959197, 2.13…
+$ HSPC_567 <dbl> 0.000000, 0.000000, 1.812523, 0.000000, 1.812523, 6.68…
+$ HSPC_568 <dbl> 0.000000, 0.000000, 2.956854, 0.000000, 2.131637, 2.95…
+$ HSPC_569 <dbl> 0.0000000, 1.2707229, 0.7709662, 0.0000000, 1.2707229,…
+$ HSPC_571 <dbl> 0.000000, 1.041987, 1.041987, 0.000000, 1.041987, 1.64…
+$ HSPC_573 <dbl> 0.000000, 0.000000, 1.461676, 0.000000, 3.288566, 0.00…
+$ HSPC_574 <dbl> 0.0000000, 0.7547277, 1.9066029, 0.0000000, 5.9897178,…
+$ HSPC_575 <dbl> 0.000000, 1.183227, 2.604870, 4.577438, 2.878570, 1.18…
+$ HSPC_576 <dbl> 0.000000, 0.000000, 2.741129, 0.000000, 10.233110, 6.7…
+$ HSPC_577 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.834036, 1.60…
+$ HSPC_578 <dbl> 0.000000, 0.000000, 1.404000, 7.371858, 2.101881, 1.40…
+$ HSPC_579 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.491585, 1.82…
+$ HSPC_580 <dbl> 0.000000, 0.000000, 1.510090, 0.000000, 1.510090, 0.00…
+$ HSPC_582 <dbl> 4.342803, 9.749104, 0.000000, 0.000000, 5.826065, 1.65…
+$ HSPC_584 <dbl> 0.000000, 0.000000, 2.565813, 0.000000, 3.733595, 7.76…
+$ HSPC_585 <dbl> 0.000000, 0.000000, 1.449121, 0.000000, 1.449121, 5.35…
+$ HSPC_586 <dbl> 0.0000000, 0.8914714, 1.4383643, 0.0000000, 1.8339836,…
+$ HSPC_589 <dbl> 0.000000, 1.963326, 2.765365, 0.000000, 7.352066, 0.00…
+$ HSPC_590 <dbl> 0.000000, 0.000000, 1.835966, 1.192227, 1.192227, 6.95…
+$ HSPC_592 <dbl> 0.000000, 0.000000, 0.000000, 5.165700, 1.227806, 0.00…
+$ HSPC_593 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.554248, 0.00…
+$ HSPC_594 <dbl> 1.950098, 0.000000, 1.950098, 0.000000, 4.395615, 5.71…
+$ HSPC_595 <dbl> 1.756544, 0.000000, 1.756544, 0.000000, 2.796431, 3.54…
+$ HSPC_596 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.086691, 7.95…
+$ HSPC_597 <dbl> 0.000000, 0.000000, 1.563392, 0.000000, 0.000000, 2.29…
+$ HSPC_598 <dbl> 0.000000, 0.000000, 1.220673, 1.220673, 0.000000, 1.22…
+$ HSPC_599 <dbl> 0.000000, 1.349651, 2.034571, 0.000000, 1.349651, 0.00…
+$ HSPC_600 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.377207, 0.00…
+$ HSPC_601 <dbl> 0.000000, 0.000000, 2.865429, 0.000000, 2.376203, 1.63…
+$ HSPC_602 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.806407, 2.41…
+$ HSPC_603 <dbl> 0.000000, 2.004660, 1.589106, 0.000000, 9.490018, 0.00…
+$ HSPC_604 <dbl> 1.748474, 0.000000, 2.516030, 0.000000, 1.748474, 0.00…
+$ HSPC_606 <dbl> 0.000000, 0.000000, 1.075087, 0.000000, 2.111214, 0.00…
+$ HSPC_607 <dbl> 0.000000, 0.000000, 3.717957, 0.000000, 9.191362, 0.00…
+$ HSPC_608 <dbl> 0.000000, 1.574605, 1.574605, 0.000000, 3.156098, 1.57…
+$ HSPC_610 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…
+$ HSPC_612 <dbl> 0.000000, 1.356545, 2.043143, 0.000000, 6.275658, 0.00…
+$ HSPC_613 <dbl> 0.000000, 0.000000, 1.438703, 6.081511, 2.144549, 6.95…
+$ HSPC_614 <dbl> 0.000000, 1.315161, 3.077500, 0.000000, 4.543775, 1.31…
+$ HSPC_615 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.450652, 0.00…
+$ HSPC_617 <dbl> 0.0000000, 1.3847809, 2.3300729, 0.0000000, 0.8525275,…
+$ HSPC_618 <dbl> 0.000000, 0.000000, 3.542103, 0.000000, 2.885428, 2.39…
+$ HSPC_620 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.517983, 0.00…
+$ HSPC_623 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.705469, 1.70…
+$ HSPC_624 <dbl> 0.0000000, 0.7200995, 0.0000000, 0.0000000, 8.9490659,…
+$ HSPC_625 <dbl> 0.000000, 0.000000, 0.000000, 3.458561, 8.847668, 2.93…
+$ HSPC_626 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.424363, 0.00…
+$ HSPC_627 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.000538, 6.99…
+$ HSPC_628 <dbl> 0.000000, 1.638219, 0.000000, 0.000000, 1.638219, 0.00…
+$ HSPC_629 <dbl> 1.196665, 0.000000, 0.000000, 0.000000, 1.841645, 0.00…
+$ HSPC_630 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.098177, 2.09…
+$ HSPC_631 <dbl> 0.000000, 1.893172, 1.237108, 0.000000, 9.108643, 1.23…
+$ HSPC_633 <dbl> 0.000000, 0.000000, 2.731996, 0.000000, 2.731996, 0.00…
+$ HSPC_634 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.501517, 0.00…
+$ HSPC_635 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.892953, 0.00…
+$ HSPC_636 <dbl> 0.000000, 1.087599, 1.700617, 1.087599, 6.627788, 0.00…
+$ HSPC_637 <dbl> 0.0000000, 0.9389313, 2.4816963, 0.0000000, 7.3021856,…
+$ HSPC_638 <dbl> 0.0000000, 0.0000000, 1.1842890, 0.0000000, 2.5023010,…
+$ HSPC_639 <dbl> 0.996580, 1.994867, 2.579258, 0.000000, 2.316452, 0.99…
+$ HSPC_640 <dbl> 0.000000, 1.025432, 2.362406, 0.000000, 2.627103, 1.02…
+$ HSPC_641 <dbl> 0.000000, 0.000000, 2.000512, 0.000000, 2.000512, 1.00…
+$ HSPC_643 <dbl> 0.000000, 2.465561, 1.705628, 0.000000, 9.092947, 1.70…
+$ HSPC_644 <dbl> 0.0000000, 0.4904320, 0.8557752, 0.0000000, 6.1750519,…
+$ HSPC_645 <dbl> 0.0000000, 0.0000000, 0.7156666, 0.0000000, 2.0742346,…
+$ HSPC_646 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.9959057, 9.3159931,…
+$ HSPC_648 <dbl> 0.000000, 2.242718, 0.000000, 0.000000, 3.081640, 1.51…
+$ HSPC_649 <dbl> 0.0000000, 0.7139349, 0.7139349, 0.0000000, 0.7139349,…
+$ HSPC_651 <dbl> 0.000000, 2.253619, 7.476806, 0.000000, 6.023902, 2.73…
+$ HSPC_652 <dbl> 1.352094, 0.000000, 2.500412, 0.000000, 2.037610, 0.00…
+$ HSPC_654 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.886385, 7.53…
+$ HSPC_656 <dbl> 0.000000, 1.355844, 1.355844, 0.000000, 0.000000, 0.00…
+$ HSPC_657 <dbl> 0.000000, 2.238724, 3.077173, 0.000000, 6.951322, 3.07…
+$ HSPC_658 <dbl> 0.000000, 0.000000, 2.796922, 3.846645, 8.942041, 0.00…
+$ HSPC_660 <dbl> 0.000000, 1.253314, 1.253314, 0.000000, 7.108572, 1.25…
+$ HSPC_661 <dbl> 0.000000, 1.491329, 3.043680, 1.491329, 9.643608, 0.00…
+$ HSPC_662 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.688146, 4.39…
+$ HSPC_663 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 6.57…
+$ HSPC_664 <dbl> 0.0000000, 1.5053124, 0.9406784, 0.0000000, 1.5053124,…
+$ HSPC_665 <dbl> 0.000000, 1.290390, 1.290390, 0.000000, 6.107672, 1.29…
+$ HSPC_666 <dbl> 0.000000, 1.130141, 0.000000, 7.631839, 8.362422, 6.31…
+$ HSPC_667 <dbl> 0.000000, 0.000000, 1.776997, 0.000000, 6.465588, 1.77…
+$ HSPC_668 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.302290, 0.00…
+$ HSPC_669 <dbl> 0.000000, 0.000000, 2.425594, 0.000000, 1.671860, 0.00…
+$ HSPC_670 <dbl> 0.000000, 2.337848, 3.718794, 0.000000, 0.000000, 2.33…
+$ HSPC_671 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.158776, 0.00…
+$ HSPC_672 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.038159, 0.00…
+$ HSPC_673 <dbl> 0.000000, 1.519487, 1.519487, 0.000000, 8.786551, 5.68…
+$ HSPC_674 <dbl> 0.000000, 1.235988, 0.000000, 0.000000, 1.891750, 1.89…
+$ HSPC_676 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.975163, 1.74…
+$ HSPC_678 <dbl> 0.000000, 2.249420, 1.524795, 0.000000, 3.089130, 0.00…
+$ HSPC_679 <dbl> 2.256522, 2.256522, 0.000000, 1.530665, 2.737162, 1.53…
+$ HSPC_680 <dbl> 0.000000, 2.323337, 0.000000, 5.359270, 8.465280, 0.00…
+$ HSPC_681 <dbl> 0.0000000, 1.6627807, 0.0000000, 0.0000000, 1.9591579,…
+$ HSPC_682 <dbl> 0.000000, 1.043463, 0.000000, 7.477477, 2.064713, 1.04…
+$ HSPC_683 <dbl> 0.000000, 0.000000, 2.261955, 0.000000, 3.631015, 0.00…
+$ HSPC_687 <dbl> 0.000000, 0.000000, 3.811941, 9.810273, 3.430744, 2.09…
+$ HSPC_689 <dbl> 0.000000, 0.000000, 0.000000, 2.085635, 10.065718, 2.0…
+$ HSPC_690 <dbl> 2.196289, 2.196289, 3.029625, 0.000000, 6.808290, 7.65…
+$ HSPC_692 <dbl> 2.555176, 0.000000, 4.919547, 0.000000, 5.637318, 2.55…
+$ HSPC_695 <dbl> 0.0000000, 0.2681148, 1.3971816, 0.2681148, 10.7977224…
+$ HSPC_696 <dbl> 0.000000, 2.265383, 4.020705, 0.000000, 8.976227, 0.00…
+$ HSPC_697 <dbl> 0.000000, 1.271303, 3.012385, 0.000000, 0.000000, 7.59…
+$ HSPC_698 <dbl> 0.000000, 0.000000, 4.694712, 0.000000, 0.000000, 0.00…
+$ HSPC_699 <dbl> 0.000000, 0.000000, 2.930008, 0.000000, 0.000000, 0.00…
+$ HSPC_700 <dbl> 0.000000, 0.000000, 1.083768, 0.000000, 1.695606, 0.00…
+$ HSPC_701 <dbl> 0.000000, 1.156584, 3.066217, 1.156584, 5.095538, 1.79…
+$ HSPC_702 <dbl> 0.000000, 0.000000, 3.185800, 0.000000, 2.336223, 2.33…
+$ HSPC_703 <dbl> 0.000000, 3.026458, 2.193467, 4.488271, 7.306130, 6.31…
+$ HSPC_704 <dbl> 0.000000, 1.708092, 1.708092, 0.000000, 1.708092, 8.37…
+$ HSPC_705 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.8292660,…
+$ HSPC_706 <dbl> 0.000000, 1.272727, 0.000000, 1.272727, 6.221902, 7.03…
+$ HSPC_707 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.917498, 1.36…
+$ HSPC_708 <dbl> 0.000000, 0.000000, 0.000000, 4.944545, 9.429525, 9.22…
+$ HSPC_709 <dbl> 0.000000, 0.000000, 6.392596, 0.000000, 5.005898, 0.00…
+$ HSPC_714 <dbl> 0.000000, 0.000000, 3.784587, 0.000000, 2.395136, 1.64…
+$ HSPC_716 <dbl> 0.000000, 0.000000, 0.000000, 1.311350, 8.040385, 1.31…
+$ HSPC_717 <dbl> 0.000000, 1.153655, 1.153655, 0.000000, 2.224752, 0.00…
+$ HSPC_719 <dbl> 0.000000, 0.000000, 3.146515, 6.492252, 4.618557, 0.00…
+$ HSPC_720 <dbl> 0.0000000, 0.8205612, 1.3403833, 0.0000000, 1.7217133,…
+$ HSPC_721 <dbl> 0.000000, 2.935568, 3.456262, 0.000000, 0.000000, 2.11…
+$ HSPC_722 <dbl> 0.000000, 0.000000, 2.268643, 0.000000, 9.095591, 1.18…
+$ HSPC_723 <dbl> 0.8420975, 0.8420975, 2.5250483, 0.0000000, 4.8843811,…
+$ HSPC_724 <dbl> 0.0000000, 1.0942930, 1.7093650, 0.0000000, 3.2499204,…
+$ HSPC_725 <dbl> 1.577147, 0.000000, 3.159495, 0.000000, 2.312545, 2.79…
+$ HSPC_727 <dbl> 0.000000, 1.576167, 3.894561, 0.000000, 8.810183, 0.00…
+$ HSPC_729 <dbl> 0.0000000, 0.0000000, 1.9363149, 0.0000000, 7.5873035,…
+$ HSPC_730 <dbl> 1.134736, 1.761952, 2.197661, 0.000000, 7.311461, 0.00…
+$ HSPC_731 <dbl> 0.000000, 1.129768, 1.129768, 4.039758, 1.755516, 5.09…
+$ HSPC_732 <dbl> 0.0000000, 0.6937047, 2.2340688, 0.6937047, 2.0310980,…
+$ HSPC_733 <dbl> 0.000000, 0.000000, 1.435585, 0.000000, 4.173862, 2.14…
+$ HSPC_734 <dbl> 0.000000, 1.217180, 1.578880, 0.000000, 9.362294, 1.21…
+$ HSPC_735 <dbl> 0.000000, 0.000000, 2.048558, 0.000000, 9.326379, 2.04…
+$ HSPC_736 <dbl> 0.0000000, 0.0000000, 0.6789402, 0.0000000, 2.2034453,…
+$ HSPC_737 <dbl> 0.0000000, 0.0000000, 0.7110779, 0.0000000, 7.7355892,…
+$ HSPC_738 <dbl> 0.000000, 2.715943, 0.000000, 0.000000, 3.362289, 8.20…
+$ HSPC_740 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.620463, 6.54…
+$ HSPC_742 <dbl> 0.0000000, 0.8436636, 0.8436636, 0.0000000, 1.7586463,…
+$ HSPC_743 <dbl> 1.122102, 0.000000, 0.000000, 0.000000, 0.000000, 6.91…
+$ HSPC_744 <dbl> 0.000000, 7.472712, 1.212953, 0.000000, 1.212953, 0.00…
+$ HSPC_745 <dbl> 0.000000, 0.000000, 0.000000, 2.402561, 2.402561, 0.00…
+$ HSPC_746 <dbl> 0.000000, 2.183740, 0.000000, 0.000000, 6.837761, 0.00…
+$ HSPC_747 <dbl> 0.000000, 0.000000, 1.777355, 0.000000, 7.778192, 2.54…
+$ HSPC_748 <dbl> 0.000000, 2.263816, 0.000000, 0.000000, 3.303450, 0.00…
+$ HSPC_749 <dbl> 0.000000, 0.000000, 1.443774, 0.000000, 3.501043, 0.00…
+$ HSPC_750 <dbl> 0.000000, 1.029692, 0.000000, 0.000000, 8.102961, 0.00…
+$ HSPC_751 <dbl> 0.000000, 0.000000, 3.435201, 0.000000, 3.435201, 7.28…
+$ HSPC_752 <dbl> 2.227867, 0.000000, 2.227867, 0.000000, 3.065024, 9.23…
+$ HSPC_753 <dbl> 0.000000, 0.000000, 2.092163, 0.000000, 2.420031, 3.10…
+$ HSPC_755 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 5.3881500,…
+$ HSPC_756 <dbl> 0.000000, 1.730911, 0.000000, 0.000000, 6.980096, 6.68…
+$ HSPC_757 <dbl> 0.000000, 1.394821, 1.394821, 0.000000, 3.193425, 0.00…
+$ HSPC_758 <dbl> 0.0000000, 0.8647569, 1.4016663, 0.0000000, 7.1436038,…
+$ HSPC_759 <dbl> 1.5063802, 0.9414682, 1.9112870, 0.9414682, 2.2271023,…
+$ HSPC_760 <dbl> 0.000000, 9.073516, 1.653873, 0.000000, 1.051989, 0.00…
+$ HSPC_761 <dbl> 0.0000000, 2.0270678, 1.5086587, 0.0000000, 2.4076406,…
+$ HSPC_762 <dbl> 0.000000, 1.935807, 1.935807, 0.000000, 7.772123, 0.00…
+$ HSPC_764 <dbl> 0.000000, 1.784134, 1.784134, 0.000000, 3.969445, 1.78…
+$ HSPC_765 <dbl> 0.000000, 2.116018, 2.116018, 0.000000, 11.306195, 2.1…
+$ HSPC_766 <dbl> 0.0000000, 0.9827582, 0.9827582, 0.0000000, 3.5531934,…
+$ HSPC_767 <dbl> 0.6646406, 0.0000000, 1.4623246, 0.0000000, 10.4507620…
+$ HSPC_768 <dbl> 0.000000, 1.703127, 1.703127, 1.703127, 9.992206, 2.46…
+$ HSPC_769 <dbl> 0.000000, 2.429943, 4.213728, 0.000000, 4.772537, 2.42…
+$ HSPC_770 <dbl> 0.000000, 1.760248, 1.760248, 1.760248, 6.134208, 2.52…
+$ HSPC_771 <dbl> 0.000000, 0.000000, 1.818104, 0.000000, 8.774881, 1.81…
+$ HSPC_772 <dbl> 0.000000, 1.600797, 0.000000, 0.000000, 2.340909, 0.00…
+$ HSPC_773 <dbl> 0.0000000, 0.9900713, 1.5717090, 0.0000000, 5.2659904,…
+$ HSPC_774 <dbl> 0.0000000, 0.0000000, 1.5496423, 0.0000000, 2.5406754,…
+$ HSPC_776 <dbl> 0.000000, 0.000000, 3.691348, 2.314052, 8.369556, 0.00…
+$ HSPC_777 <dbl> 0.000000, 0.000000, 2.079090, 0.000000, 8.370977, 1.05…
+$ HSPC_778 <dbl> 0.000000, 0.000000, 3.691686, 1.434589, 4.532296, 0.00…
+$ HSPC_780 <dbl> 0.000000, 1.178249, 1.818049, 0.000000, 7.874420, 0.00…
+$ HSPC_781 <dbl> 0.000000, 2.884261, 2.884261, 0.000000, 3.783095, 2.88…
+$ HSPC_782 <dbl> 0.000000, 0.000000, 2.691145, 0.000000, 7.913867, 0.00…
+$ HSPC_783 <dbl> 0.000000, 0.000000, 1.775679, 0.000000, 8.938539, 9.20…
+$ HSPC_784 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.668054, 1.05…
+$ HSPC_785 <dbl> 0.000000, 0.000000, 2.465903, 0.000000, 3.530782, 1.32…
+$ HSPC_786 <dbl> 0.000000, 1.148465, 1.779704, 0.000000, 2.552651, 8.42…
+$ HSPC_787 <dbl> 0.000000, 0.000000, 3.897091, 2.161763, 3.513976, 4.84…
+$ HSPC_788 <dbl> 0.000000, 0.000000, 3.844884, 0.000000, 1.877270, 6.68…
+$ HSPC_789 <dbl> 0.000000, 0.000000, 1.822053, 0.000000, 2.264164, 1.18…
+$ HSPC_790 <dbl> 0.000000, 0.000000, 2.851872, 0.000000, 9.272098, 0.00…
+$ HSPC_791 <dbl> 1.789964, 0.000000, 4.930584, 0.000000, 6.443477, 2.56…
+$ HSPC_794 <dbl> 0.000000, 1.112719, 0.000000, 1.112719, 2.498279, 3.78…
+$ HSPC_795 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.2924736,…
+$ HSPC_796 <dbl> 0.0000000, 0.7001062, 1.5229964, 1.1694432, 6.1361361,…
+$ HSPC_797 <dbl> 0.000000, 0.872225, 1.411951, 0.872225, 3.217981, 0.87…
+$ HSPC_798 <dbl> 0.000000, 1.531160, 0.000000, 2.257120, 4.314828, 0.00…
+$ HSPC_799 <dbl> 0.000000, 0.000000, 4.059222, 2.637723, 3.516894, 0.00…
+$ HSPC_800 <dbl> 0.000000, 0.000000, 1.766326, 0.000000, 5.615808, 3.03…
+$ HSPC_801 <dbl> 0.000000, 1.335456, 0.000000, 0.000000, 1.335456, 0.00…
+$ HSPC_802 <dbl> 0.000000, 0.000000, 0.000000, 2.881726, 4.081151, 0.00…
+$ HSPC_803 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.326296, 0.00…
+$ HSPC_804 <dbl> 0.000000, 0.000000, 1.871913, 0.000000, 2.660005, 6.23…
+$ HSPC_806 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.774769, 0.00…
+$ HSPC_807 <dbl> 0.0000000, 1.2411117, 0.0000000, 0.0000000, 2.3480676,…
+$ HSPC_808 <dbl> 0.0000000, 0.0000000, 1.1139506, 2.5001649, 1.1139506,…
+$ HSPC_809 <dbl> 1.788499, 0.000000, 0.000000, 1.788499, 3.064135, 1.78…
+$ HSPC_810 <dbl> 0.000000, 0.000000, 1.651345, 0.000000, 3.549216, 0.00…
+$ HSPC_812 <dbl> 0.0000000, 0.0000000, 2.0668334, 0.0000000, 8.1782380,…
+$ HSPC_813 <dbl> 0.0000000, 0.0000000, 0.6277581, 4.3663804, 1.8975541,…
+$ HSPC_814 <dbl> 0.000000, 1.109763, 2.161664, 0.000000, 0.000000, 0.00…
+$ HSPC_815 <dbl> 0.9630613, 0.9630613, 2.7434401, 0.9630613, 7.9310753,…
+$ HSPC_816 <dbl> 0.000000, 0.000000, 1.346034, 1.346034, 6.498939, 1.34…
+$ HSPC_818 <dbl> 0.0000000, 0.0000000, 3.4534483, 0.9967122, 9.8653293,…
+$ HSPC_819 <dbl> 0.000000, 0.000000, 3.149725, 1.364557, 7.496230, 2.05…
+$ HSPC_820 <dbl> 0.0000000, 0.8098885, 2.4642646, 0.0000000, 9.0793660,…
+$ HSPC_821 <dbl> 0.000000, 0.000000, 3.792154, 0.000000, 3.175252, 0.00…
+$ HSPC_822 <dbl> 0.000000, 1.386534, 3.957031, 0.000000, 2.080316, 0.00…
+$ HSPC_824 <dbl> 0.000000, 1.236780, 1.236780, 2.342030, 2.342030, 1.23…
+$ HSPC_825 <dbl> 0.000000, 0.000000, 2.079118, 0.000000, 5.922947, 2.07…
+$ HSPC_826 <dbl> 0.000000, 2.002994, 2.002994, 0.000000, 4.811203, 0.00…
+$ HSPC_827 <dbl> 1.746375, 0.000000, 3.381363, 0.000000, 7.353191, 1.74…
+$ HSPC_828 <dbl> 0.000000, 0.000000, 1.588492, 0.000000, 2.326162, 0.00…
+$ HSPC_831 <dbl> 0.000000, 1.304384, 0.000000, 0.000000, 6.405611, 6.06…
+$ HSPC_832 <dbl> 0.000000, 0.000000, 2.376979, 0.000000, 9.076790, 1.03…
+$ HSPC_833 <dbl> 0.0000000, 0.0000000, 1.8371808, 0.0000000, 0.0000000,…
+$ HSPC_834 <dbl> 0.0000000, 0.9245023, 1.8852183, 0.0000000, 8.9614736,…
+$ HSPC_835 <dbl> 0.000000, 0.000000, 2.595404, 0.000000, 6.141626, 6.76…
+$ HSPC_836 <dbl> 0.000000, 0.000000, 3.994019, 0.000000, 4.386241, 0.00…
+$ HSPC_837 <dbl> 0.000000, 0.000000, 2.792826, 0.000000, 0.000000, 0.00…
+$ HSPC_838 <dbl> 0.0000000, 0.0000000, 3.2926469, 0.0000000, 0.9838061,…
+$ HSPC_839 <dbl> 0.000000, 0.000000, 2.452494, 0.000000, 2.720721, 1.69…
+$ HSPC_840 <dbl> 0.000000, 0.000000, 3.534585, 0.000000, 1.866730, 5.03…
+$ HSPC_841 <dbl> 0.000000, 0.000000, 1.438567, 0.000000, 1.438567, 1.43…
+$ HSPC_842 <dbl> 0.000000, 2.545827, 2.545827, 0.000000, 7.622029, 2.54…
+$ HSPC_843 <dbl> 1.256817, 0.000000, 1.256817, 0.000000, 1.256817, 0.00…
+$ HSPC_844 <dbl> 0.000000, 0.000000, 1.584047, 0.000000, 7.433263, 0.00…
+$ HSPC_845 <dbl> 0.000000, 1.227393, 1.880830, 7.669286, 6.989762, 0.00…
+$ HSPC_846 <dbl> 0.000000, 1.401354, 1.401354, 0.000000, 2.919525, 7.30…
+$ HSPC_848 <dbl> 0.000000, 0.000000, 1.391594, 0.000000, 2.553619, 7.94…
+$ HSPC_849 <dbl> 0.000000, 0.000000, 1.601976, 0.000000, 8.387312, 2.82…
+$ HSPC_851 <dbl> 0.000000, 0.000000, 3.910752, 0.000000, 6.824279, 7.81…
+$ HSPC_852 <dbl> 0.000000, 1.658355, 2.409562, 1.658355, 1.658355, 0.00…
+
+
+
+
+
+#---CODING ANSWER---
+#| include: false
+glimpse(prog_hspc_results)
+
+Rows: 280
+Columns: 6
+$ Top <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
+$ p.value <dbl> 7.038138e-117, 4.736622e-90, 1.832630e-88, 4.211954e-7…
+$ FDR <dbl> 1.970679e-114, 6.631271e-88, 1.710455e-86, 2.948368e-7…
+$ summary.logFC <dbl> 1.596910, 3.035165, 3.261056, -2.146491, -3.056730, 3.…
+$ logFC.hspc <dbl> 1.596910, 3.035165, 3.261056, -2.146491, -3.056730, 3.…
+$ ensembl_gene_id <chr> "ENSMUSG00000028639", "ENSMUSG00000024053", "ENSMUSG00…
+
+
+
+
+
+
+It is useful to have this information in a single dataframe to which we will add the gene information from Ensembl Having all the information together will make it easier to interpret the results and select genes of interest.
+🎬 Merge the two dataframes:
-# merge stats results with normalise values
+# merge stats results with normalise values
prog_hspc_results <- prog_hspc_results |>
left_join(prog_hspc, by = "ensembl_gene_id")
-Add gene information from the NCBI using biomaRt
-add the gene information using biomart
-Ensembl (Birney et al. 2004) is a bioinformatics project to organise all the biological information around the sequences of large genomes. The are a large number of databases but BioMart (smedley2009?) provides a consistent interface to the material. There are web-based tools to use these but the R package biomaRtr (Durinck et al. 2009) enables you to rapidly access and integrate information into your R data structures.
+This means you have the counts for each sample along with the statistical results for each gene.
+Add gene information from Ensembl using biomaRt
+Ensembl (Martin et al. 2023; Birney et al. 2004)is a bioinformatics project to organise all the biological information around the sequences of large genomes. The are a large number of databases but BioMart (Smedley et al. 2009) provides a consistent interface to the material. There are web-based tools to use these but the R package biomaRt (Durinck et al. 2009) gives you programmatic access making it easier to integrate information into R dataframes
+🎬 Load the biomaRt (Durinck et al. 2009) package:
+🎬 Connect to the mouse database and see what information we can retrieve:
-# Connect to the mouse database
-
+# Connect to the mouse database
ensembl <- useMart(biomart = "ensembl",
- dataset = "mmusculus_gene_ensembl")
-
-See what info we can retrieve:
-
-listAttributes(mart = ensembl) |> View()
+ dataset = "mmusculus_gene_ensembl")
+
+# See what information we can retrieve
+listAttributes(mart = ensembl) |> View()
Error in .External2(C_dataviewer, x, title): unable to start data viewer
-We need ensembl_gene_id: because we will need to merge the info external_gene_name: name for gene description: description
+This may take a moment
+We use the getBM() function to retrieve information from the database. The filters argument is used to specified what kind of identifier we are supplying to retrieve information. The attributes argument is used to select the information we want to retrieve. The values argument is used to specify the identifers. The mart argument is used to specify the connection we created.
+🎬 Get the gene information:
-gene_info <- getBM(filters = "ensembl_gene_id",
+
-Notice the dataframe returned only has 279 rows, not 280. Which one is missing?
+We are getting the gene name and and a description. We also need to get the id because we will use that to merge the gene_info dataframe with the prog_hspc_results dataframe. Notice the dataframe returned only has 279 rows - one of the ids does not have information.
+🎬 We can find which is missing with:
-prog_hspc_results |> select(ensembl_gene_id) |>
+prog_hspc_results |> select(ensembl_gene_id) |>
filter(!ensembl_gene_id %in% gene_info$ensembl_gene_id)
Error:
@@ -677,280 +2373,148 @@ Workshop
• `conflicts_prefer(plotly::select)`
+Oh, conflicted has flagged a conflict for us.
+🎬 Take the appropriate action to resolve the conflict:
+❓ What is the id which is missing information?
+
+
+We might want to look that up - but let’s worry about it later if it turns out to be something important.
+🎬 Merge the gene information with the results:
-prog_hspc_results |> dplyr::select(ensembl_gene_id) |>
- filter(!ensembl_gene_id %in% gene_info$ensembl_gene_id)
-
-# A tibble: 1 × 1
- ensembl_gene_id
- <chr>
-1 ENSMUSG00000029386
-
-
-We might want to look that up. Google it. Let’s worry about it later if it turns out to be something important.
-merge the gene info with the results
-
-prog_hspc_results <- prog_hspc_results |>
+prog_hspc_results <- prog_hspc_results |>
left_join(gene_info, by = "ensembl_gene_id")
-We now have datatframe with all the info we need, normalised counts, log2 normalised counts, statistical comparisons with fold changes and p values, information about the gene other than just the id
+I recommend viewing the dataframe to see the new columns. We now have dataframe with all the info we need, normalised counts, log2 normalised counts, statistical comparisons with fold changes and p values, information about the gene other than just the id
Write the significant genes to file
-save the most sig genes to file
+We will create dateframe of the signifcant genes and write them to file. These are the files you want to examine in more detail along with the visualisations to select your genes of interest.
+🎬 Create a dataframe of the genes significant at the 0.01 level:
-prog_hspc_results_sig0.01 <- prog_hspc_results |>
+prog_hspc_results_sig0.01 <- prog_hspc_results |>
filter(FDR <= 0.01)
-168 genes
-write to csv file
+🎬 Write the dataframe to file
-write_csv(prog_hspc_results_sig0.01,
+#---CODING ANSWER---
+#| include: false
+write_csv(prog_hspc_results_sig0.01,
file = "results/prog_hspc_results_sig0.01.csv")
+🎬 Create a dataframe of the genes significant at the 0.05 level and write to file:
-prog_hspc_results_sig0.05 <- prog_hspc_results |>
- filter(FDR <= 0.05)
-
-182 genes
-write to csv file
-
-write_csv(prog_hspc_results_sig0.05,
+
+❓How many genes are significant at the 0.01 and 0.05 levels?
+
+
+
View the relationship between cells using PCA
-we do this on the log2 transformed normalised counts or the regularized the log transformed counts
-transpose the data we are reducing he number of dimensions from 280
+We have 280 genes in our dataset. PCA will allow us to plot our cells in the “gene expression” space so we can see if Prog cells cluster together and HSPC cells cluster together as we would expect. We do this on the log2 transformed normalised counts.
+Our data have genes in rows and samples in columns which is a common organisation for gene expression data. However, PCA expects cells in rows and genes, the variables, in columns. We can transpose the data to get it in the correct format.
+🎬 Transpose the log2 transformed normalised counts:
-prog_hspc_trans <- prog_hspc_results |>
+prog_hspc_trans <- prog_hspc_results |>
dplyr::select(starts_with(c("Prog_", "HSPC_"))) |>
t() |>
data.frame()
+We have used the select() function to select all the columns that start with Prog_ or HSPC_. We then use the t() function to transpose the dataframe. We then convert the resulting matrix to a dataframe using data.frame(). If you view that dataframe you’ll see it has default column name which we can fix using colnames() to set the column names to the gene ids.
+🎬 Set the column names to the gene ids:
-colnames(prog_hspc_trans) <- prog_hspc_results$ensembl_gene_id
+colnames(prog_hspc_trans) <- prog_hspc_results$ensembl_gene_id
perform PCA using standard functions
+The rank. argument tells prcomp() to only calculate the first 15 principal components. This is useful for visualisation as we can only plot in 2 or 3 dimensions. We can see the results of the PCA by viewing the summary() of the pca object.
-summary(pca)
+summary(pca)
-Importance of components:
- PC1 PC2 PC3 PC4 PC5 PC6 PC7
-Standard deviation 4.3892 3.08797 2.25263 2.13943 1.96659 1.76697 1.62753
-Proportion of Variance 0.0688 0.03406 0.01812 0.01635 0.01381 0.01115 0.00946
-Cumulative Proportion 0.0688 0.10286 0.12098 0.13733 0.15114 0.16229 0.17175
- PC8 PC9 PC10 PC11 PC12 PC13 PC14
-Standard deviation 1.47668 1.46595 1.44342 1.42486 1.41429 1.39361 1.37089
-Proportion of Variance 0.00779 0.00768 0.00744 0.00725 0.00714 0.00694 0.00671
-Cumulative Proportion 0.17954 0.18721 0.19466 0.20191 0.20905 0.21599 0.22270
- PC15 PC16 PC17 PC18 PC19 PC20 PC21
-Standard deviation 1.36576 1.33322 1.32697 1.32019 1.31398 1.30389 1.30191
-Proportion of Variance 0.00666 0.00635 0.00629 0.00622 0.00617 0.00607 0.00605
-Cumulative Proportion 0.22936 0.23571 0.24200 0.24822 0.25439 0.26046 0.26651
- PC22 PC23 PC24 PC25 PC26 PC27 PC28
-Standard deviation 1.2962 1.29071 1.28268 1.27844 1.2745 1.26791 1.2634
-Proportion of Variance 0.0060 0.00595 0.00588 0.00584 0.0058 0.00574 0.0057
-Cumulative Proportion 0.2725 0.27846 0.28434 0.29018 0.2960 0.30172 0.3074
- PC29 PC30 PC31 PC32 PC33 PC34 PC35
-Standard deviation 1.25770 1.24910 1.24288 1.23382 1.23236 1.22745 1.22563
-Proportion of Variance 0.00565 0.00557 0.00552 0.00544 0.00542 0.00538 0.00536
-Cumulative Proportion 0.31307 0.31864 0.32416 0.32959 0.33502 0.34040 0.34576
- PC36 PC37 PC38 PC39 PC40 PC41 PC42
-Standard deviation 1.2178 1.21186 1.20980 1.20808 1.20140 1.19771 1.19167
-Proportion of Variance 0.0053 0.00524 0.00523 0.00521 0.00515 0.00512 0.00507
-Cumulative Proportion 0.3511 0.35631 0.36153 0.36675 0.37190 0.37702 0.38210
- PC43 PC44 PC45 PC46 PC47 PC48 PC49
-Standard deviation 1.18693 1.1830 1.17952 1.17629 1.16840 1.16561 1.16494
-Proportion of Variance 0.00503 0.0050 0.00497 0.00494 0.00488 0.00485 0.00485
-Cumulative Proportion 0.38713 0.3921 0.39709 0.40204 0.40691 0.41176 0.41661
- PC50 PC51 PC52 PC53 PC54 PC55 PC56
-Standard deviation 1.16276 1.1597 1.15720 1.15025 1.14323 1.13645 1.1348
-Proportion of Variance 0.00483 0.0048 0.00478 0.00473 0.00467 0.00461 0.0046
-Cumulative Proportion 0.42144 0.4262 0.43102 0.43575 0.44042 0.44503 0.4496
- PC57 PC58 PC59 PC60 PC61 PC62 PC63
-Standard deviation 1.13171 1.12596 1.1226 1.12127 1.11630 1.11416 1.11191
-Proportion of Variance 0.00457 0.00453 0.0045 0.00449 0.00445 0.00443 0.00442
-Cumulative Proportion 0.45420 0.45873 0.4632 0.46772 0.47217 0.47661 0.48102
- PC64 PC65 PC66 PC67 PC68 PC69 PC70
-Standard deviation 1.10848 1.10387 1.09837 1.09610 1.09477 1.08975 1.08839
-Proportion of Variance 0.00439 0.00435 0.00431 0.00429 0.00428 0.00424 0.00423
-Cumulative Proportion 0.48541 0.48976 0.49407 0.49836 0.50264 0.50688 0.51111
- PC71 PC72 PC73 PC74 PC75 PC76 PC77
-Standard deviation 1.08334 1.08041 1.07919 1.07612 1.07293 1.06974 1.06652
-Proportion of Variance 0.00419 0.00417 0.00416 0.00414 0.00411 0.00409 0.00406
-Cumulative Proportion 0.51531 0.51947 0.52363 0.52777 0.53188 0.53597 0.54003
- PC78 PC79 PC80 PC81 PC82 PC83 PC84
-Standard deviation 1.06480 1.06249 1.05454 1.05217 1.04951 1.04646 1.0447
-Proportion of Variance 0.00405 0.00403 0.00397 0.00395 0.00393 0.00391 0.0039
-Cumulative Proportion 0.54408 0.54811 0.55208 0.55604 0.55997 0.56388 0.5678
- PC85 PC86 PC87 PC88 PC89 PC90 PC91
-Standard deviation 1.04048 1.03910 1.03605 1.03344 1.03033 1.02706 1.02398
-Proportion of Variance 0.00387 0.00386 0.00383 0.00381 0.00379 0.00377 0.00374
-Cumulative Proportion 0.57165 0.57550 0.57934 0.58315 0.58694 0.59071 0.59445
- PC92 PC93 PC94 PC95 PC96 PC97 PC98
-Standard deviation 1.02269 1.0178 1.01520 1.01022 1.00512 1.0043 1.00011
-Proportion of Variance 0.00374 0.0037 0.00368 0.00364 0.00361 0.0036 0.00357
-Cumulative Proportion 0.59819 0.6019 0.60557 0.60921 0.61282 0.6164 0.62000
- PC99 PC100 PC101 PC102 PC103 PC104 PC105
-Standard deviation 0.99823 0.99478 0.99302 0.9901 0.98821 0.98275 0.98033
-Proportion of Variance 0.00356 0.00353 0.00352 0.0035 0.00349 0.00345 0.00343
-Cumulative Proportion 0.62356 0.62709 0.63061 0.6341 0.63760 0.64105 0.64448
- PC106 PC107 PC108 PC109 PC110 PC111 PC112
-Standard deviation 0.97805 0.97199 0.96996 0.96693 0.96433 0.96244 0.95904
-Proportion of Variance 0.00342 0.00337 0.00336 0.00334 0.00332 0.00331 0.00328
-Cumulative Proportion 0.64790 0.65127 0.65463 0.65797 0.66129 0.66460 0.66789
- PC113 PC114 PC115 PC116 PC117 PC118 PC119
-Standard deviation 0.95590 0.95299 0.95160 0.94787 0.9468 0.94142 0.94086
-Proportion of Variance 0.00326 0.00324 0.00323 0.00321 0.0032 0.00317 0.00316
-Cumulative Proportion 0.67115 0.67439 0.67763 0.68084 0.6840 0.68720 0.69036
- PC120 PC121 PC122 PC123 PC124 PC125 PC126
-Standard deviation 0.93911 0.93573 0.93081 0.92879 0.92768 0.92383 0.92272
-Proportion of Variance 0.00315 0.00313 0.00309 0.00308 0.00307 0.00305 0.00304
-Cumulative Proportion 0.69351 0.69664 0.69973 0.70282 0.70589 0.70894 0.71198
- PC127 PC128 PC129 PC130 PC131 PC132 PC133
-Standard deviation 0.92121 0.91999 0.91773 0.91295 0.91056 0.90720 0.90358
-Proportion of Variance 0.00303 0.00302 0.00301 0.00298 0.00296 0.00294 0.00292
-Cumulative Proportion 0.71501 0.71803 0.72104 0.72402 0.72698 0.72992 0.73283
- PC134 PC135 PC136 PC137 PC138 PC139 PC140
-Standard deviation 0.9009 0.89932 0.89834 0.89301 0.89167 0.89028 0.88693
-Proportion of Variance 0.0029 0.00289 0.00288 0.00285 0.00284 0.00283 0.00281
-Cumulative Proportion 0.7357 0.73862 0.74150 0.74435 0.74719 0.75002 0.75283
- PC141 PC142 PC143 PC144 PC145 PC146 PC147
-Standard deviation 0.88267 0.88149 0.87828 0.87515 0.87308 0.87031 0.86696
-Proportion of Variance 0.00278 0.00278 0.00275 0.00274 0.00272 0.00271 0.00268
-Cumulative Proportion 0.75561 0.75839 0.76114 0.76388 0.76660 0.76931 0.77199
- PC148 PC149 PC150 PC151 PC152 PC153 PC154
-Standard deviation 0.86536 0.86323 0.86248 0.86061 0.85821 0.8531 0.85071
-Proportion of Variance 0.00267 0.00266 0.00266 0.00265 0.00263 0.0026 0.00258
-Cumulative Proportion 0.77466 0.77733 0.77998 0.78263 0.78526 0.7879 0.79044
- PC155 PC156 PC157 PC158 PC159 PC160 PC161
-Standard deviation 0.84901 0.84521 0.84208 0.84179 0.83835 0.8370 0.83336
-Proportion of Variance 0.00257 0.00255 0.00253 0.00253 0.00251 0.0025 0.00248
-Cumulative Proportion 0.79302 0.79557 0.79810 0.80063 0.80314 0.8056 0.80812
- PC162 PC163 PC164 PC165 PC166 PC167 PC168
-Standard deviation 0.83207 0.82803 0.82685 0.82371 0.82099 0.8189 0.81575
-Proportion of Variance 0.00247 0.00245 0.00244 0.00242 0.00241 0.0024 0.00238
-Cumulative Proportion 0.81060 0.81304 0.81549 0.81791 0.82032 0.8227 0.82509
- PC169 PC170 PC171 PC172 PC173 PC174 PC175
-Standard deviation 0.81407 0.81227 0.80721 0.8026 0.80146 0.79810 0.79633
-Proportion of Variance 0.00237 0.00236 0.00233 0.0023 0.00229 0.00227 0.00226
-Cumulative Proportion 0.82745 0.82981 0.83214 0.8344 0.83673 0.83901 0.84127
- PC176 PC177 PC178 PC179 PC180 PC181 PC182
-Standard deviation 0.79349 0.79025 0.78951 0.78582 0.78058 0.77895 0.77538
-Proportion of Variance 0.00225 0.00223 0.00223 0.00221 0.00218 0.00217 0.00215
-Cumulative Proportion 0.84352 0.84575 0.84798 0.85018 0.85236 0.85453 0.85667
- PC183 PC184 PC185 PC186 PC187 PC188 PC189
-Standard deviation 0.77191 0.77111 0.76859 0.7669 0.7666 0.76160 0.76145
-Proportion of Variance 0.00213 0.00212 0.00211 0.0021 0.0021 0.00207 0.00207
-Cumulative Proportion 0.85880 0.86093 0.86303 0.8651 0.8672 0.86931 0.87138
- PC190 PC191 PC192 PC193 PC194 PC195 PC196
-Standard deviation 0.75890 0.75735 0.75547 0.75079 0.7489 0.74442 0.74130
-Proportion of Variance 0.00206 0.00205 0.00204 0.00201 0.0020 0.00198 0.00196
-Cumulative Proportion 0.87343 0.87548 0.87752 0.87953 0.8815 0.88352 0.88548
- PC197 PC198 PC199 PC200 PC201 PC202 PC203
-Standard deviation 0.73911 0.73641 0.73232 0.73086 0.7299 0.72752 0.72384
-Proportion of Variance 0.00195 0.00194 0.00192 0.00191 0.0019 0.00189 0.00187
-Cumulative Proportion 0.88743 0.88937 0.89128 0.89319 0.8951 0.89698 0.89885
- PC204 PC205 PC206 PC207 PC208 PC209 PC210
-Standard deviation 0.72191 0.72004 0.71646 0.71404 0.71147 0.70617 0.70423
-Proportion of Variance 0.00186 0.00185 0.00183 0.00182 0.00181 0.00178 0.00177
-Cumulative Proportion 0.90071 0.90257 0.90440 0.90622 0.90803 0.90981 0.91158
- PC211 PC212 PC213 PC214 PC215 PC216 PC217
-Standard deviation 0.70298 0.69954 0.69657 0.69582 0.69276 0.69270 0.68833
-Proportion of Variance 0.00176 0.00175 0.00173 0.00173 0.00171 0.00171 0.00169
-Cumulative Proportion 0.91335 0.91509 0.91683 0.91856 0.92027 0.92198 0.92368
- PC218 PC219 PC220 PC221 PC222 PC223 PC224
-Standard deviation 0.68455 0.68008 0.67851 0.67558 0.67106 0.66645 0.66592
-Proportion of Variance 0.00167 0.00165 0.00164 0.00163 0.00161 0.00159 0.00158
-Cumulative Proportion 0.92535 0.92700 0.92864 0.93027 0.93188 0.93347 0.93505
- PC225 PC226 PC227 PC228 PC229 PC230 PC231
-Standard deviation 0.66215 0.65987 0.65570 0.65358 0.65119 0.6488 0.64568
-Proportion of Variance 0.00157 0.00156 0.00154 0.00153 0.00151 0.0015 0.00149
-Cumulative Proportion 0.93662 0.93817 0.93971 0.94124 0.94275 0.9443 0.94574
- PC232 PC233 PC234 PC235 PC236 PC237 PC238
-Standard deviation 0.64343 0.64096 0.63918 0.63427 0.63107 0.62967 0.6260
-Proportion of Variance 0.00148 0.00147 0.00146 0.00144 0.00142 0.00142 0.0014
-Cumulative Proportion 0.94722 0.94869 0.95015 0.95158 0.95301 0.95442 0.9558
- PC239 PC240 PC241 PC242 PC243 PC244 PC245
-Standard deviation 0.6253 0.62063 0.61999 0.61433 0.61119 0.60898 0.60787
-Proportion of Variance 0.0014 0.00138 0.00137 0.00135 0.00133 0.00132 0.00132
-Cumulative Proportion 0.9572 0.95859 0.95997 0.96131 0.96265 0.96397 0.96529
- PC246 PC247 PC248 PC249 PC250 PC251 PC252
-Standard deviation 0.6030 0.60131 0.59776 0.59573 0.59070 0.58844 0.5805
-Proportion of Variance 0.0013 0.00129 0.00128 0.00127 0.00125 0.00124 0.0012
-Cumulative Proportion 0.9666 0.96788 0.96916 0.97043 0.97167 0.97291 0.9741
- PC253 PC254 PC255 PC256 PC257 PC258 PC259
-Standard deviation 0.5791 0.57666 0.57115 0.57036 0.56503 0.56091 0.5561
-Proportion of Variance 0.0012 0.00119 0.00117 0.00116 0.00114 0.00112 0.0011
-Cumulative Proportion 0.9753 0.97650 0.97766 0.97882 0.97996 0.98109 0.9822
- PC260 PC261 PC262 PC263 PC264 PC265 PC266
-Standard deviation 0.55234 0.54999 0.54348 0.53916 0.53751 0.53097 0.52478
-Proportion of Variance 0.00109 0.00108 0.00105 0.00104 0.00103 0.00101 0.00098
-Cumulative Proportion 0.98328 0.98436 0.98542 0.98646 0.98749 0.98849 0.98948
- PC267 PC268 PC269 PC270 PC271 PC272 PC273
-Standard deviation 0.51696 0.51540 0.51156 0.5013 0.48840 0.47896 0.46889
-Proportion of Variance 0.00095 0.00095 0.00093 0.0009 0.00085 0.00082 0.00079
-Cumulative Proportion 0.99043 0.99138 0.99232 0.9932 0.99406 0.99488 0.99567
- PC274 PC275 PC276 PC277 PC278 PC279 PC280
-Standard deviation 0.46518 0.45568 0.43304 0.42194 0.40659 0.36945 0.34821
-Proportion of Variance 0.00077 0.00074 0.00067 0.00064 0.00059 0.00049 0.00043
-Cumulative Proportion 0.99644 0.99718 0.99785 0.99849 0.99908 0.99957 1.00000
-
-
-Importance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Standard deviation 4.3892 3.08797 2.25263 2.13943 1.96659 1.76697 1.62753 1.47668 Proportion of Variance 0.0688 0.03406 0.01812 0.01635 0.01381 0.01115 0.00946 0.00779 Cumulative Proportion 0.0688 0.10286 0.12098 0.13733 0.15114 0.16229 0.17175 0.17954
+Importance of first k=15 (out of 280) components:
+ PC1 PC2 PC3 PC4 PC5 PC6 PC7
+Standard deviation 12.5612 8.36646 5.98988 5.41386 4.55730 4.06142 3.84444
+Proportion of Variance 0.1099 0.04874 0.02498 0.02041 0.01446 0.01149 0.01029
+Cumulative Proportion 0.1099 0.15861 0.18359 0.20400 0.21846 0.22995 0.24024
+ PC8 PC9 PC10 PC11 PC12 PC13 PC14
+Standard deviation 3.70848 3.66899 3.5549 3.48508 3.44964 3.42393 3.37882
+Proportion of Variance 0.00958 0.00937 0.0088 0.00846 0.00829 0.00816 0.00795
+Cumulative Proportion 0.24982 0.25919 0.2680 0.27645 0.28473 0.29290 0.30085
+ PC15
+Standard deviation 3.33622
+Proportion of Variance 0.00775
+Cumulative Proportion 0.30860
+
+The Proportion of Variance tells us how much of the variance is explained by each component. We can see that the first component explains 0.1099 of the variance, the second 0.04874, and the third 0.2498. Together the first three components explain 18% of the total variance in the data. Plotting PC1 against PC2 will capture about 16% of the variance. This is not that high but it likely better than we would get plotting any two genes against each other. To plot the PC1 against PC2 we will need to extract the PC1 and PC2 score from the pca object and add labels for the cells.
+🎬 Create a dataframe of the PC1 and PC2 scores which are in pca$x and add the cell ids:
-pca_labelled <- data.frame(pca$x,
+pca_labelled <- data.frame(pca$x,
cell_id = row.names(prog_hspc_trans))
-add the cell type information so we can label points split cell_id into cell type and replicate and keep cell_id column
+It will be helpful to add a column for the cell type so we can label points. One way to do this is to extract the information in the cell_id column into two columns.
+🎬 Extract the cell type and cell number from the cell_id column (keeping the cell_id column):
-pca_labelled <- pca_labelled |>
+
+"([a-zA-Z]{4})_([0-9]{3})" is a regular expression - or regex. [a-zA-Z] means any lower or upper case letter, {4} means 4 of them, and [0-9] means any number, {3} means 3 of them. The brackets around the two parts of the regex mean we want to extract those parts. The first part goes into cell_type and the second part goes into cell_number. The _ between the two patterns matches the underscore and the fact it isn’t in a bracket means we don’t want to keep it.
+We can now plot the PC1 and PC2 scores.
+🎬 Plot PC1 against PC2 and colour the points by cell type:
-pca <- pca_labelled |>
+pca <- pca_labelled |>
ggplot(aes(x = PC1, y = PC2,
colour = cell_type)) +
geom_point(alpha = 0.4) +
scale_colour_viridis_d(end = 0.8, begin = 0.15,
name = "Cell type") +
- theme_classic()
+ theme_classic()
+pca
+
+
+
Fairly good separation of cell types but plenty of overlap
+🎬 Save the plot to file:
-ggsave("figures/prog_hspc-pca.png",
+ggsave("figures/prog_hspc-pca.png",
plot = pca,
height = 3,
width = 4,
units = "in",
device = "png")
-tSNE ??
Visualise the expression of the most significant genes using a heatmap
we will use the most significant genes on a random subset of the cells since ~1500 columns is a lot
-mat <- prog_hspc_results_sig0.01 |>
+
-rownames(mat) <- prog_hspc_results_sig0.01$external_gene_name
+rownames(mat) <- prog_hspc_results_sig0.01$external_gene_name
-n_cell_clusters <- 2
+n_cell_clusters <- 2
n_gene_clusters <- 2
-heatmaply(mat,
+heatmaply(mat,
scale = "row",
hide_colorbar = TRUE,
k_col = n_cell_clusters,
@@ -961,24 +2525,24 @@ Workshop
labRow = rownames(mat),
heatmap_layers = theme(axis.line = element_blank()))
-
-
+
+
will take a few mins to run, and longer to appear in the viewer separation is not as strong as for the frog data run a few times to see different subset
Visualise all the results with a volcano plot
colour the points if FDR < 0.05 and prog_hspc_results > 1
-prog_hspc_results <- prog_hspc_results |>
+
-vol <- prog_hspc_results |>
+vol <- prog_hspc_results |>
ggplot(aes(x = summary.logFC,
y = log10_FDR,
colour = interaction(sig, bigfc))) +
@@ -1003,7 +2567,7 @@ Workshop
theme(legend.position = "none")
-ggsave("figures/prog-hspc-volcano.png",
+ggsave("figures/prog-hspc-volcano.png",
plot = vol,
height = 4.5,
width = 4.5,
@@ -1047,15 +2611,30 @@ Workshop
Durinck, Steffen, Paul T. Spellman, Ewan Birney, and Wolfgang Huber. 2009. “Mapping Identifiers for the Integration of Genomic Datasets with the r/Bioconductor Package biomaRt” 4.
+
+Fisher, Malcolm, Christina James-Zorn, Virgilio Ponferrada, Andrew J Bell, Nivitha Sundararaj, Erik Segerdell, Praneet Chaturvedi, et al. 2023. “Xenbase: Key Features and Resources of the Xenopus Model Organism Knowledgebase.” Genetics 224 (1): iyad018. https://doi.org/10.1093/genetics/iyad018.
+
Hester, Jim, Hadley Wickham, and Gábor Csárdi. 2023. “Fs: Cross-Platform File System Operations Based on ’Libuv’.”
+
+Martin, Fergal J, M Ridwan Amode, Alisha Aneja, Olanrewaju Austine-Orimoloye, Andrey G Azov, If Barnes, Arne Becker, et al. 2023. “Ensembl 2023.” Nucleic Acids Research 51 (D1): D933–41. https://doi.org/10.1093/nar/gkac958.
+
R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
+
+Smedley, Damian, Syed Haider, Benoit Ballester, Richard Holland, Darin London, Gudmundur Thorisson, and Arek Kasprzyk. 2009. “BioMart Biological Queries Made Easy.” BMC Genomics 10 (1): 22. https://doi.org/10.1186/1471-2164-10-22.
+
+
+Wickham, Hadley. 2023. “Conflicted: An Alternative Conflict Resolution Strategy.”
+
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the Tidyverse” 4: 1686. https://doi.org/10.21105/joss.01686.
+
+Wickham, Hadley, and Jennifer Bryan. 2023. “Readxl: Read Excel Files.” https://CRAN.R-project.org/package=readxl.
+
Xie, Yihui. 2022. “Knitr: A General-Purpose Package for Dynamic Report Generation in r.” https://yihui.org/knitr/.
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--- a/search.json
+++ b/search.json
@@ -39,42 +39,49 @@
"href": "omics/week-5/workshop.html",
"title": "Workshop",
"section": "",
- "text": "In the workshop, you will"
+ "text": "In the workshop, you will learn how to merge gene information into our results, conduct and plot a Principle Component Analysis (PCA) as well as how to create a nicely formatted Volcano plot and heatmap."
},
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"href": "omics/week-5/workshop.html#session-overview",
"title": "Workshop",
"section": "",
- "text": "In the workshop, you will"
+ "text": "In the workshop, you will learn how to merge gene information into our results, conduct and plot a Principle Component Analysis (PCA) as well as how to create a nicely formatted Volcano plot and heatmap."
},
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"section": "Import",
- "text": "Import\nWe need to import both the normalised counts and the statistical results. We will need all of these for the visualisation and interpretation.\n🎬 Import files saved from last week from the results folder: S30_normalised_counts.csv and S30_results.csv. I used the names s30_count_norm and s30_results for the dataframes.\n🎬 Remind yourself what is in the rows and columns and the structure of the dataframes (perhaps using glimpse())\n🎬 Merge the two dataframes:\n\n# merge the results with the normalised counts\ns30_results <- s30_count_norm |>\n left_join(s30_results, by = \"xenbase_gene_id\")\n\nThis means you have the counts for each sample along with the statistical results for each gene."
+ "text": "Import\nWe need to import both the normalised counts and the statistical results. We will need all of these for the visualisation and interpretation.\n🎬 Import files saved from last week from the results folder: S30_normalised_counts.csv and S30_results.csv. I used the names s30_count_norm and s30_results for the dataframes.\n🎬 Remind yourself what is in the rows and columns and the structure of the dataframes (perhaps using glimpse())\n\n#---CODING ANSWER---\n#| include: false\nglimpse(s30_count_norm)\n\nRows: 10,136\nColumns: 7\n$ S30_C_5 <dbl> 228.092879, 480.016357, 111.209463, 48.795989, 439.163…\n$ S30_C_6 <dbl> 222.114104, 498.170204, 81.441838, 45.480507, 551.0545…\n$ S30_C_A <dbl> 238.647198, 668.212153, 83.141604, 53.118247, 282.5274…\n$ S30_F_5 <dbl> 251.794947, 453.047112, 98.328684, 70.759894, 427.3162…\n$ S30_F_6 <dbl> 239.567397, 481.986786, 81.757127, 44.681221, 539.9773…\n$ S30_F_A <dbl> 245.10704, 607.96159, 62.47827, 36.04515, 279.95069, 1…\n$ xenbase_gene_id <chr> \"XB-GENE-1000007\", \"XB-GENE-1000023\", \"XB-GENE-1000062…\n\n\n\n\n\n\n\n\n\n\n\n\n#---CODING ANSWER---\n#| include: false\nglimpse(s30_results)\n\nRows: 10,136\nColumns: 7\n$ baseMean <dbl> 237.553928, 531.565700, 86.392830, 49.813502, 419.9983…\n$ log2FoldChange <dbl> 0.096601855, -0.089588528, -0.192811203, -0.008858703,…\n$ lfcSE <dbl> 0.2079396, 0.1557384, 0.3253216, 0.4342614, 0.1685420,…\n$ stat <dbl> 0.46456683, -0.57525007, -0.59267874, -0.02039947, -0.…\n$ pvalue <dbl> 0.64224169, 0.56512218, 0.55339617, 0.98372471, 0.8699…\n$ padj <dbl> 0.9998970, 0.9998970, 0.9998970, 0.9998970, 0.9998970,…\n$ xenbase_gene_id <chr> \"XB-GENE-1000007\", \"XB-GENE-1000023\", \"XB-GENE-1000062…\n\n\n\n\n\n\nIt is useful to have this information in a single dataframe to which we will add the gene information from xenbase. Having all the information together will make it easier to interpret the results and select genes of interest.\n🎬 Merge the two dataframes:\n\n# merge the results with the normalised counts\ns30_results <- s30_count_norm |>\n left_join(s30_results, by = \"xenbase_gene_id\")\n\nThis means you have the counts for each sample along with the statistical results for each gene."
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- "section": "Add gene information from xenbase",
- "text": "Add gene information from xenbase\nThis information comes from the Xenbase information pages\n\nxenbase Gene Product Information [readme]gzipped gpi (tab separated)\n\nThe xenbase.gpi.gz file contains gene product information for species specific Xenbase genes.\nIf you click on the readme link you can see information telling you that the file is in the Gene Product information 2.1 format and is provided with gzip compression. gene product information for both Xenopus tropicalis (taxon:8364) and Xenopus laevis (taxon:8355)\n🎬 If you want to\ngunzip xenbase.gpi.gz\nless xenbase.gpi\nq\n\nlibrary(readxl)\n\n\ngene_info <- read_excel(\"meta/xenbase_info.xlsx\") \n\n\n# join the gene info with the results\ns30_results <- s30_results |>\n left_join(gene_info, by = \"xenbase_gene_id\")\n\n\n# Import metadata that maps the sample names to treatments\nmeta <- read_table(\"meta/frog_meta_data.txt\")\nrow.names(meta) <- meta$sample_id\n\nWe only need the s30\n\nmeta_s30 <- meta |>\n dplyr::filter(stage == \"stage_30\")\n\nlog2 transformed normalised counts needed for data viz\nlog2 transform the counts in s30_count_norm add a tiny amount to avoid log(0)\n\n# log2 transform the counts in s30_count_norm\n# add a tiny amount to avoid log(0)\ns30_results <- s30_results |>\n mutate(across(starts_with(\"s30\"), \n \\(x) log2(x + 0.001),\n .names = \"log2_{.col}\"))\n\nread more about across() and anonymous functions from my posit::conf(2023) workshop\nWe now have datatframe with all the info we need, normalised counts, log2 normalised counts, statistical comparisons with fold changes and p values, information about the gene other than just the id"
+ "section": "Add gene information from Xenbase",
+ "text": "Add gene information from Xenbase\n\nI got the information from the Xenbase information pages under Data Reports | Gene Information\nThis is listed: Xenbase Gene Product Information [readme] gzipped gpi (tab separated)\nClick on the readme link to see the file format and columns\nI downloaded xenbase.gpi.gz, unzipped it, removed header lines and the Xenopus tropicalis (taxon:8364) entries and saved it as xenbase_info.xlsx\n\nIf you want to emulate what I did you can use the following commands in the terminal after downloading the file:\ngunzip xenbase.gpi.gz\nless xenbase.gpi\nq\ngunzip unzips the file and less allows you to view the file. q quits the viewer. You will see the header lines and that the file contains both Xenopus tropicalis and Xenopus laevis. I read the file in with read_tsv (skipping the first header lines) then filtered out the Xenopus tropicalis entries, dropped some columns and saved the file as an excel file.\nHowever, I have already done this for you and saved the file as xenbase_info.xlsx in the meta folder. We will import this file and join it to the results dataframe.\n🎬 Load the readxl (Wickham and Bryan 2023) package:\n\nlibrary(readxl)\n\n🎬 Import the Xenbase gene information file:\n\ngene_info <- read_excel(\"meta/xenbase_info.xlsx\") \n\nYou should view the resulting dataframe to see what information is available. You can use glimpse() or View().\n🎬 Merge the gene information with the results:\n\n# join the gene info with the results\ns30_results <- s30_results |>\n left_join(gene_info, by = \"xenbase_gene_id\")\n\nWe will also find it useful to import the metadata that maps the sample names to treatments. This will allow us to label the samples in the visualisations.\n🎬 Import the metadata that maps the sample names to treatments:\n\n# Import metadata that maps the sample names to treatments\nmeta <- read_table(\"meta/frog_meta_data.txt\")\nrow.names(meta) <- meta$sample_id\n# We only need the s30\nmeta_s30 <- meta |>\n dplyr::filter(stage == \"stage_30\")"
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+ "href": "omics/week-5/workshop.html#log2-transform-the-data",
+ "title": "Workshop",
+ "section": "log2 transform the data",
+ "text": "log2 transform the data\nWe use the normalised counts for data visualisations so that the comparisons are meaningful. Since the fold changes are given is log2 it is useful to log2 transform the normalised counts too. We will add columns to the dataframe with these transformed values. Since we have some counts of 0 we will add a tiny amount to avoid -Inf values.\n🎬 log2 transform the normalised counts:\n\n# log2 transform the counts plus a tiny amount to avoid log(0)\ns30_results <- s30_results |>\n mutate(across(starts_with(\"s30\"), \n \\(x) log2(x + 0.001),\n .names = \"log2_{.col}\"))\n\nThis is a wonderful bit or R wizardy. We are using the across() function to apply a transformation to multiple columns. We have selected all the columns that start with s30. The \\(x) is an “anonymous” function that takes the value of the column and adds 0.001 to it before applying the log2() function. The .names = \"log2_{.col}\" argument tells across() to name the new columns with the prefix log2_ followed by the original column name. You can read more about across() and anonymous functions from my posit::conf(2023) workshop\nI recommend viewing the dataframe to see the new columns.\nWe now have dataframe with all the information we need: normalised counts, log2 normalised counts, statistical comparisons with fold changes and p values, and information about the gene other than just the id."
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"section": "Write the significant genes to file",
- "text": "Write the significant genes to file\n\ns30_results_sig0.01 <- s30_results |> \n filter(padj <= 0.01)\n# 59 genes\n\n\n# write to csv file\nwrite_csv(s30_results_sig0.01, \n file = \"results/s30_results_sig0.01.csv\")\n\n\ns30_results_sig0.05 <- s30_results |> \n filter(padj <= 0.05)\n\n# 117 genes\n\n\n# write to csv file\nwrite_csv(s30_results_sig0.05, \n file = \"results/s30_results_sig0.05.csv\")"
+ "text": "Write the significant genes to file\nWe will create dateframe of the signifcant genes and wrte them to file. These are the files you want to examine in more detail along with the visualisations to select your genes of interest.\n🎬 Create a dataframe of the genes significant at the 0.01 level:\n\ns30_results_sig0.01 <- s30_results |> \n filter(padj <= 0.01)\n\n🎬 Write the dataframe to file\n\n#---CODING ANSWER---\n#| include: false\n# write to csv file\nwrite_csv(s30_results_sig0.01, \n file = \"results/s30_results_sig0.01.csv\")\n\n🎬 Create a dataframe of the genes significant at the 0.05 level and write to file:\n\n#---CODING ANSWER---\n#| include: false\ns30_results_sig0.05 <- s30_results |> \n filter(padj <= 0.05)\n\n# write to csv file\nwrite_csv(s30_results_sig0.05, \n file = \"results/s30_results_sig0.05.csv\")\n\n❓How many genes are significant at the 0.01 and 0.05 levels?"
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"section": "View the relationship between samples using PCA",
- "text": "View the relationship between samples using PCA\nwe do this on the log2 transformed normalised counts or the regularized the log transformed counts\ntranspose the data we are reducing he number of dimensions from 10136 to 6\n\ns30_log2_trans <- s30_results |> \n select(starts_with(\"log2_\")) |>\n t() |> \n data.frame()\n\n\ncolnames(s30_log2_trans) <- s30_results$xenbase_gene_id\n\nperform PCA using standard functions\n\npca <- s30_log2_trans |>\n prcomp(scale. = TRUE) \n\n\nsummary(pca)\n\nImportance of components:\n PC1 PC2 PC3 PC4 PC5 PC6\nStandard deviation 62.3983 45.6748 44.5555 33.3601 32.5295 2.405e-13\nProportion of Variance 0.3841 0.2058 0.1959 0.1098 0.1044 0.000e+00\nCumulative Proportion 0.3841 0.5899 0.7858 0.8956 1.0000 1.000e+00\n\n\nImportance of components: PC1 PC2 PC3 PC4 PC5 PC6 Standard deviation 57.1519 50.3285 43.8382 35.4644 33.9440 2.403e-13 Proportion of Variance 0.3224 0.2500 0.1897 0.1241 0.1137 0.000e+00 Cumulative Proportion 0.3224 0.5724 0.7621 0.8863 1.0000 1.000e+00\nremove log2 from row.names(s30_log2_trans) to label the pca results\n\nsample_id <- row.names(s30_log2_trans) |> str_remove(\"log2_\")\n\n\npca_labelled <- data.frame(pca$x,\n sample_id)\n\nmerge with metadata so we can label points by treatment and sib group\n\npca_labelled <- pca_labelled |> \n left_join(meta_s30, \n by = \"sample_id\")\n\n\npca <- pca_labelled |> \n ggplot(aes(x = PC1, y = PC2, \n colour = sibling_rep,\n shape = treatment)) +\n geom_point(size = 3) +\n scale_colour_viridis_d(end = 0.95, begin = 0.15,\n name = \"Sibling pair\",\n labels = c(\"A\", \".5\", \".6\")) +\n scale_shape_manual(values = c(21, 19),\n name = NULL,\n labels = c(\"Control\", \"FGF-Treated\")) +\n theme_classic()\n\nThere is a bit of separation between treatments on PCA2 not that it isn’t easy to draw strong conclusions on the basis of 3 points\n\nggsave(\"figures/frog-s30-pca.png\",\n plot = pca,\n height = 3, \n width = 4,\n units = \"in\",\n device = \"png\")"
+ "text": "View the relationship between samples using PCA\nWe have 10,136 genes in our dataset. PCA will allow us to plot our samples in the “gene expression” space so we can see if FGF-treated sample cluster together and control samples cluster together as we would expect. We do this on the log2 transformed normalised counts.\nOur data have genes in rows and samples in columns which is a common organisation for gene expression data. However, PCA expects samples in rows and genes, the variables, in columns. We can transpose the data to get it in the correct format.\n🎬 Transpose the log2 transformed normalised counts:\n\ns30_log2_trans <- s30_results |> \n select(starts_with(\"log2_\")) |>\n t() |> \n data.frame()\n\nWe have used the select() function to select all the columns that start with log2_. We then use the t() function to transpose the dataframe. We then convert the resulting matrix to a dataframe using data.frame(). If you view that dataframe you’ll see it has default column name which we can fix using colnames() to set the column names to the Xenbase gene ids.\n🎬 Set the column names to the Xenbase gene ids:\n\ncolnames(s30_log2_trans) <- s30_results$xenbase_gene_id\n\n🎬 Perform PCA on the log2 transformed normalised counts:\n\npca <- s30_log2_trans |>\n prcomp(rank. = 4) \n\nThe rank. argument tells prcomp() to only calculate the first 4 principal components. This is useful for visualisation as we can only plot in 2 or 3 dimensions. We can see the results of the PCA by viewing the summary() of the pca object.\n\nsummary(pca)\n\nImportance of first k=4 (out of 6) components:\n PC1 PC2 PC3 PC4\nStandard deviation 64.0124 47.3351 38.4706 31.4111\nProportion of Variance 0.4243 0.2320 0.1532 0.1022\nCumulative Proportion 0.4243 0.6562 0.8095 0.9116\n\n\nThe Proportion of Variance tells us how much of the variance is explained by each component. We can see that the first component explains 0.4243 of the variance, the second 0.2320, and the third 0.1532. Together the first three components explain nearly 81% of the total variance in the data. Plotting PC1 against PC2 will capture about 66% of the variance which is likely much better than we would get plotting any two genes against each other. To plot the PC1 against PC2 we will need to extract the PC1 and PC2 score from the pca object and add labels for the samples.\n🎬 Remove log2 from the row names:\n\nsample_id <- row.names(s30_log2_trans) |> str_remove(\"log2_\")\n\n🎬 Create a dataframe of the PC1 and PC2 scores which are in pca$x and add the sample ids:\n\npca_labelled <- data.frame(pca$x,\n sample_id)\n\n🎬 Merge with the metadata so we can label points by treatment and sibling pair:\n\npca_labelled <- pca_labelled |> \n left_join(meta_s30, \n by = \"sample_id\")\n\nSince the metadata contained the sample ids, it was especially important to remove the log2_ from the row names so that the join would work. The dataframe should look like this:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPC1\nPC2\nPC3\nPC4\nsample_id\nstage\ntreatment\nsibling_rep\n\n\n\n-76.38391\n0.814699\n-60.728327\n-5.820669\nS30_C_5\nstage_30\ncontrol\nfive\n\n\n-67.02571\n25.668563\n51.476835\n28.480254\nS30_C_6\nstage_30\ncontrol\nsix\n\n\n-14.02772\n-78.474054\n15.282058\n-9.213076\nS30_C_A\nstage_30\ncontrol\nA\n\n\n47.60726\n49.035510\n-19.288753\n20.928290\nS30_F_5\nstage_30\nFGF\nfive\n\n\n26.04954\n32.914201\n20.206072\n-55.752818\nS30_F_6\nstage_30\nFGF\nsix\n\n\n83.78054\n-29.958919\n-6.947884\n21.378020\nS30_F_A\nstage_30\nFGF\nA\n\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by sibling pair and shape by treatment:\n\npca <- pca_labelled |> \n ggplot(aes(x = PC1, y = PC2, \n colour = sibling_rep,\n shape = treatment)) +\n geom_point(size = 3) +\n scale_colour_viridis_d(end = 0.95, begin = 0.15,\n name = \"Sibling pair\",\n labels = c(\"A\", \".5\", \".6\")) +\n scale_shape_manual(values = c(21, 19),\n name = NULL,\n labels = c(\"Control\", \"FGF-Treated\")) +\n theme_classic()\npca\n\n\n\n\nThere is a good separation between treatments on PCA1. The sibling pairs do not seem to cluster together.\n🎬 Save the plot to file:\n\nggsave(\"figures/frog-s30-pca.png\",\n plot = pca,\n height = 3, \n width = 4,\n units = \"in\",\n device = \"png\")"
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@@ -95,28 +102,28 @@
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"title": "Workshop",
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- "text": "Import\n🎬 Import\n🎬 Combine the two dataframes (minus the gene ids) into one dataframe called prog_hspc:\n\n# combine into one dataframe dropping one of the gene id columns\nprog_hspc <- bind_cols(prog, hspc[-1])\n\n🎬 …\n\n# import the DE results\nprog_hspc_results <- read_csv(\"results/prog_hspc_results.csv\")\n\n🎬 …\n\n# merge stats results with normalise values\nprog_hspc_results <- prog_hspc_results |> \n left_join(prog_hspc, by = \"ensembl_gene_id\")"
+ "text": "Import\nWe need to import both the normalised counts and the statistical results. We will need all of these for the visualisation and interpretation.\n🎬 Import the normalised counts for the Prog and HSPC cell types. I used the names prog and hspc for the dataframes.\n🎬 Combine the two dataframes (minus one set of gene ids) into one dataframe called prog_hspc:\n\n# combine into one dataframe dropping one of the gene id columns\nprog_hspc <- bind_cols(prog, hspc[-1])\n\n🎬 Import the statistical results in results/prog_hspc_results.csv. I used the name prog_hspc_results for the dataframe.\n🎬 Remind yourself what is in the rows and columns and the structure of the dataframes (perhaps using glimpse())\n\n#---CODING ANSWER---\n#| include: false\nglimpse(prog_hspc)\n\nRows: 280\nColumns: 1,500\n$ ensembl_gene_id <chr> \"ENSMUSG00000004730\", \"ENSMUSG00000027962\", \"ENSMUSG00…\n$ Prog_001 <dbl> 0.000000, 0.000000, 2.447692, 0.000000, 2.447692, 1.07…\n$ Prog_002 <dbl> 0.0000000, 0.7859542, 9.8669873, 0.0000000, 7.5139828,…\n$ Prog_003 <dbl> 0.000000, 1.049924, 9.466541, 0.000000, 7.636827, 1.04…\n$ Prog_004 <dbl> 1.032808, 0.000000, 2.639234, 0.000000, 2.639234, 0.00…\n$ Prog_006 <dbl> 0.0000000, 0.9376688, 8.9509200, 0.0000000, 0.5437090,…\n$ Prog_007 <dbl> 0.0000000, 0.7008173, 2.2487025, 0.0000000, 2.0451378,…\n$ Prog_008 <dbl> 0.0000000, 0.0000000, 9.8216688, 0.0000000, 7.9747826,…\n$ Prog_009 <dbl> 0.0000000, 0.0000000, 10.3888553, 0.0000000, 4.4437936…\n$ Prog_010 <dbl> 0.000000, 0.000000, 3.277715, 1.453985, 6.670995, 1.45…\n$ Prog_011 <dbl> 0.000000, 0.000000, 9.329603, 0.000000, 1.756729, 1.13…\n$ Prog_012 <dbl> 1.909353, 1.909353, 3.588040, 0.000000, 0.000000, 0.00…\n$ Prog_013 <dbl> 0.0000000, 1.2104051, 0.7285849, 4.6985794, 7.8677891,…\n$ Prog_014 <dbl> 0.0000000, 0.0000000, 8.6212214, 0.0000000, 2.4391238,…\n$ Prog_015 <dbl> 0.0000000, 0.9646149, 1.5375869, 0.0000000, 0.0000000,…\n$ Prog_016 <dbl> 0.000000, 0.000000, 9.604794, 0.000000, 8.631105, 0.00…\n$ Prog_017 <dbl> 0.0000000, 0.6852295, 1.4976754, 0.0000000, 5.5232130,…\n$ Prog_018 <dbl> 0.000000, 0.000000, 8.815843, 0.000000, 1.775663, 0.00…\n$ Prog_019 <dbl> 0.0000000, 1.2988288, 0.7908933, 0.0000000, 4.5175504,…\n$ Prog_020 <dbl> 0.000000, 0.000000, 10.912794, 0.000000, 1.588903, 1.0…\n$ Prog_021 <dbl> 0.000000, 0.000000, 7.638658, 0.000000, 0.000000, 1.08…\n$ Prog_022 <dbl> 0.000000, 0.000000, 8.671409, 0.000000, 0.000000, 0.00…\n$ Prog_023 <dbl> 0.0000000, 2.1551569, 1.6194142, 0.0000000, 1.9120124,…\n$ Prog_024 <dbl> 1.717498, 0.000000, 9.023824, 0.000000, 7.928356, 0.00…\n$ Prog_025 <dbl> 0.000000, 2.368099, 8.553185, 0.000000, 5.413663, 1.62…\n$ Prog_026 <dbl> 0.000000, 0.000000, 3.648527, 1.190477, 1.190477, 0.00…\n$ Prog_027 <dbl> 0.0000000, 0.0000000, 0.8557008, 0.0000000, 1.3891677,…\n$ Prog_028 <dbl> 0.0000000, 0.0000000, 9.9095248, 0.0000000, 8.1128539,…\n$ Prog_029 <dbl> 1.152871, 1.152871, 5.870544, 0.000000, 3.060282, 0.00…\n$ Prog_030 <dbl> 0.000000, 2.598365, 9.181785, 2.127853, 8.651444, 0.00…\n$ Prog_031 <dbl> 0.0000000, 0.8413201, 9.2314616, 0.8413201, 2.5235954,…\n$ Prog_032 <dbl> 0.000000, 1.327820, 9.568213, 0.000000, 7.984181, 0.00…\n$ Prog_033 <dbl> 0.0000000, 1.3828699, 9.7312399, 0.0000000, 1.3828699,…\n$ Prog_035 <dbl> 0.0000000, 1.3171343, 9.3029109, 0.0000000, 6.4998843,…\n$ Prog_036 <dbl> 0.000000, 1.499263, 9.081582, 0.000000, 3.054489, 0.00…\n$ Prog_037 <dbl> 0.000000, 1.042273, 7.977819, 0.000000, 8.941148, 0.00…\n$ Prog_038 <dbl> 0.000000, 1.602107, 10.272361, 0.000000, 1.012878, 1.0…\n$ Prog_039 <dbl> 0.0000000, 0.0000000, 8.7123610, 0.0000000, 7.5301015,…\n$ Prog_040 <dbl> 0.000000, 0.000000, 2.054034, 8.442082, 2.054034, 1.36…\n$ Prog_042 <dbl> 0.000000, 0.000000, 10.345104, 1.507712, 1.507712, 0.0…\n$ Prog_043 <dbl> 0.0000000, 0.8515947, 10.7190561, 0.0000000, 1.3834907…\n$ Prog_044 <dbl> 0.000000, 1.139041, 1.767524, 1.139041, 5.258244, 0.00…\n$ Prog_045 <dbl> 0.6854953, 0.6854953, 9.9645500, 0.0000000, 8.6240290,…\n$ Prog_046 <dbl> 0.000000, 0.000000, 9.450093, 0.000000, 1.866185, 0.91…\n$ Prog_047 <dbl> 0.000000, 8.678573, 2.850378, 1.165030, 3.874419, 1.16…\n$ Prog_048 <dbl> 0.000000, 0.000000, 2.794052, 0.000000, 1.825335, 1.82…\n$ Prog_049 <dbl> 0.0000000, 0.6579981, 9.3946513, 0.0000000, 4.7857083,…\n$ Prog_050 <dbl> 0.0000000, 0.8386825, 2.0543847, 0.0000000, 6.6143445,…\n$ Prog_051 <dbl> 0.000000, 0.000000, 10.468034, 0.000000, 1.066147, 2.0…\n$ Prog_052 <dbl> 0.000000, 2.361305, 4.133679, 0.000000, 8.233765, 1.61…\n$ Prog_053 <dbl> 0.9224419, 0.9224419, 9.2857331, 0.0000000, 1.8820411,…\n$ Prog_055 <dbl> 0.0000000, 0.0000000, 3.4913807, 0.0000000, 6.6568051,…\n$ Prog_056 <dbl> 0.000000, 0.000000, 9.725035, 0.000000, 8.116732, 0.00…\n$ Prog_057 <dbl> 0.5967097, 0.0000000, 2.3499624, 0.0000000, 2.0262980,…\n$ Prog_058 <dbl> 0.000000, 0.000000, 10.114434, 0.000000, 5.739715, 0.0…\n$ Prog_059 <dbl> 0.000000, 2.038654, 8.871380, 0.000000, 7.819265, 1.02…\n$ Prog_060 <dbl> 0.000000, 1.776961, 9.102002, 0.000000, 2.549440, 1.14…\n$ Prog_061 <dbl> 0.0000000, 0.8198795, 10.4280461, 0.0000000, 1.3394322…\n$ Prog_062 <dbl> 0.000000, 0.000000, 9.964751, 0.000000, 5.990110, 0.00…\n$ Prog_063 <dbl> 0.000000, 8.002331, 2.337480, 1.597934, 3.477092, 0.00…\n$ Prog_064 <dbl> 0.000000, 1.007715, 10.123899, 1.007715, 6.687664, 0.0…\n$ Prog_065 <dbl> 0.0000000, 0.7570004, 2.1537620, 0.0000000, 8.4055113,…\n$ Prog_066 <dbl> 0.000000, 0.000000, 6.895361, 0.000000, 2.160890, 0.00…\n$ Prog_067 <dbl> 0.000000, 0.000000, 9.037415, 0.000000, 1.747210, 2.18…\n$ Prog_068 <dbl> 0.000000, 0.000000, 1.706107, 0.000000, 7.498985, 0.00…\n$ Prog_069 <dbl> 0.0000000, 0.0000000, 2.6246170, 0.8962338, 8.5297976,…\n$ Prog_070 <dbl> 0.000000, 1.475962, 3.022681, 1.475962, 9.416561, 0.00…\n$ Prog_071 <dbl> 0.000000, 1.028372, 0.000000, 1.028372, 1.622670, 0.00…\n$ Prog_072 <dbl> 0.000000, 1.478766, 10.580032, 0.000000, 8.863092, 2.1…\n$ Prog_073 <dbl> 0.0000000, 0.0000000, 3.6520519, 0.0000000, 0.0000000,…\n$ Prog_074 <dbl> 0.000000, 0.000000, 9.408942, 0.000000, 2.193005, 0.00…\n$ Prog_075 <dbl> 0.0000000, 0.0000000, 2.2865667, 0.0000000, 1.7343788,…\n$ Prog_076 <dbl> 0.000000, 0.000000, 9.433579, 0.000000, 6.383341, 1.04…\n$ Prog_077 <dbl> 0.000000, 1.068131, 9.018616, 1.068131, 5.035832, 2.69…\n$ Prog_078 <dbl> 0.0000000, 0.0000000, 11.2234617, 0.0000000, 7.5155691…\n$ Prog_079 <dbl> 0.0000000, 0.0000000, 11.1687105, 0.0000000, 1.2918528…\n$ Prog_080 <dbl> 0.000000, 6.834183, 4.353284, 0.000000, 8.210067, 1.46…\n$ Prog_081 <dbl> 0.663083, 0.663083, 9.804585, 0.000000, 1.459635, 0.00…\n$ Prog_082 <dbl> 0.0000000, 0.0000000, 10.2058437, 0.0000000, 0.9846850…\n$ Prog_083 <dbl> 0.0000000, 1.4258002, 2.7872699, 0.0000000, 7.7551943,…\n$ Prog_084 <dbl> 0.000000, 0.000000, 9.331667, 0.000000, 1.701931, 1.08…\n$ Prog_085 <dbl> 0.0000000, 0.6491118, 9.3288292, 0.0000000, 5.1330763,…\n$ Prog_087 <dbl> 0.000000, 0.000000, 1.104019, 0.000000, 5.052535, 2.15…\n$ Prog_088 <dbl> 0.000000, 0.000000, 3.564043, 0.000000, 3.038816, 0.00…\n$ Prog_089 <dbl> 0.000000, 0.000000, 10.085311, 0.000000, 4.543583, 0.0…\n$ Prog_090 <dbl> 0.0000000, 0.0000000, 9.2374975, 0.0000000, 1.2838093,…\n$ Prog_091 <dbl> 0.4305087, 0.7616406, 10.0688175, 0.0000000, 8.2019581…\n$ Prog_092 <dbl> 0.0000000, 0.9254699, 4.0257506, 0.0000000, 1.8867094,…\n$ Prog_093 <dbl> 0.9251997, 0.0000000, 6.7901406, 0.0000000, 9.5469428,…\n$ Prog_094 <dbl> 0.000000, 1.210416, 9.726634, 0.000000, 2.645281, 0.00…\n$ Prog_095 <dbl> 0.0000000, 0.0000000, 10.3666061, 0.0000000, 9.1749393…\n$ Prog_096 <dbl> 0.000000, 1.515954, 3.603985, 0.000000, 3.364487, 0.00…\n$ Prog_097 <dbl> 0.0000000, 0.0000000, 3.3142351, 0.9957312, 1.5792680,…\n$ Prog_098 <dbl> 0.0000000, 0.0000000, 10.9079028, 0.0000000, 1.6489683…\n$ Prog_099 <dbl> 0.0000000, 0.0000000, 9.4733082, 0.0000000, 9.2852571,…\n$ Prog_100 <dbl> 0.0000000, 0.0000000, 10.0645065, 0.0000000, 0.8335261…\n$ Prog_101 <dbl> 0.0000000, 0.0000000, 3.0554930, 0.0000000, 1.2201851,…\n$ Prog_102 <dbl> 0.000000, 0.000000, 10.314337, 0.000000, 8.135122, 0.0…\n$ Prog_103 <dbl> 0.000000, 0.000000, 2.694305, 0.000000, 2.427053, 0.00…\n$ Prog_105 <dbl> 0.000000, 0.000000, 1.715023, 0.000000, 8.272431, 0.00…\n$ Prog_106 <dbl> 0.000000, 0.000000, 2.897396, 0.000000, 9.269248, 0.00…\n$ Prog_107 <dbl> 0.0000000, 0.0000000, 11.2438039, 0.8959356, 8.2190795…\n$ Prog_108 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.187893, 0.00…\n$ Prog_109 <dbl> 0.000000, 0.000000, 2.847758, 0.000000, 1.798890, 0.00…\n$ Prog_110 <dbl> 0.0000000, 0.0000000, 7.0447094, 0.0000000, 1.1498271,…\n$ Prog_111 <dbl> 0.000000, 1.347991, 9.989313, 0.000000, 2.844445, 0.00…\n$ Prog_112 <dbl> 0.0000000, 0.0000000, 9.7736274, 0.0000000, 0.0000000,…\n$ Prog_113 <dbl> 0.000000, 0.000000, 3.375645, 0.000000, 4.805584, 0.00…\n$ Prog_114 <dbl> 0.000000, 0.000000, 5.270999, 0.000000, 9.142589, 0.00…\n$ Prog_115 <dbl> 0.000000, 0.000000, 9.198819, 0.000000, 7.984094, 0.00…\n$ Prog_116 <dbl> 0.0000000, 0.8762674, 3.6666065, 0.0000000, 6.3585217,…\n$ Prog_117 <dbl> 0.0000000, 0.0000000, 1.5843370, 0.0000000, 4.3914238,…\n$ Prog_118 <dbl> 0.000000, 2.358196, 3.210167, 0.000000, 2.358196, 1.61…\n$ Prog_119 <dbl> 0.0000000, 0.0000000, 9.5596469, 0.0000000, 5.0827697,…\n$ Prog_120 <dbl> 0.000000, 0.000000, 9.797611, 1.233285, 6.208574, 1.23…\n$ Prog_122 <dbl> 0.0000000, 0.0000000, 10.1430231, 0.0000000, 1.5290744…\n$ Prog_123 <dbl> 0.972353, 3.271775, 1.547981, 0.000000, 0.972353, 0.97…\n$ Prog_124 <dbl> 0.000000, 1.866390, 1.866390, 0.000000, 7.948926, 1.21…\n$ Prog_125 <dbl> 0.000000, 0.000000, 10.339672, 0.000000, 1.886583, 0.0…\n$ Prog_126 <dbl> 0.0000000, 0.9244523, 6.9333965, 0.0000000, 6.5045625,…\n$ Prog_127 <dbl> 0.0000000, 0.0000000, 1.8946766, 0.9306455, 2.8763501,…\n$ Prog_128 <dbl> 0.0000000, 0.0000000, 0.9281856, 0.0000000, 7.8305039,…\n$ Prog_129 <dbl> 0.0000000, 0.4157242, 0.0000000, 0.0000000, 8.6962250,…\n$ Prog_130 <dbl> 0.0000000, 0.0000000, 0.7728911, 0.0000000, 2.1837890,…\n$ Prog_131 <dbl> 0.000000, 1.024302, 0.000000, 0.000000, 2.360617, 1.61…\n$ Prog_132 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.5667490,…\n$ Prog_133 <dbl> 0.000000, 0.801043, 1.988936, 0.000000, 8.275295, 2.44…\n$ Prog_134 <dbl> 0.000000, 1.945025, 4.178130, 0.000000, 2.744352, 1.94…\n$ Prog_135 <dbl> 0.0000000, 0.0000000, 0.9244625, 8.3087390, 1.4833406,…\n$ Prog_136 <dbl> 0.0000000, 1.3808535, 0.6179955, 0.6179955, 7.6121006,…\n$ Prog_137 <dbl> 0.0000000, 0.7394643, 0.7394643, 0.7394643, 7.8827291,…\n$ Prog_138 <dbl> 0.000000, 1.563600, 0.000000, 0.000000, 3.430222, 0.00…\n$ Prog_139 <dbl> 0.0000000, 1.7812055, 8.3267743, 0.0000000, 1.3921828,…\n$ Prog_140 <dbl> 0.0000000, 0.7781468, 9.9514318, 0.0000000, 0.7781468,…\n$ Prog_141 <dbl> 0.0000000, 5.7825012, 0.6970532, 0.0000000, 1.5178150,…\n$ Prog_142 <dbl> 0.000000, 0.000000, 3.511726, 1.314161, 6.116501, 1.31…\n$ Prog_143 <dbl> 0.0000000, 0.5895431, 1.5941091, 0.0000000, 2.1805905,…\n$ Prog_144 <dbl> 0.0000000, 1.7299786, 3.2473969, 0.0000000, 7.0136857,…\n$ Prog_145 <dbl> 0.0000000, 0.8803065, 2.1253422, 0.0000000, 3.8532234,…\n$ Prog_146 <dbl> 0.0000000, 0.0000000, 9.6048304, 0.0000000, 6.0801391,…\n$ Prog_149 <dbl> 0.000000, 6.728263, 1.219978, 0.000000, 5.097434, 0.00…\n$ Prog_151 <dbl> 0.000000, 0.000000, 1.135191, 0.000000, 8.548053, 1.76…\n$ Prog_152 <dbl> 0.000000, 0.000000, 2.509964, 0.000000, 1.743312, 0.00…\n$ Prog_153 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0767785,…\n$ Prog_154 <dbl> 0.000000, 0.000000, 1.780367, 0.000000, 3.579917, 1.14…\n$ Prog_155 <dbl> 0.000000, 1.593623, 0.000000, 0.000000, 8.767828, 0.00…\n$ Prog_156 <dbl> 0.000000, 0.000000, 2.135119, 0.000000, 7.938267, 0.00…\n$ Prog_157 <dbl> 0.000000, 0.000000, 2.867544, 0.000000, 2.677727, 0.00…\n$ Prog_158 <dbl> 0.0000000, 0.0000000, 8.8872134, 0.0000000, 2.0919047,…\n$ Prog_159 <dbl> 0.000000, 1.188374, 0.000000, 1.188374, 2.886508, 0.00…\n$ Prog_160 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 10.974767, 3.1…\n$ Prog_161 <dbl> 0.0000000, 0.0000000, 8.1861205, 0.0000000, 6.4392695,…\n$ Prog_162 <dbl> 0.0000000, 0.0000000, 2.2097857, 0.0000000, 7.8073744,…\n$ Prog_163 <dbl> 0.0000000, 0.3830123, 10.3455921, 0.0000000, 7.7155815…\n$ Prog_165 <dbl> 0.000000, 1.540371, 0.000000, 0.000000, 3.110158, 1.54…\n$ Prog_166 <dbl> 0.000000, 0.000000, 9.372041, 0.000000, 5.466508, 0.00…\n$ Prog_167 <dbl> 0.0000000, 0.0000000, 1.0825470, 0.0000000, 1.0825470,…\n$ Prog_168 <dbl> 0.000000, 0.000000, 7.898674, 0.000000, 8.481620, 0.00…\n$ Prog_170 <dbl> 0.0000000, 0.0000000, 9.5291517, 0.0000000, 6.8814286,…\n$ Prog_171 <dbl> 0.000000, 1.290529, 1.960644, 0.000000, 8.107341, 0.00…\n$ Prog_172 <dbl> 0.000000, 1.916108, 10.409685, 0.000000, 6.246008, 1.2…\n$ Prog_173 <dbl> 1.421440, 0.000000, 3.672391, 0.000000, 5.367534, 0.00…\n$ Prog_176 <dbl> 0.0000000, 0.0000000, 1.6763286, 0.0000000, 1.4044136,…\n$ Prog_177 <dbl> 0.0000000, 0.8985254, 7.2418761, 0.0000000, 6.8134809,…\n$ Prog_178 <dbl> 0.0000000, 0.0000000, 10.1816714, 0.9261866, 0.9261866…\n$ Prog_179 <dbl> 0.3678341, 0.6606806, 1.7326221, 0.0000000, 0.6606806,…\n$ Prog_180 <dbl> 0.0000000, 1.2775281, 1.2775281, 0.0000000, 8.3848718,…\n$ Prog_182 <dbl> 0.000000, 0.000000, 8.323335, 1.692852, 1.692852, 0.00…\n$ Prog_183 <dbl> 0.000000, 0.000000, 1.720696, 0.000000, 5.479719, 1.10…\n$ Prog_184 <dbl> 0.0000000, 0.0000000, 2.6959891, 0.0000000, 8.4827944,…\n$ Prog_185 <dbl> 0.0000000, 0.2978388, 10.6532638, 0.0000000, 6.0319626…\n$ Prog_186 <dbl> 4.7784217, 0.7180674, 2.8971958, 0.0000000, 3.7969651,…\n$ Prog_187 <dbl> 0.000000, 1.124236, 0.000000, 1.124236, 1.124236, 1.12…\n$ Prog_188 <dbl> 0.000000, 0.000000, 9.905256, 0.000000, 7.068250, 0.00…\n$ Prog_189 <dbl> 0.0000000, 0.0000000, 1.7117591, 0.0000000, 1.7117591,…\n$ Prog_190 <dbl> 0.000000, 0.000000, 1.704477, 0.000000, 6.713132, 0.00…\n$ Prog_191 <dbl> 0.000000, 2.200713, 1.484670, 0.000000, 0.000000, 0.00…\n$ Prog_193 <dbl> 0.000000, 1.727872, 3.357490, 0.000000, 1.727872, 2.49…\n$ Prog_194 <dbl> 0.000000, 2.695708, 9.532603, 0.000000, 9.350867, 0.00…\n$ Prog_195 <dbl> 0.000000, 0.000000, 3.086892, 0.000000, 8.875103, 0.00…\n$ Prog_196 <dbl> 0.0000000, 0.5107742, 10.3705485, 0.0000000, 6.0443204…\n$ Prog_197 <dbl> 0.7982166, 1.3091182, 3.0685852, 0.0000000, 8.4503226,…\n$ Prog_199 <dbl> 0.0000000, 1.8201009, 0.0000000, 0.0000000, 0.0000000,…\n$ Prog_200 <dbl> 0.000000, 0.000000, 8.033544, 0.000000, 9.090910, 0.00…\n$ Prog_201 <dbl> 0.000000, 1.373088, 1.373088, 0.000000, 5.606067, 0.00…\n$ Prog_203 <dbl> 6.8032081, 0.0000000, 2.1723014, 0.0000000, 3.2590750,…\n$ Prog_204 <dbl> 0.9657368, 0.0000000, 0.0000000, 0.0000000, 8.3423596,…\n$ Prog_205 <dbl> 0.000000, 1.909471, 1.909471, 0.000000, 1.909471, 0.00…\n$ Prog_206 <dbl> 0.0000000, 0.0000000, 1.9393051, 0.0000000, 5.2425154,…\n$ Prog_207 <dbl> 0.000000, 0.000000, 9.685052, 0.000000, 2.032410, 0.00…\n$ Prog_208 <dbl> 0.7583745, 0.0000000, 2.5462366, 0.0000000, 2.8528484,…\n$ Prog_209 <dbl> 0.0000000, 0.3645471, 0.6553104, 0.0000000, 1.7223870,…\n$ Prog_210 <dbl> 0.0000000, 0.3252499, 0.8144636, 0.0000000, 7.3338424,…\n$ Prog_211 <dbl> 0.0000000, 1.3954137, 9.7650888, 0.0000000, 7.5460682,…\n$ Prog_212 <dbl> 0.000000, 0.000000, 9.604897, 0.000000, 1.163710, 0.00…\n$ Prog_213 <dbl> 0.000000, 1.214833, 7.656877, 0.000000, 8.100536, 1.21…\n$ Prog_214 <dbl> 0.000000, 1.388857, 2.902043, 0.000000, 2.083186, 0.00…\n$ Prog_215 <dbl> 0.0000000, 0.5263012, 9.3835995, 0.0000000, 0.0000000,…\n$ Prog_216 <dbl> 0.0000000, 0.5587277, 7.1964757, 0.0000000, 0.9604667,…\n$ Prog_217 <dbl> 0.5419335, 0.9349655, 9.8528399, 0.0000000, 5.3294585,…\n$ Prog_218 <dbl> 0.0000000, 0.7082775, 9.7867961, 0.0000000, 4.9738365,…\n$ Prog_221 <dbl> 0.0000000, 0.0000000, 8.9092814, 0.0000000, 1.4786872,…\n$ Prog_222 <dbl> 0.0000000, 0.4289669, 2.0413911, 0.0000000, 8.6509789,…\n$ Prog_223 <dbl> 0.000000, 0.000000, 3.372517, 0.000000, 9.791425, 0.00…\n$ Prog_224 <dbl> 0.0000000, 1.0463276, 1.3773453, 0.0000000, 7.6529883,…\n$ Prog_225 <dbl> 0.0000000, 0.0000000, 9.3874302, 0.0000000, 2.7216013,…\n$ Prog_226 <dbl> 0.000000, 0.000000, 1.888170, 0.000000, 2.954966, 0.00…\n$ Prog_227 <dbl> 0.0000000, 0.8370335, 1.7480832, 0.0000000, 2.9418899,…\n$ Prog_228 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.4718062,…\n$ Prog_229 <dbl> 0.0000000, 0.7312399, 10.8082218, 0.0000000, 1.5754071…\n$ Prog_230 <dbl> 0.000000, 1.860455, 1.211393, 0.000000, 8.465908, 0.00…\n$ Prog_231 <dbl> 0.0000000, 0.7685650, 7.1611020, 0.0000000, 8.4669639,…\n$ Prog_232 <dbl> 1.095818, 0.000000, 2.472324, 0.000000, 1.095818, 1.09…\n$ Prog_233 <dbl> 0.0000000, 1.8274369, 2.7965576, 0.0000000, 9.0691873,…\n$ Prog_234 <dbl> 0.0000000, 0.7511543, 0.7511543, 0.0000000, 1.9001638,…\n$ Prog_235 <dbl> 0.000000, 0.000000, 6.015426, 0.000000, 9.502735, 0.00…\n$ Prog_236 <dbl> 0.0000000, 0.0000000, 2.0857948, 0.0000000, 8.4672119,…\n$ Prog_237 <dbl> 0.000000, 0.000000, 4.187354, 0.000000, 7.742022, 1.65…\n$ Prog_238 <dbl> 0.000000, 0.000000, 3.507252, 0.000000, 1.026497, 7.06…\n$ Prog_239 <dbl> 0.3303969, 0.0000000, 10.2051547, 0.3303969, 0.5990497…\n$ Prog_240 <dbl> 0.000000, 1.379989, 10.514349, 0.000000, 5.180252, 0.0…\n$ Prog_241 <dbl> 0.000000, 0.000000, 3.848112, 0.000000, 1.879796, 0.00…\n$ Prog_242 <dbl> 0.000000, 0.000000, 8.952912, 0.000000, 4.535018, 0.00…\n$ Prog_243 <dbl> 0.0000000, 0.5890774, 2.0092422, 0.0000000, 1.8161583,…\n$ Prog_244 <dbl> 0.000000, 0.000000, 1.124031, 0.000000, 0.000000, 0.00…\n$ Prog_245 <dbl> 0.0000000, 0.0000000, 2.2721650, 0.0000000, 5.5768786,…\n$ Prog_246 <dbl> 0.977575, 0.000000, 1.554984, 0.000000, 6.184227, 0.97…\n$ Prog_247 <dbl> 0.000000, 0.000000, 10.272485, 0.000000, 4.860599, 0.0…\n$ Prog_248 <dbl> 0.000000, 4.449338, 2.450784, 0.000000, 6.978192, 0.00…\n$ Prog_249 <dbl> 0.0000000, 0.6239062, 9.8966865, 0.0000000, 1.6618262,…\n$ Prog_250 <dbl> 0.000000, 0.000000, 9.480626, 0.000000, 1.228688, 1.22…\n$ Prog_252 <dbl> 0.0000000, 0.7256255, 0.0000000, 0.0000000, 7.8522069,…\n$ Prog_253 <dbl> 0.0000000, 1.8381001, 8.4528360, 0.0000000, 1.8381001,…\n$ Prog_254 <dbl> 0.0000000, 2.2350374, 8.0150724, 0.0000000, 1.5129219,…\n$ Prog_255 <dbl> 0.0000000, 0.0000000, 2.0614306, 0.0000000, 7.0863486,…\n$ Prog_256 <dbl> 0.000000, 0.000000, 8.290864, 0.000000, 9.221942, 0.00…\n$ Prog_258 <dbl> 0.5102471, 0.0000000, 0.8864240, 0.0000000, 0.8864240,…\n$ Prog_259 <dbl> 0.000000, 0.000000, 2.225233, 0.000000, 4.527019, 8.20…\n$ Prog_260 <dbl> 2.727131, 1.086971, 1.699796, 0.000000, 1.699796, 0.00…\n$ Prog_261 <dbl> 0.000000, 1.053649, 9.254943, 0.000000, 6.937373, 2.07…\n$ Prog_262 <dbl> 3.2411394, 0.0000000, 1.8648554, 0.0000000, 4.2851211,…\n$ Prog_263 <dbl> 0.4551799, 0.8006681, 0.8006681, 0.0000000, 1.3125578,…\n$ Prog_264 <dbl> 0.0000000, 0.5042469, 0.8771674, 0.0000000, 7.5675045,…\n$ Prog_265 <dbl> 0.7833943, 0.0000000, 10.3417120, 0.0000000, 9.1141111…\n$ Prog_266 <dbl> 0.000000, 1.009002, 1.596953, 0.000000, 4.948100, 1.00…\n$ Prog_267 <dbl> 0.0000000, 0.7969509, 0.4528196, 0.4528196, 8.7938434,…\n$ Prog_268 <dbl> 0.000000, 0.000000, 2.972219, 0.000000, 1.714783, 0.00…\n$ Prog_269 <dbl> 0.0000000, 0.8457948, 9.4133220, 0.0000000, 1.9223467,…\n$ Prog_270 <dbl> 0.0000000, 0.0000000, 2.5432898, 0.7569217, 7.4725653,…\n$ Prog_271 <dbl> 0.000000, 0.000000, 3.311082, 0.000000, 5.664642, 1.82…\n$ Prog_272 <dbl> 0.000000, 1.145846, 10.205810, 0.000000, 2.213588, 1.1…\n$ Prog_273 <dbl> 0.0000000, 0.0000000, 1.8298483, 0.0000000, 1.1874501,…\n$ Prog_274 <dbl> 0.000000, 0.000000, 10.553163, 0.000000, 3.759580, 1.8…\n$ Prog_275 <dbl> 0.000000, 1.998751, 9.093735, 0.000000, 1.320929, 0.00…\n$ Prog_276 <dbl> 0.0000000, 0.6816236, 10.1768619, 0.0000000, 5.7426292…\n$ Prog_277 <dbl> 0.000000, 0.000000, 3.397199, 0.000000, 2.194039, 1.75…\n$ Prog_278 <dbl> 0.000000, 0.000000, 10.520882, 0.000000, 1.782549, 0.8…\n$ Prog_279 <dbl> 0.0000000, 0.0000000, 9.7750743, 0.0000000, 6.4866546,…\n$ Prog_280 <dbl> 0.000000, 0.000000, 3.801777, 0.000000, 7.830068, 2.08…\n$ Prog_281 <dbl> 0.000000, 2.991784, 8.743845, 0.000000, 6.568134, 1.45…\n$ Prog_282 <dbl> 0.0000000, 0.7748444, 10.1974123, 0.0000000, 7.7812922…\n$ Prog_283 <dbl> 0.000000, 0.000000, 4.121837, 0.000000, 3.051945, 1.49…\n$ Prog_284 <dbl> 0.000000, 0.000000, 10.038043, 0.000000, 7.876859, 1.2…\n$ Prog_285 <dbl> 0.0000000, 0.0000000, 10.3972698, 0.0000000, 2.6015592…\n$ Prog_286 <dbl> 0.0000000, 0.7853987, 10.9267969, 0.0000000, 7.6013459…\n$ Prog_287 <dbl> 0.0000000, 0.7030116, 2.7335919, 0.0000000, 8.2038714,…\n$ Prog_288 <dbl> 0.0000000, 0.9565732, 7.1791755, 0.0000000, 3.2427933,…\n$ Prog_289 <dbl> 0.000000, 1.765305, 1.765305, 0.000000, 7.537283, 0.00…\n$ Prog_290 <dbl> 0.0000000, 0.7598427, 9.0962542, 0.0000000, 6.8064394,…\n$ Prog_291 <dbl> 0.000000, 0.000000, 9.960959, 0.000000, 8.272307, 0.00…\n$ Prog_292 <dbl> 0.0000000, 0.0000000, 8.2527252, 0.8850525, 2.3879578,…\n$ Prog_293 <dbl> 0.0000000, 1.4666332, 1.4666332, 3.7383714, 2.6533547,…\n$ Prog_294 <dbl> 0.0000000, 0.0000000, 8.2472025, 0.0000000, 1.4465873,…\n$ Prog_295 <dbl> 0.000000, 1.391683, 3.188912, 0.000000, 1.391683, 0.00…\n$ Prog_296 <dbl> 0.0000000, 1.2877903, 2.6520596, 0.0000000, 3.2704137,…\n$ Prog_297 <dbl> 0.000000, 0.000000, 2.650275, 0.000000, 6.416939, 1.46…\n$ Prog_298 <dbl> 0.0000000, 0.0000000, 8.9270709, 0.0000000, 8.1595880,…\n$ Prog_299 <dbl> 0.0000000, 0.0000000, 9.5977407, 0.0000000, 3.8037900,…\n$ Prog_300 <dbl> 0.0000000, 0.0000000, 1.6011895, 0.0000000, 1.0670845,…\n$ Prog_301 <dbl> 0.000000, 1.072566, 6.161947, 0.000000, 6.329631, 0.00…\n$ Prog_303 <dbl> 0.000000, 0.000000, 5.016012, 0.000000, 4.736380, 2.74…\n$ Prog_304 <dbl> 0.0000000, 0.0000000, 2.6952110, 0.0000000, 9.0901058,…\n$ Prog_305 <dbl> 0.000000, 1.050712, 3.411964, 0.000000, 3.900159, 0.00…\n$ Prog_306 <dbl> 0.0000000, 0.0000000, 9.2865102, 0.0000000, 6.6183087,…\n$ Prog_307 <dbl> 1.203107, 6.546382, 3.338631, 0.000000, 7.508005, 1.84…\n$ Prog_308 <dbl> 0.0000000, 0.0000000, 3.4805338, 0.0000000, 1.9151292,…\n$ Prog_309 <dbl> 0.000000, 2.723041, 1.926499, 0.000000, 9.004616, 0.00…\n$ Prog_310 <dbl> 0.000000, 0.000000, 3.472773, 1.594758, 6.122982, 0.00…\n$ Prog_311 <dbl> 0.000000, 7.237647, 4.058607, 1.694935, 1.694935, 0.00…\n$ Prog_312 <dbl> 0.0000000, 0.3194344, 7.9489885, 0.0000000, 6.8424653,…\n$ Prog_313 <dbl> 0.000000, 1.263594, 2.379287, 0.000000, 6.233608, 1.26…\n$ Prog_314 <dbl> 0.0000000, 0.0000000, 2.6506885, 0.0000000, 0.9106903,…\n$ Prog_315 <dbl> 1.578629, 0.000000, 9.916794, 0.000000, 2.314324, 0.00…\n$ Prog_316 <dbl> 0.000000, 0.000000, 10.114795, 0.000000, 2.811615, 0.0…\n$ Prog_318 <dbl> 0.0000000, 0.9288238, 10.0756689, 0.0000000, 8.9040916…\n$ Prog_319 <dbl> 0.0000000, 0.8930483, 1.4405226, 0.0000000, 7.9507895,…\n$ Prog_320 <dbl> 0.0000000, 0.0000000, 1.8190653, 0.0000000, 6.2758989,…\n$ Prog_321 <dbl> 0.000000, 0.000000, 9.297222, 0.000000, 1.833420, 0.00…\n$ Prog_322 <dbl> 0.000000, 2.443010, 9.816765, 0.000000, 4.047200, 2.44…\n$ Prog_323 <dbl> 0.000000, 0.000000, 7.704855, 0.000000, 3.419902, 1.55…\n$ Prog_324 <dbl> 0.000000, 0.496873, 3.205241, 0.000000, 2.465365, 0.86…\n$ Prog_325 <dbl> 0.0000000, 0.0000000, 3.2416838, 0.0000000, 1.6391134,…\n$ Prog_326 <dbl> 0.4088145, 0.0000000, 10.4688185, 0.0000000, 3.6773280…\n$ Prog_327 <dbl> 7.3751525, 0.9125905, 2.8433724, 0.0000000, 6.5628039,…\n$ Prog_328 <dbl> 1.304601, 1.304601, 8.871933, 0.000000, 2.435752, 0.00…\n$ Prog_329 <dbl> 0.000000, 2.325922, 9.373489, 0.000000, 2.325922, 0.00…\n$ Prog_330 <dbl> 0.0000000, 0.0000000, 2.2816003, 0.0000000, 0.8013449,…\n$ Prog_331 <dbl> 0.0000000, 1.1695764, 1.8069067, 0.0000000, 1.1695764,…\n$ Prog_332 <dbl> 1.4707132, 0.9151723, 3.3012867, 0.0000000, 5.2217830,…\n$ Prog_333 <dbl> 0.000000, 2.910316, 7.536611, 0.000000, 5.474183, 2.41…\n$ Prog_334 <dbl> 0.000000, 1.214493, 8.796749, 1.214493, 3.956661, 0.00…\n$ Prog_335 <dbl> 0.000000, 1.261316, 2.997422, 3.430286, 6.333373, 0.00…\n$ Prog_336 <dbl> 0.0000000, 0.0000000, 2.9306788, 0.0000000, 2.1084727,…\n$ Prog_337 <dbl> 0.0000000, 0.0000000, 9.6699736, 0.7842944, 7.7515990,…\n$ Prog_338 <dbl> 0.000000, 1.756498, 1.756498, 0.842314, 2.311683, 1.37…\n$ Prog_339 <dbl> 0.0000000, 0.0000000, 10.3268110, 0.0000000, 2.2183583…\n$ Prog_340 <dbl> 0.000000, 0.000000, 7.859406, 0.000000, 9.505666, 0.00…\n$ Prog_341 <dbl> 0.0000000, 0.0000000, 3.2100356, 1.9073694, 8.5229056,…\n$ Prog_342 <dbl> 0.0000000, 0.0000000, 8.9705036, 0.6908909, 6.6423997,…\n$ Prog_343 <dbl> 0.0000000, 0.0000000, 8.2891140, 0.6450933, 8.2707946,…\n$ Prog_345 <dbl> 0.000000, 0.000000, 9.479736, 0.000000, 1.696915, 1.69…\n$ Prog_346 <dbl> 0.9342914, 0.9342914, 9.3271260, 1.4966687, 7.8375519,…\n$ Prog_348 <dbl> 0.000000, 1.340304, 2.833535, 0.000000, 2.484432, 1.34…\n$ Prog_349 <dbl> 0.000000, 2.025731, 9.389506, 0.000000, 2.836725, 0.00…\n$ Prog_350 <dbl> 0.000000, 0.000000, 9.316761, 0.000000, 3.756149, 0.00…\n$ Prog_351 <dbl> 0.000000, 0.000000, 10.000866, 0.000000, 1.448414, 0.0…\n$ Prog_352 <dbl> 0.0000000, 0.0000000, 10.4731912, 0.0000000, 7.7402569…\n$ Prog_353 <dbl> 0.0000000, 1.4503390, 2.1588040, 0.0000000, 8.7350300,…\n$ Prog_354 <dbl> 0.000000, 0.000000, 9.385151, 0.000000, 8.239107, 0.76…\n$ Prog_355 <dbl> 0.000000, 0.000000, 1.917469, 0.000000, 9.046425, 0.00…\n$ Prog_356 <dbl> 0.0000000, 0.0000000, 8.7251015, 0.0000000, 1.3680251,…\n$ Prog_357 <dbl> 0.0000000, 0.0000000, 10.1597396, 0.0000000, 5.0216000…\n$ Prog_358 <dbl> 0.000000, 0.000000, 3.652693, 0.000000, 0.000000, 0.00…\n$ Prog_359 <dbl> 0.000000, 0.000000, 8.515427, 0.000000, 1.047573, 0.00…\n$ Prog_360 <dbl> 0.0000000, 0.0000000, 9.4810351, 0.0000000, 4.4471795,…\n$ Prog_361 <dbl> 0.000000, 0.000000, 9.887200, 0.000000, 1.666612, 0.00…\n$ Prog_362 <dbl> 0.000000, 0.000000, 3.585966, 0.000000, 3.585966, 0.00…\n$ Prog_363 <dbl> 0.000000, 0.000000, 10.432505, 0.000000, 1.237491, 1.2…\n$ Prog_364 <dbl> 0.0000000, 0.0000000, 9.8163606, 0.6419434, 4.4787343,…\n$ Prog_366 <dbl> 0.0000000, 0.0000000, 10.0814799, 0.8456917, 8.5501780…\n$ Prog_367 <dbl> 0.0000000, 0.0000000, 0.9017145, 0.0000000, 2.0139972,…\n$ Prog_368 <dbl> 0.000000, 1.337730, 9.782767, 0.000000, 9.129481, 0.00…\n$ Prog_369 <dbl> 0.000000, 0.000000, 10.397370, 0.000000, 4.858203, 0.0…\n$ Prog_370 <dbl> 0.000000, 0.000000, 8.153273, 0.000000, 3.850934, 0.00…\n$ Prog_371 <dbl> 0.814282, 0.814282, 2.968239, 0.000000, 8.376910, 7.17…\n$ Prog_372 <dbl> 0.0000000, 0.0000000, 9.1458575, 0.0000000, 3.1131982,…\n$ Prog_373 <dbl> 0.000000, 1.040556, 8.096510, 0.000000, 6.546729, 0.61…\n$ Prog_374 <dbl> 0.000000, 1.181850, 8.730428, 0.000000, 4.575080, 0.00…\n$ Prog_375 <dbl> 0.8199375, 0.8199375, 9.2367661, 0.0000000, 3.8851772,…\n$ Prog_376 <dbl> 0.0000000, 0.0000000, 10.9071546, 0.0000000, 1.9697298…\n$ Prog_377 <dbl> 0.4409935, 0.7782740, 10.7136336, 0.0000000, 5.8180971…\n$ Prog_378 <dbl> 0.000000, 0.000000, 3.454142, 0.000000, 8.770626, 1.16…\n$ Prog_379 <dbl> 0.0000000, 0.0000000, 8.9052981, 0.0000000, 1.8557362,…\n$ Prog_380 <dbl> 0.0000000, 0.0000000, 8.8754228, 0.0000000, 0.0000000,…\n$ Prog_381 <dbl> 0.0000000, 1.4949361, 10.6523772, 0.0000000, 7.4634170…\n$ Prog_382 <dbl> 0.000000, 0.000000, 3.895517, 0.000000, 2.633624, 0.00…\n$ Prog_383 <dbl> 0.0000000, 0.0000000, 10.3584797, 0.0000000, 1.5530176…\n$ Prog_384 <dbl> 0.000000, 0.000000, 8.168259, 0.000000, 6.937475, 0.00…\n$ Prog_385 <dbl> 0.0000000, 0.5725933, 8.4545280, 0.0000000, 6.5175617,…\n$ Prog_386 <dbl> 0.000000, 0.000000, 10.021964, 0.000000, 1.301204, 0.5…\n$ Prog_387 <dbl> 0.0000000, 0.0000000, 1.7618175, 0.0000000, 1.4820418,…\n$ Prog_388 <dbl> 0.0000000, 0.0000000, 10.4140086, 0.0000000, 8.4288756…\n$ Prog_389 <dbl> 0.0000000, 0.8721416, 9.4378255, 0.0000000, 9.4987074,…\n$ Prog_390 <dbl> 0.000000, 0.000000, 9.182987, 0.000000, 2.512135, 0.00…\n$ Prog_392 <dbl> 0.0000000, 0.0000000, 2.4781231, 0.0000000, 0.9368503,…\n$ Prog_393 <dbl> 0.0000000, 0.9095146, 10.5410103, 0.0000000, 0.9095146…\n$ Prog_394 <dbl> 0.000000, 0.000000, 8.680072, 0.000000, 2.259602, 0.00…\n$ Prog_395 <dbl> 0.0000000, 0.0000000, 8.5766999, 0.8694313, 8.5144444,…\n$ Prog_396 <dbl> 0.000000, 0.000000, 2.829717, 0.000000, 2.342810, 0.00…\n$ Prog_397 <dbl> 0.000000, 0.000000, 11.053751, 0.000000, 8.301462, 1.0…\n$ Prog_398 <dbl> 0.0000000, 0.0000000, 9.9231664, 0.0000000, 1.7608152,…\n$ Prog_399 <dbl> 0.000000, 0.000000, 1.747110, 1.123287, 1.123287, 0.00…\n$ Prog_401 <dbl> 0.0000000, 0.0000000, 10.2838777, 0.0000000, 1.3667061…\n$ Prog_402 <dbl> 0.000000, 0.000000, 9.474438, 0.000000, 8.566503, 2.37…\n$ Prog_403 <dbl> 0.000000, 1.003743, 10.036757, 0.000000, 8.911343, 0.0…\n$ Prog_404 <dbl> 0.0000000, 0.0000000, 8.5957521, 0.0000000, 9.0399316,…\n$ Prog_405 <dbl> 0.000000, 0.000000, 9.323862, 0.000000, 3.182537, 1.38…\n$ Prog_406 <dbl> 0.000000, 1.015963, 10.219077, 0.000000, 8.682725, 0.0…\n$ Prog_407 <dbl> 0.0000000, 0.0000000, 3.4247185, 0.0000000, 2.7746481,…\n$ Prog_408 <dbl> 0.8524009, 0.0000000, 1.3846059, 0.0000000, 7.2289926,…\n$ Prog_409 <dbl> 0.0000000, 0.0000000, 10.4395270, 0.0000000, 5.5552806…\n$ Prog_410 <dbl> 0.0000000, 0.0000000, 7.8292885, 0.0000000, 2.9892172,…\n$ Prog_411 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.548764, 0.00…\n$ Prog_412 <dbl> 0.0000000, 0.0000000, 0.9874232, 0.0000000, 0.0000000,…\n$ Prog_413 <dbl> 0.0000000, 0.8578666, 2.5543770, 5.7925396, 2.0872676,…\n$ Prog_414 <dbl> 0.0000000, 0.0000000, 1.7153789, 0.0000000, 7.8320059,…\n$ Prog_415 <dbl> 0.0000000, 0.0000000, 10.2240962, 0.0000000, 1.8398668…\n$ Prog_416 <dbl> 0.000000, 0.914352, 9.241424, 0.000000, 9.037932, 0.91…\n$ Prog_417 <dbl> 0.000000, 1.650501, 9.313812, 0.000000, 0.000000, 1.65…\n$ Prog_418 <dbl> 0.000000, 0.000000, 10.392544, 0.000000, 0.000000, 0.0…\n$ Prog_419 <dbl> 0.0000000, 0.0000000, 9.9305306, 0.0000000, 1.0363712,…\n$ Prog_420 <dbl> 0.000000, 1.108997, 2.762303, 0.000000, 6.511636, 0.00…\n$ Prog_421 <dbl> 0.0000000, 0.6017629, 10.2259563, 0.0000000, 6.3890708…\n$ Prog_422 <dbl> 0.000000, 0.000000, 2.362098, 0.000000, 8.105137, 0.00…\n$ Prog_423 <dbl> 1.444615, 1.444615, 3.885107, 0.000000, 2.151795, 1.44…\n$ Prog_424 <dbl> 0.0000000, 0.8952933, 10.7817986, 0.0000000, 7.0600274…\n$ Prog_425 <dbl> 0.000000, 0.000000, 9.254046, 0.000000, 6.323844, 1.35…\n$ Prog_426 <dbl> 0.000000, 0.000000, 1.897181, 0.000000, 1.897181, 0.00…\n$ Prog_427 <dbl> 1.778952, 0.000000, 3.717961, 0.000000, 8.839747, 1.77…\n$ Prog_428 <dbl> 0.0000000, 0.0000000, 2.7453877, 4.6697636, 4.7152397,…\n$ Prog_429 <dbl> 0.0000000, 0.3320376, 9.7967309, 0.0000000, 1.0251422,…\n$ Prog_430 <dbl> 0.0000000, 0.0000000, 10.4368277, 0.0000000, 1.9727890…\n$ Prog_431 <dbl> 0.0000000, 0.9806429, 3.1353307, 0.0000000, 2.7740105,…\n$ Prog_432 <dbl> 0.000000, 0.000000, 10.161765, 0.000000, 4.439729, 1.9…\n$ Prog_433 <dbl> 0.000000, 0.000000, 2.941087, 0.000000, 7.272747, 0.00…\n$ Prog_434 <dbl> 0.4851222, 0.4851222, 10.0887820, 0.0000000, 1.1369218…\n$ Prog_435 <dbl> 0.000000, 1.371249, 7.739109, 0.000000, 7.862289, 0.00…\n$ Prog_436 <dbl> 0.0000000, 0.7633949, 1.9221680, 0.0000000, 6.2275278,…\n$ Prog_437 <dbl> 0.4301699, 0.7611018, 1.6249680, 0.0000000, 5.9655170,…\n$ Prog_438 <dbl> 0.0000000, 1.3715048, 9.9041891, 0.0000000, 1.0414313,…\n$ Prog_439 <dbl> 0.000000, 0.919893, 9.514768, 0.000000, 6.719198, 1.47…\n$ Prog_440 <dbl> 0.000000, 0.000000, 8.620704, 0.000000, 1.516426, 0.00…\n$ Prog_441 <dbl> 0.0000000, 3.7077249, 0.8515092, 0.0000000, 5.2171313,…\n$ Prog_443 <dbl> 0.8568178, 0.8568178, 0.8568178, 0.0000000, 8.7319079,…\n$ Prog_444 <dbl> 0.0000000, 0.0000000, 6.2211540, 0.0000000, 5.7354811,…\n$ Prog_445 <dbl> 2.7511743, 0.0000000, 0.9674944, 0.0000000, 9.2741933,…\n$ Prog_446 <dbl> 0.0000000, 0.0000000, 1.8019241, 0.0000000, 1.8019241,…\n$ Prog_447 <dbl> 0.0000000, 0.9446561, 1.9161671, 0.0000000, 5.5898003,…\n$ Prog_448 <dbl> 0.0000000, 0.9878187, 2.5646029, 0.0000000, 7.6480800,…\n$ Prog_449 <dbl> 0.0000000, 0.7278756, 9.7152860, 0.0000000, 6.4746081,…\n$ Prog_450 <dbl> 0.000000, 0.985243, 1.977807, 0.000000, 0.985243, 0.00…\n$ Prog_451 <dbl> 0.6729049, 0.0000000, 0.0000000, 0.6729049, 1.7557933,…\n$ Prog_453 <dbl> 1.389362, 1.389362, 8.979295, 0.000000, 3.802920, 1.38…\n$ Prog_454 <dbl> 0.000000, 0.000000, 7.000632, 0.000000, 7.480365, 1.33…\n$ Prog_455 <dbl> 0.000000, 0.000000, 7.930216, 0.000000, 7.108809, 2.85…\n$ Prog_456 <dbl> 0.000000, 1.432880, 0.000000, 0.000000, 9.287355, 2.60…\n$ Prog_457 <dbl> 0.000000, 0.000000, 7.041415, 1.190074, 2.276476, 0.00…\n$ Prog_458 <dbl> 0.000000, 1.882882, 3.180201, 0.000000, 7.022693, 0.00…\n$ Prog_459 <dbl> 0.000000, 1.649848, 1.048936, 0.000000, 2.072795, 0.00…\n$ Prog_460 <dbl> 0.000000, 0.000000, 2.257397, 2.257397, 6.549074, 1.53…\n$ Prog_461 <dbl> 0.000000, 0.000000, 9.434593, 0.000000, 9.285307, 3.02…\n$ Prog_462 <dbl> 0.000000, 1.634975, 9.970640, 0.000000, 7.950592, 1.63…\n$ Prog_463 <dbl> 0.000000, 0.000000, 2.116349, 0.000000, 2.445710, 0.00…\n$ Prog_464 <dbl> 0.000000, 0.000000, 1.648173, 0.000000, 1.047665, 2.39…\n$ Prog_465 <dbl> 0.000000, 0.000000, 3.880921, 0.000000, 7.349103, 0.00…\n$ Prog_466 <dbl> 0.000000, 0.000000, 10.601220, 0.000000, 9.686435, 0.0…\n$ Prog_467 <dbl> 0.0000000, 0.0000000, 8.4180800, 0.0000000, 2.6458263,…\n$ Prog_468 <dbl> 0.000000, 0.000000, 1.313332, 7.104689, 1.313332, 0.00…\n$ Prog_469 <dbl> 0.000000, 0.000000, 9.461121, 0.000000, 2.538473, 0.00…\n$ Prog_470 <dbl> 0.0000000, 1.3664076, 0.8392653, 0.0000000, 4.0689094,…\n$ Prog_471 <dbl> 0.0000000, 1.8011894, 0.6971231, 0.0000000, 0.6971231,…\n$ Prog_472 <dbl> 0.000000, 0.000000, 6.413269, 0.000000, 3.085256, 0.00…\n$ Prog_473 <dbl> 1.056667, 0.000000, 8.155181, 1.056667, 2.903177, 0.00…\n$ Prog_474 <dbl> 0.0000000, 0.0000000, 0.7632875, 0.0000000, 7.2641661,…\n$ Prog_475 <dbl> 0.000000, 0.000000, 1.542251, 0.000000, 3.781350, 0.00…\n$ Prog_476 <dbl> 0.0000000, 0.0000000, 2.4062738, 0.0000000, 5.4597707,…\n$ Prog_477 <dbl> 0.000000, 1.655184, 9.744210, 0.000000, 7.858513, 0.00…\n$ Prog_478 <dbl> 0.0000000, 0.0000000, 10.0977542, 0.0000000, 3.2996519…\n$ Prog_479 <dbl> 0.0000000, 0.7318815, 2.7964385, 0.0000000, 8.8233345,…\n$ Prog_480 <dbl> 0.0000000, 0.8383081, 2.8686112, 0.0000000, 2.5179592,…\n$ Prog_481 <dbl> 1.110305, 1.110305, 1.110305, 7.826472, 1.730238, 0.00…\n$ Prog_482 <dbl> 0.0000000, 0.0000000, 9.4254529, 0.0000000, 0.8800252,…\n$ Prog_483 <dbl> 1.626780, 1.626780, 8.333546, 0.000000, 2.860901, 1.62…\n$ Prog_484 <dbl> 0.000000, 1.111289, 2.163872, 0.000000, 5.296191, 0.00…\n$ Prog_485 <dbl> 0.0000000, 0.9130228, 1.2169081, 0.5275642, 1.6814343,…\n$ Prog_486 <dbl> 0.000000, 1.568011, 8.198585, 0.000000, 0.000000, 0.00…\n$ Prog_487 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.1249038,…\n$ Prog_488 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.7316639,…\n$ Prog_489 <dbl> 0.000000, 0.000000, 1.005175, 0.000000, 6.054569, 0.00…\n$ Prog_490 <dbl> 0.000000, 2.212057, 3.047313, 0.000000, 3.957303, 0.00…\n$ Prog_491 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.1603819,…\n$ Prog_493 <dbl> 0.0000000, 0.0000000, 3.5499065, 0.7394121, 8.9976185,…\n$ Prog_494 <dbl> 1.831731, 0.000000, 2.613361, 0.000000, 3.490385, 3.49…\n$ Prog_495 <dbl> 1.265705, 1.929372, 0.000000, 0.000000, 1.265705, 2.38…\n$ Prog_496 <dbl> 0.000000, 0.000000, 9.475254, 0.000000, 1.669143, 2.42…\n$ Prog_497 <dbl> 0.000000, 0.000000, 8.923724, 0.000000, 3.399092, 1.76…\n$ Prog_498 <dbl> 0.0000000, 0.9340109, 1.4962888, 0.0000000, 0.0000000,…\n$ Prog_499 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.3263224,…\n$ Prog_500 <dbl> 0.0000000, 0.9006771, 0.0000000, 0.0000000, 5.2907726,…\n$ Prog_501 <dbl> 0.000000, 1.120901, 0.000000, 0.000000, 7.945672, 1.12…\n$ Prog_502 <dbl> 0.000000, 1.455251, 8.049410, 0.000000, 7.386855, 1.45…\n$ Prog_503 <dbl> 0.000000, 0.000000, 1.783222, 0.000000, 1.783222, 0.00…\n$ Prog_504 <dbl> 0.000000, 0.000000, 1.378520, 1.378520, 2.070400, 1.37…\n$ Prog_505 <dbl> 0.0000000, 0.8351884, 0.0000000, 0.0000000, 1.7451384,…\n$ Prog_506 <dbl> 0.000000, 0.000000, 2.164756, 0.000000, 4.527616, 1.11…\n$ Prog_507 <dbl> 0.000000, 2.909729, 8.916157, 0.000000, 6.761468, 0.00…\n$ Prog_508 <dbl> 0.0000000, 0.0000000, 8.8593867, 0.5896197, 6.1430118,…\n$ Prog_509 <dbl> 1.265883, 1.929596, 7.888724, 0.000000, 7.939140, 1.26…\n$ Prog_510 <dbl> 0.000000, 0.000000, 9.093905, 0.000000, 7.553223, 0.00…\n$ Prog_511 <dbl> 0.000000, 1.575947, 3.309744, 0.000000, 8.234428, 0.99…\n$ Prog_513 <dbl> 0.000000, 0.000000, 9.854753, 0.000000, 7.868100, 1.26…\n$ Prog_514 <dbl> 0.0000000, 0.9527165, 0.9527165, 0.0000000, 2.7253228,…\n$ Prog_515 <dbl> 0.000000, 0.000000, 7.842518, 0.000000, 1.234028, 0.00…\n$ Prog_516 <dbl> 0.0000000, 0.5366068, 10.4910286, 0.0000000, 7.9975441…\n$ Prog_517 <dbl> 0.000000, 1.039847, 1.637851, 0.000000, 2.385166, 1.63…\n$ Prog_518 <dbl> 0.000000, 1.051187, 1.051187, 0.000000, 1.051187, 0.00…\n$ Prog_519 <dbl> 0.000000, 0.000000, 2.266830, 1.539194, 2.266830, 0.00…\n$ Prog_520 <dbl> 0.0000000, 0.0000000, 0.8762977, 0.5036838, 8.8143147,…\n$ Prog_521 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.2659645,…\n$ Prog_523 <dbl> 0.0000000, 0.0000000, 6.3655387, 0.6771116, 9.8628645,…\n$ Prog_524 <dbl> 0.000000, 0.000000, 0.000000, 2.300151, 2.300151, 0.00…\n$ Prog_525 <dbl> 0.000000, 0.000000, 10.149746, 0.000000, 8.955660, 0.0…\n$ Prog_526 <dbl> 0.000000, 2.465377, 2.465377, 0.000000, 4.254809, 0.00…\n$ Prog_527 <dbl> 0.000000, 1.405156, 1.405156, 1.405156, 4.487359, 0.00…\n$ Prog_528 <dbl> 0.000000, 0.000000, 3.703958, 0.000000, 2.002859, 0.00…\n$ Prog_529 <dbl> 0.0000000, 0.6208773, 0.3435841, 0.3435841, 7.9689194,…\n$ Prog_530 <dbl> 3.616063, 1.847519, 0.900157, 0.000000, 1.450242, 0.00…\n$ Prog_531 <dbl> 0.0000000, 0.0000000, 0.8097507, 0.0000000, 1.3252810,…\n$ Prog_532 <dbl> 0.000000, 0.000000, 2.220678, 0.000000, 7.634453, 0.00…\n$ Prog_533 <dbl> 0.000000, 2.341256, 7.785561, 0.000000, 3.722719, 0.00…\n$ Prog_535 <dbl> 0.6464963, 0.0000000, 9.4669991, 0.0000000, 8.0157220,…\n$ Prog_536 <dbl> 0.7907649, 0.0000000, 9.2330993, 0.7907649, 7.8468524,…\n$ Prog_537 <dbl> 0.000000, 0.000000, 2.183805, 0.000000, 9.202666, 2.18…\n$ Prog_539 <dbl> 0.000000, 1.283362, 9.065827, 0.000000, 4.820028, 1.28…\n$ Prog_541 <dbl> 0.2532158, 0.2532158, 1.1051183, 6.7835723, 0.9703415,…\n$ Prog_542 <dbl> 0.000000, 0.000000, 2.058906, 0.000000, 0.000000, 0.00…\n$ Prog_543 <dbl> 0.000000, 0.000000, 8.238569, 0.000000, 8.274372, 0.00…\n$ Prog_545 <dbl> 0.000000, 1.026229, 10.445342, 1.619829, 1.026229, 0.0…\n$ Prog_546 <dbl> 0.000000, 0.000000, 2.149675, 1.442885, 6.088034, 1.44…\n$ Prog_547 <dbl> 0.0000000, 0.3825120, 1.7777364, 0.3825120, 8.8655281,…\n$ Prog_548 <dbl> 0.0000000, 1.0510767, 9.0544075, 0.0000000, 7.4081337,…\n$ Prog_549 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 5.241115, 7.25…\n$ Prog_550 <dbl> 0.0000000, 0.0000000, 1.2782125, 0.0000000, 8.8068960,…\n$ Prog_551 <dbl> 0.0000000, 0.0000000, 1.1223357, 4.3519362, 2.7834476,…\n$ Prog_552 <dbl> 0.000000, 1.176198, 0.000000, 0.000000, 3.625066, 1.17…\n$ Prog_553 <dbl> 0.000000, 1.495165, 7.651670, 0.000000, 8.097214, 0.00…\n$ Prog_554 <dbl> 0.000000, 1.219934, 2.659340, 0.000000, 4.853824, 0.00…\n$ Prog_555 <dbl> 0.000000, 1.141904, 1.141904, 0.000000, 0.000000, 0.00…\n$ Prog_556 <dbl> 0.000000, 1.124787, 9.989247, 0.000000, 2.516714, 1.12…\n$ Prog_557 <dbl> 0.0000000, 1.3494493, 7.3940342, 0.0000000, 2.6822406,…\n$ Prog_558 <dbl> 0.000000, 0.000000, 2.074530, 0.000000, 6.885581, 1.38…\n$ Prog_559 <dbl> 0.5911951, 0.5911951, 0.5911951, 0.5911951, 6.8258074,…\n$ Prog_560 <dbl> 0.0000000, 0.0000000, 0.7046262, 0.0000000, 0.0000000,…\n$ Prog_561 <dbl> 0.000000, 1.947226, 1.947226, 0.000000, 0.000000, 0.00…\n$ Prog_562 <dbl> 0.0000000, 0.6633517, 9.8697423, 0.0000000, 0.6633517,…\n$ Prog_564 <dbl> 0.000000, 1.348598, 9.062297, 0.000000, 6.751123, 0.00…\n$ Prog_565 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.6974946, 1.5185645,…\n$ Prog_566 <dbl> 0.000000, 1.063583, 7.865391, 0.000000, 6.752633, 0.00…\n$ Prog_567 <dbl> 0.0000000, 0.0000000, 0.7865545, 0.0000000, 1.6666884,…\n$ Prog_568 <dbl> 0.000000, 0.000000, 9.497890, 0.000000, 7.413043, 1.68…\n$ Prog_569 <dbl> 0.4774038, 0.0000000, 0.4774038, 0.0000000, 3.4851866,…\n$ Prog_570 <dbl> 0.000000, 2.182193, 9.979357, 0.000000, 5.788057, 0.00…\n$ Prog_571 <dbl> 0.000000, 0.997198, 9.327555, 0.000000, 0.997198, 0.00…\n$ Prog_572 <dbl> 0.000000, 0.000000, 1.750185, 0.000000, 2.518041, 0.00…\n$ Prog_573 <dbl> 0.000000, 2.813110, 0.000000, 0.000000, 0.000000, 0.00…\n$ Prog_574 <dbl> 1.031583, 2.047119, 0.000000, 0.000000, 1.031583, 0.00…\n$ Prog_575 <dbl> 0.0000000, 0.7179101, 8.6030543, 0.4031573, 6.7852000,…\n$ Prog_576 <dbl> 0.0000000, 0.0000000, 2.3868570, 0.0000000, 6.3835014,…\n$ Prog_577 <dbl> 0.0000000, 0.0000000, 0.7666567, 0.7666567, 0.7666567,…\n$ Prog_578 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.454997, 0.00…\n$ Prog_579 <dbl> 1.603613, 1.603613, 0.000000, 0.000000, 1.603613, 0.00…\n$ Prog_580 <dbl> 1.209853, 1.209853, 6.432193, 0.000000, 2.304342, 0.00…\n$ Prog_581 <dbl> 0.0000000, 0.0000000, 0.6688077, 0.6688077, 9.3476295,…\n$ Prog_582 <dbl> 0.000000, 2.354813, 1.020641, 0.000000, 5.712623, 1.02…\n$ Prog_583 <dbl> 0.000000, 0.000000, 0.000000, 1.979123, 4.221506, 0.00…\n$ Prog_584 <dbl> 0.000000, 0.000000, 1.079040, 0.000000, 1.689415, 0.00…\n$ Prog_585 <dbl> 0.0000000, 1.5173021, 8.9527636, 0.0000000, 0.0000000,…\n$ Prog_586 <dbl> 0.000000, 1.696757, 3.317139, 0.000000, 5.204275, 1.69…\n$ Prog_587 <dbl> 0.6020971, 1.0256262, 1.8438260, 0.6020971, 2.0382706,…\n$ Prog_588 <dbl> 0.000000, 0.000000, 10.081128, 0.000000, 2.420903, 0.0…\n$ Prog_589 <dbl> 0.000000, 1.628065, 10.059886, 0.000000, 2.373499, 1.6…\n$ Prog_590 <dbl> 0.0000000, 0.0000000, 1.9411007, 0.0000000, 8.5023337,…\n$ Prog_592 <dbl> 0.000000, 0.000000, 8.895117, 0.000000, 6.650046, 0.48…\n$ Prog_593 <dbl> 0.6844273, 0.6844273, 9.6524015, 0.0000000, 9.2962372,…\n$ Prog_594 <dbl> 0.6207776, 0.6207776, 1.3857686, 0.6207776, 7.6893191,…\n$ Prog_595 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 6.647709, 2.33…\n$ Prog_596 <dbl> 0.8059391, 0.8059391, 0.0000000, 0.0000000, 3.6973885,…\n$ Prog_597 <dbl> 0.000000, 0.000000, 1.671257, 0.000000, 6.148068, 1.67…\n$ Prog_598 <dbl> 0.0000000, 0.0000000, 0.8266159, 0.0000000, 6.1419402,…\n$ Prog_599 <dbl> 0.0000000, 0.7373198, 9.1258666, 0.7373198, 5.5348447,…\n$ Prog_600 <dbl> 0.0000000, 1.4306495, 0.0000000, 0.0000000, 1.9358875,…\n$ Prog_602 <dbl> 0.0000000, 0.7878959, 1.4938453, 0.0000000, 6.8734285,…\n$ Prog_603 <dbl> 0.0000000, 0.5895655, 8.8497855, 0.0000000, 1.0068990,…\n$ Prog_604 <dbl> 0.000000, 2.158370, 6.354541, 0.000000, 5.201466, 0.00…\n$ Prog_605 <dbl> 0.0000000, 1.4916226, 9.0127039, 0.0000000, 8.5722250,…\n$ Prog_606 <dbl> 0.3359520, 0.0000000, 0.6082605, 0.3359520, 1.2085485,…\n$ Prog_607 <dbl> 0.0000000, 1.0003358, 0.5851864, 0.0000000, 1.0003358,…\n$ Prog_609 <dbl> 0.0000000, 0.0000000, 1.4356382, 0.6492377, 1.4356382,…\n$ Prog_610 <dbl> 0.000000, 1.132395, 7.441885, 0.000000, 1.132395, 0.00…\n$ Prog_611 <dbl> 0.000000, 1.114904, 9.678417, 0.000000, 7.496128, 2.50…\n$ Prog_612 <dbl> 0.6916362, 1.7909624, 2.9572192, 0.0000000, 0.6916362,…\n$ Prog_613 <dbl> 0.0000000, 0.0000000, 10.2758159, 0.0000000, 1.4086882…\n$ Prog_614 <dbl> 0.0000000, 0.0000000, 9.0110580, 0.0000000, 7.1304853,…\n$ Prog_615 <dbl> 0.000000, 1.171725, 9.531687, 0.000000, 1.809668, 0.00…\n$ Prog_616 <dbl> 0.0000000, 0.0000000, 9.6063822, 0.0000000, 2.7659031,…\n$ Prog_617 <dbl> 1.264360, 0.000000, 3.001987, 0.000000, 9.875097, 0.00…\n$ Prog_618 <dbl> 0.0000000, 1.2218338, 8.8580415, 0.0000000, 2.8065170,…\n$ Prog_619 <dbl> 0.000000, 0.000000, 7.167761, 0.000000, 8.648737, 0.00…\n$ Prog_620 <dbl> 0.000000, 0.000000, 2.286936, 0.000000, 3.659988, 1.55…\n$ Prog_621 <dbl> 0.0000000, 1.3888582, 1.3888582, 0.0000000, 1.3888582,…\n$ Prog_622 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.2209391,…\n$ Prog_623 <dbl> 0.6405688, 0.6405688, 1.6941112, 0.6405688, 8.3794328,…\n$ Prog_624 <dbl> 0.0000000, 0.0000000, 0.0000000, 2.0571234, 0.6106303,…\n$ Prog_625 <dbl> 0.000000, 1.388263, 2.549152, 0.000000, 2.549152, 0.00…\n$ Prog_626 <dbl> 0.000000, 1.008900, 1.008900, 0.000000, 6.812002, 1.00…\n$ Prog_627 <dbl> 0.000000, 0.000000, 1.920623, 0.000000, 1.258780, 7.40…\n$ Prog_628 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 8.4838024,…\n$ Prog_629 <dbl> 5.0303197, 0.0000000, 0.0000000, 0.0000000, 4.9755010,…\n$ Prog_631 <dbl> 0.0000000, 1.4518123, 0.6585587, 0.0000000, 1.4518123,…\n$ Prog_632 <dbl> 0.000000, 0.000000, 10.173789, 0.000000, 8.744978, 0.0…\n$ Prog_633 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.674084, 1.88…\n$ Prog_634 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.537888, 0.00…\n$ Prog_635 <dbl> 0.0000000, 1.7763551, 2.0820650, 0.8548192, 9.2636820,…\n$ Prog_636 <dbl> 0.0000000, 0.8921448, 0.8921448, 0.0000000, 1.9984723,…\n$ Prog_637 <dbl> 0.0000000, 0.8377079, 8.9592153, 0.0000000, 7.2342868,…\n$ Prog_638 <dbl> 0.0000000, 0.7605919, 8.3816073, 0.0000000, 5.8732571,…\n$ Prog_639 <dbl> 0.000000, 1.163663, 8.702121, 0.000000, 7.683991, 2.23…\n$ Prog_640 <dbl> 0.4944753, 0.0000000, 9.4705871, 0.4944753, 4.0113082,…\n$ Prog_641 <dbl> 0.000000, 2.188281, 4.231748, 0.000000, 2.188281, 2.18…\n$ Prog_642 <dbl> 0.0000000, 0.0000000, 2.1674040, 0.7642007, 1.6300728,…\n$ Prog_643 <dbl> 0.000000, 1.302062, 3.058153, 0.000000, 6.000673, 0.00…\n$ Prog_644 <dbl> 0.0000000, 0.9303941, 0.9303941, 0.0000000, 4.7691423,…\n$ Prog_645 <dbl> 0.000000, 1.608213, 3.200844, 0.000000, 5.588596, 1.60…\n$ Prog_646 <dbl> 0.000000, 0.000000, 9.617756, 0.000000, 1.260740, 1.26…\n$ Prog_647 <dbl> 0.000000, 1.368686, 2.522823, 0.000000, 2.873697, 2.52…\n$ Prog_648 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.8965290,…\n$ Prog_649 <dbl> 0.0000000, 0.5925347, 9.7824606, 0.0000000, 7.1517211,…\n$ Prog_650 <dbl> 0.0000000, 0.0000000, 1.5687693, 0.0000000, 1.5687693,…\n$ Prog_651 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.774445, 1.14…\n$ Prog_652 <dbl> 0.0000000, 1.3231882, 1.7018936, 0.0000000, 2.8091547,…\n$ Prog_653 <dbl> 1.061994, 1.061994, 0.000000, 0.000000, 5.288120, 1.06…\n$ Prog_654 <dbl> 0.000000, 1.589743, 7.721376, 0.000000, 2.590935, 0.00…\n$ Prog_655 <dbl> 0.0000000, 0.6610958, 10.3373698, 0.6610958, 1.4562015…\n$ Prog_657 <dbl> 0.000000, 0.651827, 9.382710, 0.000000, 0.651827, 0.00…\n$ Prog_658 <dbl> 0.0000000, 0.9072354, 0.9072354, 0.0000000, 8.0471779,…\n$ Prog_659 <dbl> 0.0000000, 1.5454819, 2.5354405, 0.0000000, 1.9555074,…\n$ Prog_661 <dbl> 0.0000000, 0.9426602, 1.9131123, 0.5469903, 1.7247256,…\n$ Prog_662 <dbl> 0.0000000, 0.9950628, 0.0000000, 0.0000000, 8.9731218,…\n$ Prog_663 <dbl> 0.0000000, 0.7811878, 10.0093743, 0.0000000, 0.7811878…\n$ Prog_665 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.131402, 0.00…\n$ Prog_666 <dbl> 0.000000, 1.043999, 1.043999, 1.043999, 6.918847, 2.65…\n$ Prog_667 <dbl> 0.000000, 0.000000, 7.672750, 1.561871, 1.561871, 1.56…\n$ Prog_668 <dbl> 0.000000, 0.000000, 10.119086, 1.452127, 2.160992, 0.0…\n$ Prog_669 <dbl> 0.000000, 0.000000, 4.232952, 0.000000, 4.539453, 0.00…\n$ Prog_670 <dbl> 0.0000000, 0.0000000, 0.9334255, 0.0000000, 3.8725409,…\n$ Prog_671 <dbl> 0.000000, 0.000000, 1.892324, 1.236440, 4.504532, 1.23…\n$ Prog_672 <dbl> 0.000000, 1.645398, 7.864120, 0.000000, 1.645398, 0.00…\n$ Prog_673 <dbl> 0.0000000, 1.9663305, 1.5550731, 0.9776412, 5.0437391,…\n$ Prog_674 <dbl> 0.000000, 0.000000, 1.040583, 0.000000, 8.039744, 2.06…\n$ Prog_675 <dbl> 0.000000, 1.169400, 1.169400, 0.000000, 5.356630, 0.00…\n$ Prog_676 <dbl> 0.000000, 1.594415, 2.010630, 0.000000, 2.010630, 0.00…\n$ Prog_677 <dbl> 0.0000000, 1.1248680, 9.4439808, 0.0000000, 7.0125523,…\n$ Prog_678 <dbl> 0.000000, 2.193471, 8.208765, 0.000000, 9.694170, 1.47…\n$ Prog_679 <dbl> 0.000000, 0.000000, 1.135700, 0.000000, 2.533315, 0.00…\n$ Prog_680 <dbl> 0.0000000, 1.8157017, 0.8797863, 0.0000000, 8.8680725,…\n$ Prog_681 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.2517939,…\n$ Prog_682 <dbl> 0.0000000, 0.4016099, 1.9593362, 0.0000000, 6.5007023,…\n$ Prog_683 <dbl> 0.000000, 9.583948, 2.195848, 0.000000, 2.195848, 0.00…\n$ Prog_684 <dbl> 0.0000000, 0.0000000, 0.9312689, 0.0000000, 8.4960952,…\n$ Prog_685 <dbl> 0.0000000, 0.0000000, 1.3228236, 0.0000000, 1.3228236,…\n$ Prog_686 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.587241, 0.00…\n$ Prog_687 <dbl> 0.0000000, 0.0000000, 0.8278948, 0.0000000, 8.9208238,…\n$ Prog_688 <dbl> 0.5330259, 0.9213767, 8.2919793, 0.5330259, 2.3284480,…\n$ Prog_689 <dbl> 0.000000, 0.000000, 3.769030, 1.265236, 1.265236, 0.00…\n$ Prog_690 <dbl> 0.000000, 0.000000, 2.334005, 0.000000, 3.183338, 0.00…\n$ Prog_692 <dbl> 0.000000, 0.000000, 2.536182, 0.000000, 3.646306, 0.00…\n$ Prog_693 <dbl> 0.8515745, 0.0000000, 1.3834628, 0.0000000, 8.3090505,…\n$ Prog_694 <dbl> 0.000000, 0.000000, 9.762034, 0.000000, 4.183809, 1.15…\n$ Prog_695 <dbl> 0.0000000, 0.0000000, 10.0125370, 0.0000000, 2.6631327…\n$ Prog_696 <dbl> 0.000000, 0.000000, 8.383161, 0.000000, 4.056444, 0.00…\n$ Prog_697 <dbl> 0.0000000, 0.0000000, 10.2221871, 0.0000000, 5.7896883…\n$ Prog_698 <dbl> 0.0000000, 0.6822428, 1.4925696, 0.0000000, 1.7733759,…\n$ Prog_699 <dbl> 0.000000, 0.957785, 0.000000, 0.000000, 0.957785, 0.00…\n$ Prog_700 <dbl> 0.0000000, 0.7265333, 9.2553627, 0.0000000, 1.8554449,…\n$ Prog_701 <dbl> 0.0000000, 0.6332115, 6.4960826, 0.0000000, 0.6332115,…\n$ Prog_702 <dbl> 0.000000, 1.275410, 10.011801, 0.000000, 4.568689, 0.0…\n$ Prog_704 <dbl> 1.221222, 0.000000, 1.221222, 0.000000, 3.168104, 1.22…\n$ Prog_705 <dbl> 0.0000000, 0.8741893, 0.0000000, 0.0000000, 1.4146527,…\n$ Prog_706 <dbl> 0.0000000, 0.0000000, 2.4793580, 0.0000000, 4.6052639,…\n$ Prog_707 <dbl> 0.000000, 0.000000, 1.786510, 0.000000, 3.587853, 1.15…\n$ Prog_708 <dbl> 0.000000, 0.000000, 9.549802, 1.348519, 8.374833, 1.34…\n$ Prog_709 <dbl> 0.0000000, 1.5350623, 7.6797336, 0.0000000, 1.9437392,…\n$ Prog_710 <dbl> 0.000000, 0.000000, 8.215204, 0.000000, 8.978276, 2.54…\n$ Prog_711 <dbl> 0.000000, 1.040121, 6.806350, 0.000000, 2.059771, 1.04…\n$ Prog_712 <dbl> 0.0000000, 0.7702032, 10.9709094, 0.4359005, 9.2048176…\n$ Prog_713 <dbl> 0.0000000, 0.0000000, 3.2947070, 0.0000000, 1.5863921,…\n$ Prog_714 <dbl> 0.0000000, 0.0000000, 8.5213414, 0.0000000, 5.9558786,…\n$ Prog_715 <dbl> 0.0000000, 0.0000000, 2.1701910, 0.7656756, 6.5883034,…\n$ Prog_716 <dbl> 0.000000, 1.176927, 1.176927, 0.000000, 1.816351, 1.17…\n$ Prog_717 <dbl> 0.0000000, 0.0000000, 0.7005884, 0.0000000, 8.0109133,…\n$ Prog_718 <dbl> 0.0000000, 0.0000000, 6.1872182, 0.0000000, 7.4849030,…\n$ Prog_719 <dbl> 0.0000000, 0.0000000, 9.5219963, 0.0000000, 7.8381010,…\n$ Prog_720 <dbl> 0.0000000, 0.7519114, 1.2436965, 0.0000000, 2.3516665,…\n$ Prog_721 <dbl> 0.0000000, 0.8558138, 10.0556082, 0.0000000, 2.0837639…\n$ Prog_722 <dbl> 0.0000000, 1.1100129, 6.7723804, 0.0000000, 7.3757589,…\n$ Prog_723 <dbl> 0.0000000, 0.5725688, 10.3326661, 0.0000000, 7.3464267…\n$ Prog_724 <dbl> 0.000000, 0.000000, 2.873689, 0.000000, 2.600150, 0.00…\n$ Prog_725 <dbl> 0.000000, 0.000000, 3.337706, 0.000000, 8.312086, 0.00…\n$ Prog_726 <dbl> 0.000000, 1.980022, 2.437623, 0.000000, 1.980022, 1.30…\n$ Prog_727 <dbl> 0.0000000, 0.7280794, 2.3041007, 0.0000000, 1.2096811,…\n$ Prog_728 <dbl> 0.0000000, 0.0000000, 3.3216219, 0.0000000, 6.9654468,…\n$ Prog_729 <dbl> 0.000000, 1.012052, 3.021026, 1.601010, 1.601010, 1.01…\n$ Prog_730 <dbl> 0.0000000, 0.6177827, 9.7062851, 0.0000000, 3.8442726,…\n$ Prog_731 <dbl> 0.8692337, 0.8692337, 9.9924544, 0.0000000, 8.0210060,…\n$ Prog_732 <dbl> 0.000000, 1.249094, 2.359174, 0.000000, 5.305744, 1.24…\n$ Prog_733 <dbl> 0.8753909, 1.4163048, 2.5866519, 0.0000000, 2.1170368,…\n$ Prog_734 <dbl> 0.000000, 0.000000, 9.663944, 0.000000, 4.813194, 0.00…\n$ Prog_735 <dbl> 0.0000000, 0.0000000, 0.8639745, 0.0000000, 0.8639745,…\n$ Prog_736 <dbl> 0.0000000, 0.6604615, 0.6604615, 0.0000000, 4.2477932,…\n$ Prog_737 <dbl> 0.000000, 2.165385, 1.732880, 0.000000, 10.736052, 2.1…\n$ Prog_738 <dbl> 0.000000, 2.017320, 1.335801, 1.335801, 3.342687, 0.00…\n$ Prog_739 <dbl> 0.000000, 1.906558, 1.247665, 0.000000, 1.247665, 0.00…\n$ Prog_740 <dbl> 0.000000, 0.000000, 2.921112, 0.000000, 1.674204, 0.00…\n$ Prog_743 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.6795528,…\n$ Prog_744 <dbl> 0.0000000, 1.5756644, 0.0000000, 0.0000000, 8.0084521,…\n$ Prog_745 <dbl> 0.000000, 1.167434, 1.804151, 0.000000, 3.455353, 0.00…\n$ Prog_746 <dbl> 0.000000, 0.000000, 9.257702, 0.000000, 0.000000, 0.00…\n$ Prog_747 <dbl> 0.000000, 0.000000, 2.651195, 0.000000, 1.214416, 2.92…\n$ Prog_748 <dbl> 0.000000, 0.000000, 1.912066, 0.000000, 1.912066, 0.00…\n$ Prog_749 <dbl> 0.000000, 0.000000, 7.907326, 0.000000, 7.494291, 0.00…\n$ Prog_750 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…\n$ Prog_751 <dbl> 0.000000, 0.000000, 9.567823, 0.000000, 8.062315, 1.43…\n$ Prog_752 <dbl> 0.000000, 1.088315, 8.584688, 0.000000, 1.088315, 2.13…\n$ Prog_753 <dbl> 0.4546240, 0.0000000, 1.3113301, 0.0000000, 1.0781157,…\n$ Prog_754 <dbl> 2.5350590, 0.9045812, 1.2066442, 0.0000000, 7.1346333,…\n$ Prog_757 <dbl> 0.000000, 0.000000, 8.521070, 0.000000, 3.008178, 0.00…\n$ Prog_758 <dbl> 0.000000, 1.027425, 10.650493, 0.000000, 6.577320, 0.0…\n$ Prog_759 <dbl> 0.0000000, 0.0000000, 1.1091425, 0.0000000, 6.6345901,…\n$ Prog_760 <dbl> 0.0000000, 7.3793575, 1.9694755, 0.7899915, 1.6722863,…\n$ Prog_761 <dbl> 0.000000, 1.623226, 2.856367, 0.000000, 8.266286, 1.02…\n$ Prog_762 <dbl> 0.0000000, 0.6583874, 1.1087820, 0.0000000, 7.1799196,…\n$ Prog_763 <dbl> 0.5774134, 0.5774134, 0.5774134, 0.0000000, 8.7733244,…\n$ Prog_764 <dbl> 0.0000000, 0.0000000, 2.9788233, 0.0000000, 3.5627696,…\n$ Prog_765 <dbl> 0.4887321, 6.8867263, 1.5926631, 0.0000000, 7.2306270,…\n$ Prog_767 <dbl> 0.0000000, 0.0000000, 9.0647725, 0.0000000, 9.5066596,…\n$ Prog_768 <dbl> 0.0000000, 0.8028968, 1.6932313, 0.0000000, 7.9467204,…\n$ Prog_769 <dbl> 0.0000000, 0.0000000, 1.8636357, 0.0000000, 2.8396004,…\n$ Prog_770 <dbl> 1.022676, 0.000000, 10.140358, 1.022676, 5.744589, 1.0…\n$ Prog_771 <dbl> 0.000000, 0.000000, 7.102581, 0.000000, 4.293110, 0.00…\n$ Prog_772 <dbl> 0.9806377, 0.9806377, 2.7740015, 0.0000000, 5.4537430,…\n$ Prog_774 <dbl> 0.000000, 0.000000, 1.939114, 0.000000, 6.398148, 1.09…\n$ Prog_775 <dbl> 0.0000000, 1.2167722, 1.8673123, 0.0000000, 2.4943937,…\n$ Prog_776 <dbl> 0.6768304, 0.0000000, 9.7760819, 0.0000000, 4.9786447,…\n$ Prog_777 <dbl> 0.0000000, 0.9423675, 1.5075959, 0.0000000, 0.9423675,…\n$ Prog_778 <dbl> 0.000000, 0.000000, 8.986217, 0.000000, 8.551634, 0.00…\n$ Prog_779 <dbl> 0.4211472, 0.4211472, 2.1341941, 0.0000000, 1.4302634,…\n$ Prog_780 <dbl> 0.5131073, 0.5131073, 9.3775848, 0.8908289, 7.1492370,…\n$ Prog_781 <dbl> 0.000000, 1.080345, 0.000000, 0.000000, 2.448417, 1.08…\n$ Prog_782 <dbl> 0.000000, 0.774577, 2.578895, 5.923573, 2.186957, 1.64…\n$ Prog_783 <dbl> 1.074572, 0.000000, 10.556887, 0.000000, 7.781406, 1.6…\n$ Prog_784 <dbl> 0.000000, 0.000000, 2.729708, 4.454398, 9.011330, 0.00…\n$ Prog_785 <dbl> 0.000000, 0.000000, 7.680402, 0.000000, 3.650143, 0.00…\n$ Prog_786 <dbl> 0.8739354, 0.0000000, 0.8739354, 0.8739354, 1.4143035,…\n$ Prog_787 <dbl> 1.738193, 0.000000, 3.370817, 0.000000, 9.072318, 1.73…\n$ Prog_788 <dbl> 0.7368005, 1.2221566, 9.1819824, 0.0000000, 8.2710517,…\n$ Prog_789 <dbl> 0.0000000, 6.2993373, 3.2689842, 0.0000000, 2.9839948,…\n$ Prog_790 <dbl> 0.641878, 0.000000, 11.027752, 0.641878, 1.926681, 1.6…\n$ Prog_791 <dbl> 7.354611, 0.888815, 3.115182, 0.000000, 4.584642, 0.00…\n$ Prog_792 <dbl> 0.0000000, 0.5602293, 1.7538261, 0.0000000, 7.1441796,…\n$ Prog_793 <dbl> 0.0000000, 0.6100055, 0.6100055, 0.6100055, 8.3024400,…\n$ Prog_794 <dbl> 0.0000000, 1.2839055, 7.7226264, 1.6564775, 9.9829054,…\n$ Prog_795 <dbl> 0.0000000, 0.8871069, 2.1367998, 0.0000000, 7.9894805,…\n$ Prog_796 <dbl> 0.000000, 0.000000, 2.296474, 0.000000, 6.034682, 0.00…\n$ Prog_797 <dbl> 0.000000, 0.000000, 1.464793, 2.176476, 3.531249, 0.00…\n$ Prog_798 <dbl> 0.0000000, 0.8285245, 1.7344840, 8.4849731, 2.1671674,…\n$ Prog_799 <dbl> 0.000000, 4.860757, 0.000000, 0.000000, 7.665351, 1.26…\n$ Prog_800 <dbl> 0.000000, 8.803266, 2.829021, 0.000000, 2.829021, 0.00…\n$ Prog_801 <dbl> 0.5808097, 0.0000000, 1.5766449, 3.4480774, 8.7052117,…\n$ Prog_802 <dbl> 0.000000, 1.898890, 8.412752, 0.000000, 8.778230, 0.00…\n$ Prog_803 <dbl> 0.000000, 2.543597, 7.878204, 0.000000, 3.708852, 0.00…\n$ Prog_804 <dbl> 0.000000, 0.000000, 9.348673, 1.233011, 2.954727, 0.00…\n$ Prog_805 <dbl> 0.000000, 0.000000, 8.315664, 0.000000, 9.369724, 0.00…\n$ Prog_806 <dbl> 0.000000, 0.935748, 0.935748, 0.935748, 7.896934, 0.00…\n$ Prog_807 <dbl> 0.0000000, 0.0000000, 8.6811767, 0.0000000, 8.1350692,…\n$ Prog_808 <dbl> 0.0000000, 0.0000000, 3.0377707, 0.0000000, 1.6615960,…\n$ Prog_809 <dbl> 0.6287654, 0.0000000, 2.2701368, 0.6287654, 1.6712775,…\n$ Prog_810 <dbl> 0.0000000, 0.5049969, 1.1746370, 0.0000000, 7.3167564,…\n$ Prog_811 <dbl> 0.000000, 1.329155, 9.629477, 0.000000, 5.626490, 0.00…\n$ Prog_812 <dbl> 0.000000, 3.245813, 9.354092, 0.000000, 7.095686, 0.00…\n$ Prog_813 <dbl> 0.0000000, 0.8803957, 10.2579542, 0.0000000, 5.5099063…\n$ Prog_814 <dbl> 0.0000000, 0.0000000, 9.3443362, 0.8521854, 2.7304517,…\n$ Prog_815 <dbl> 0.000000, 0.000000, 9.145702, 0.000000, 1.640454, 1.04…\n$ Prog_816 <dbl> 0.0000000, 1.5005517, 7.5725424, 0.0000000, 8.4454565,…\n$ Prog_817 <dbl> 0.000000, 0.617522, 1.876247, 0.000000, 1.876247, 0.61…\n$ Prog_818 <dbl> 0.000000, 0.000000, 8.817434, 0.000000, 5.110650, 0.00…\n$ Prog_819 <dbl> 0.0000000, 0.0000000, 9.9789196, 0.0000000, 7.2226335,…\n$ Prog_820 <dbl> 0.0000000, 0.0000000, 7.8202471, 0.6094958, 6.5794013,…\n$ Prog_821 <dbl> 0.000000, 0.527402, 9.347671, 0.000000, 7.134023, 0.00…\n$ Prog_822 <dbl> 0.000000, 0.000000, 10.830559, 0.000000, 5.611126, 0.7…\n$ Prog_823 <dbl> 0.000000, 2.368033, 6.108218, 0.000000, 3.421181, 0.00…\n$ Prog_824 <dbl> 0.0000000, 0.0000000, 3.2970935, 0.0000000, 7.2393470,…\n$ Prog_825 <dbl> 0.614008, 0.000000, 9.102087, 0.000000, 7.067777, 0.61…\n$ Prog_826 <dbl> 0.0000000, 0.0000000, 10.0184296, 0.0000000, 0.8930072…\n$ Prog_827 <dbl> 0.000000, 1.153240, 9.651103, 0.000000, 2.224160, 0.00…\n$ Prog_828 <dbl> 0.0000000, 0.0000000, 1.4730404, 0.0000000, 7.1366525,…\n$ Prog_829 <dbl> 0.000000, 0.000000, 2.002737, 0.679441, 6.402776, 0.00…\n$ Prog_830 <dbl> 0.0000000, 0.0000000, 9.9350866, 0.7122405, 2.0675466,…\n$ Prog_831 <dbl> 0.0000000, 0.7518682, 8.8581337, 0.0000000, 1.2436351,…\n$ Prog_832 <dbl> 0.0000000, 0.9591086, 1.9382199, 0.0000000, 7.2304444,…\n$ Prog_833 <dbl> 0.0000000, 0.8376881, 3.0151862, 0.0000000, 3.0151862,…\n$ Prog_834 <dbl> 0.0000000, 0.0000000, 2.4274006, 0.0000000, 5.4373805,…\n$ Prog_835 <dbl> 0.0000000, 0.9809832, 10.5344381, 0.0000000, 2.5531271…\n$ Prog_836 <dbl> 0.0000000, 0.5020465, 1.2967651, 0.5020465, 1.5225555,…\n$ Prog_837 <dbl> 0.0000000, 0.9850818, 9.8963349, 0.0000000, 0.0000000,…\n$ Prog_838 <dbl> 0.000000, 0.000000, 9.547417, 0.000000, 1.987452, 1.31…\n$ Prog_839 <dbl> 0.0000000, 0.0000000, 8.0095143, 0.0000000, 6.1786824,…\n$ Prog_840 <dbl> 0.0000000, 0.6485416, 2.4700583, 0.0000000, 8.4397289,…\n$ Prog_841 <dbl> 0.000000, 2.018049, 9.332316, 0.000000, 6.751711, 0.00…\n$ Prog_842 <dbl> 0.0000000, 0.0000000, 8.9760410, 0.0000000, 6.6732008,…\n$ Prog_843 <dbl> 0.0000000, 0.0000000, 2.4085725, 0.0000000, 3.5947323,…\n$ Prog_844 <dbl> 1.153513, 1.153513, 10.215305, 0.000000, 2.832433, 0.0…\n$ Prog_845 <dbl> 0.0000000, 0.0000000, 1.5265633, 0.0000000, 5.0433788,…\n$ Prog_846 <dbl> 0.000000, 0.000000, 2.924854, 2.924854, 7.842922, 1.67…\n$ Prog_847 <dbl> 0.0000000, 0.8935156, 2.1475639, 0.8935156, 8.6586321,…\n$ Prog_848 <dbl> 0.000000, 0.000000, 9.603595, 0.000000, 3.859249, 0.00…\n$ Prog_849 <dbl> 1.653308, 2.403562, 4.344108, 0.000000, 2.403562, 1.65…\n$ Prog_850 <dbl> 0.000000, 0.000000, 9.300996, 0.000000, 7.843614, 0.00…\n$ Prog_851 <dbl> 0.000000, 1.120675, 9.108092, 0.000000, 7.050312, 1.12…\n$ Prog_852 <dbl> 0.000000, 0.000000, 9.579540, 0.000000, 3.070503, 0.00…\n$ HSPC_001 <dbl> 0.0000000, 0.0000000, 0.7390988, 0.7390988, 1.2254389,…\n$ HSPC_002 <dbl> 0.000000, 0.000000, 2.238601, 0.000000, 2.238601, 0.00…\n$ HSPC_003 <dbl> 0.0000000, 0.0000000, 0.9929154, 0.9929154, 1.5755086,…\n$ HSPC_004 <dbl> 0.000000, 0.000000, 2.465632, 0.000000, 8.073635, 0.00…\n$ HSPC_006 <dbl> 0.000000, 0.000000, 1.941849, 0.000000, 1.941849, 0.00…\n$ HSPC_008 <dbl> 0.000000, 1.395221, 0.000000, 0.000000, 0.000000, 0.00…\n$ HSPC_009 <dbl> 0.000000, 1.684562, 2.934193, 0.000000, 5.635680, 0.00…\n$ HSPC_011 <dbl> 1.237406, 0.000000, 2.685036, 0.000000, 1.237406, 2.34…\n$ HSPC_012 <dbl> 0.000000, 0.000000, 2.517338, 0.000000, 7.378370, 1.74…\n$ HSPC_014 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.857888, 0.00…\n$ HSPC_015 <dbl> 0.000000, 2.398869, 0.000000, 7.717134, 4.177566, 2.39…\n$ HSPC_016 <dbl> 0.0000000, 0.9487581, 2.2390441, 0.0000000, 0.9487581,…\n$ HSPC_017 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.146951, 0.00…\n$ HSPC_018 <dbl> 0.000000, 1.248476, 2.701241, 0.000000, 8.633940, 1.24…\n$ HSPC_020 <dbl> 0.000000, 0.000000, 2.277106, 0.000000, 2.277106, 6.73…\n$ HSPC_021 <dbl> 0.000000, 0.000000, 2.996123, 0.000000, 8.043618, 2.49…\n$ HSPC_022 <dbl> 0.000000, 1.211071, 1.860045, 1.211071, 7.334868, 8.67…\n$ HSPC_023 <dbl> 0.000000, 0.000000, 1.539553, 0.000000, 9.321612, 1.53…\n$ HSPC_024 <dbl> 0.000000, 0.000000, 1.692191, 0.000000, 9.143541, 0.00…\n$ HSPC_025 <dbl> 1.571690, 0.000000, 3.152201, 8.738315, 1.571690, 0.00…\n$ HSPC_026 <dbl> 0.000000, 0.000000, 1.384631, 0.000000, 1.384631, 0.00…\n$ HSPC_027 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.9302884, 7.0772133,…\n$ HSPC_028 <dbl> 0.0000000, 0.8288203, 0.8288203, 0.0000000, 1.7349575,…\n$ HSPC_030 <dbl> 0.000000, 1.118674, 1.118674, 0.000000, 5.508301, 0.00…\n$ HSPC_031 <dbl> 0.0000000, 0.0000000, 2.9918679, 0.0000000, 9.6855069,…\n$ HSPC_033 <dbl> 0.000000, 0.000000, 1.303195, 0.000000, 1.303195, 0.00…\n$ HSPC_034 <dbl> 0.0000000, 1.3093499, 2.6262158, 0.7983817, 8.3884282,…\n$ HSPC_035 <dbl> 0.000000, 2.175011, 0.000000, 0.000000, 2.649348, 0.00…\n$ HSPC_036 <dbl> 0.0000000, 0.0000000, 2.1381879, 5.5782959, 8.5678887,…\n$ HSPC_037 <dbl> 0.000000, 1.516975, 2.239950, 0.000000, 3.365905, 0.00…\n$ HSPC_038 <dbl> 0.000000, 0.000000, 1.364250, 1.364250, 0.000000, 0.00…\n$ HSPC_040 <dbl> 0.0000000, 1.5383598, 1.9474646, 9.3621654, 8.6231804,…\n$ HSPC_041 <dbl> 0.000000, 0.000000, 2.579199, 2.579199, 6.743574, 0.00…\n$ HSPC_042 <dbl> 0.0000000, 1.4051125, 1.7960064, 0.0000000, 0.8672577,…\n$ HSPC_043 <dbl> 0.000000, 0.000000, 3.553494, 1.341668, 6.491814, 7.93…\n$ HSPC_044 <dbl> 0.000000, 0.000000, 2.582992, 0.000000, 0.000000, 0.00…\n$ HSPC_045 <dbl> 0.000000, 0.000000, 1.346994, 0.000000, 0.000000, 2.03…\n$ HSPC_046 <dbl> 0.0000000, 0.8452114, 1.3746533, 0.0000000, 6.7513398,…\n$ HSPC_047 <dbl> 0.000000, 8.428296, 4.239537, 0.000000, 3.552610, 2.19…\n$ HSPC_048 <dbl> 0.000000, 1.108133, 1.727411, 8.235885, 6.865010, 2.98…\n$ HSPC_049 <dbl> 0.000000, 0.000000, 2.394418, 6.252069, 1.274540, 1.94…\n$ HSPC_050 <dbl> 0.000000, 0.000000, 3.086078, 0.000000, 6.466053, 0.00…\n$ HSPC_051 <dbl> 0.0000000, 0.0000000, 1.9634358, 0.0000000, 0.0000000,…\n$ HSPC_052 <dbl> 0.000000, 7.749301, 2.709016, 1.509063, 1.509063, 0.00…\n$ HSPC_053 <dbl> 0.0000000, 0.0000000, 2.4796614, 0.0000000, 5.3728144,…\n$ HSPC_054 <dbl> 0.000000, 0.000000, 1.158501, 0.000000, 2.231666, 1.15…\n$ HSPC_055 <dbl> 0.000000, 1.797473, 3.075245, 0.000000, 3.446842, 0.00…\n$ HSPC_056 <dbl> 0.000000, 2.016637, 3.720889, 0.000000, 8.325851, 6.21…\n$ HSPC_057 <dbl> 0.000000, 0.000000, 1.847003, 0.000000, 8.239896, 7.42…\n$ HSPC_058 <dbl> 1.398955, 0.000000, 3.199362, 0.000000, 2.916174, 0.00…\n$ HSPC_060 <dbl> 0.000000, 1.080287, 2.118815, 0.000000, 6.512085, 1.08…\n$ HSPC_061 <dbl> 0.000000, 1.725558, 2.985726, 0.000000, 1.725558, 2.48…\n$ HSPC_062 <dbl> 0.000000, 0.000000, 2.211545, 0.000000, 0.000000, 0.00…\n$ HSPC_063 <dbl> 0.000000, 1.514955, 2.237502, 0.000000, 1.514955, 2.23…\n$ HSPC_064 <dbl> 0.000000, 0.000000, 1.718347, 0.000000, 8.222925, 1.10…\n$ HSPC_065 <dbl> 0.0000000, 0.0000000, 0.9703625, 0.0000000, 2.2742114,…\n$ HSPC_066 <dbl> 0.000000, 0.000000, 2.564196, 5.716981, 9.399772, 1.78…\n$ HSPC_067 <dbl> 0.000000, 0.000000, 2.147724, 0.000000, 9.138862, 5.93…\n$ HSPC_068 <dbl> 0.000000, 1.870770, 2.658681, 0.000000, 0.000000, 0.00…\n$ HSPC_069 <dbl> 0.000000, 1.116169, 1.737865, 7.866496, 3.791268, 8.80…\n$ HSPC_070 <dbl> 0.0000000, 0.0000000, 1.4430580, 0.8949015, 4.5972951,…\n$ HSPC_071 <dbl> 0.0000000, 0.9271506, 0.0000000, 0.0000000, 1.8892984,…\n$ HSPC_072 <dbl> 1.744738, 0.000000, 0.000000, 0.000000, 7.077098, 0.00…\n$ HSPC_073 <dbl> 0.000000, 0.000000, 4.134335, 0.000000, 3.387315, 0.00…\n$ HSPC_074 <dbl> 0.000000, 0.000000, 1.703855, 0.000000, 3.326366, 1.70…\n$ HSPC_075 <dbl> 1.674178, 0.000000, 4.030303, 0.000000, 9.381928, 8.86…\n$ HSPC_076 <dbl> 0.000000, 0.000000, 2.821284, 0.000000, 6.891389, 1.14…\n$ HSPC_077 <dbl> 0.000000, 0.000000, 1.114630, 0.000000, 8.460932, 0.00…\n$ HSPC_078 <dbl> 0.000000, 2.209396, 4.387948, 0.000000, 6.627462, 2.68…\n$ HSPC_079 <dbl> 0.000000, 0.000000, 4.095390, 0.000000, 2.076810, 0.00…\n$ HSPC_080 <dbl> 0.000000, 1.012596, 2.828881, 0.000000, 1.012596, 0.00…\n$ HSPC_081 <dbl> 0.0000000, 1.2652832, 1.2652832, 0.0000000, 5.3328809,…\n$ HSPC_082 <dbl> 0.000000, 0.000000, 4.421943, 0.000000, 1.970627, 0.00…\n$ HSPC_083 <dbl> 1.895620, 0.000000, 4.114776, 0.000000, 10.864775, 7.8…\n$ HSPC_084 <dbl> 0.000000, 0.000000, 3.216417, 0.000000, 4.036024, 0.00…\n$ HSPC_085 <dbl> 0.000000, 0.000000, 1.934463, 0.000000, 0.000000, 1.93…\n$ HSPC_087 <dbl> 0.000000, 0.000000, 1.859256, 0.000000, 8.387078, 7.44…\n$ HSPC_088 <dbl> 0.000000, 0.000000, 2.091761, 1.061818, 1.666812, 0.00…\n$ HSPC_089 <dbl> 0.000000, 0.000000, 1.542804, 0.000000, 5.429591, 0.00…\n$ HSPC_090 <dbl> 0.0000000, 0.9583485, 0.0000000, 0.9583485, 7.8106423,…\n$ HSPC_094 <dbl> 0.000000, 1.001307, 3.002286, 0.000000, 1.001307, 0.00…\n$ HSPC_095 <dbl> 0.000000, 2.055264, 3.768162, 0.000000, 5.497828, 0.00…\n$ HSPC_096 <dbl> 0.000000, 0.000000, 3.020317, 0.000000, 4.694719, 0.00…\n$ HSPC_098 <dbl> 0.000000, 0.000000, 4.033084, 0.000000, 3.353704, 0.00…\n$ HSPC_099 <dbl> 0.000000, 0.000000, 1.355566, 8.533241, 1.355566, 7.54…\n$ HSPC_100 <dbl> 0.000000, 1.117108, 2.504994, 0.000000, 1.117108, 0.00…\n$ HSPC_101 <dbl> 0.000000, 1.007084, 3.182495, 0.000000, 6.058315, 0.00…\n$ HSPC_102 <dbl> 0.000000, 1.110882, 2.992525, 0.000000, 1.110882, 6.01…\n$ HSPC_103 <dbl> 0.000000, 0.000000, 3.444022, 3.444022, 3.444022, 6.84…\n$ HSPC_104 <dbl> 0.000000, 0.000000, 2.720362, 0.000000, 5.326705, 0.00…\n$ HSPC_105 <dbl> 0.000000, 0.000000, 2.953351, 0.000000, 2.128535, 0.00…\n$ HSPC_106 <dbl> 0.000000, 0.000000, 2.917053, 8.830809, 10.056331, 0.0…\n$ HSPC_107 <dbl> 0.000000, 1.539035, 2.266637, 1.539035, 2.266637, 0.00…\n$ HSPC_108 <dbl> 0.000000, 0.000000, 2.342599, 1.237188, 9.967793, 0.00…\n$ HSPC_109 <dbl> 0.000000, 1.595174, 3.183524, 1.595174, 6.584986, 0.00…\n$ HSPC_110 <dbl> 0.000000, 0.000000, 2.667372, 0.000000, 9.520268, 2.66…\n$ HSPC_111 <dbl> 0.000000, 1.495539, 2.691330, 3.780086, 1.495539, 0.00…\n$ HSPC_114 <dbl> 0.0000000, 0.0000000, 2.1872025, 0.0000000, 0.9172819,…\n$ HSPC_115 <dbl> 0.000000, 2.348960, 4.119213, 6.801590, 2.348960, 2.34…\n$ HSPC_117 <dbl> 0.000000, 0.000000, 2.662801, 0.000000, 2.662801, 0.00…\n$ HSPC_118 <dbl> 0.000000, 0.000000, 2.731892, 0.000000, 7.192924, 1.26…\n$ HSPC_119 <dbl> 0.000000, 2.432480, 3.827272, 0.000000, 2.432480, 8.53…\n$ HSPC_120 <dbl> 0.000000, 0.000000, 1.186672, 0.000000, 1.186672, 9.49…\n$ HSPC_121 <dbl> 0.000000, 0.000000, 4.264196, 0.000000, 2.822082, 0.00…\n$ HSPC_122 <dbl> 0.000000, 1.435059, 2.140080, 0.000000, 0.000000, 0.00…\n$ HSPC_123 <dbl> 0.000000, 0.000000, 2.244368, 0.000000, 6.093587, 0.00…\n$ HSPC_125 <dbl> 0.000000, 0.000000, 1.285018, 0.000000, 5.553951, 6.70…\n$ HSPC_126 <dbl> 0.0000000, 0.0000000, 1.5017883, 0.0000000, 2.6995085,…\n$ HSPC_127 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.822566, 0.00…\n$ HSPC_130 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.443009, 0.00…\n$ HSPC_131 <dbl> 0.000000, 0.000000, 2.384852, 0.000000, 4.161211, 0.00…\n$ HSPC_132 <dbl> 0.0000000, 0.7827224, 0.4438051, 0.0000000, 1.6604369,…\n$ HSPC_133 <dbl> 2.234359, 3.072289, 0.000000, 2.234359, 8.065004, 0.00…\n$ HSPC_134 <dbl> 0.000000, 2.768568, 0.000000, 0.000000, 6.641594, 0.00…\n$ HSPC_135 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.600652, 8.08…\n$ HSPC_136 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.962552, 0.00…\n$ HSPC_138 <dbl> 0.0000000, 0.7062338, 0.7062338, 0.0000000, 1.8180968,…\n$ HSPC_139 <dbl> 0.000000, 0.000000, 4.345514, 0.000000, 0.000000, 4.09…\n$ HSPC_140 <dbl> 0.000000, 0.000000, 4.660038, 0.000000, 0.000000, 0.00…\n$ HSPC_141 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.543589, 0.00…\n$ HSPC_142 <dbl> 0.000000, 0.000000, 2.523431, 0.000000, 0.000000, 0.00…\n$ HSPC_143 <dbl> 0.000000, 1.958674, 2.760027, 0.000000, 8.379422, 0.00…\n$ HSPC_144 <dbl> 0.000000, 0.000000, 1.075288, 0.000000, 0.000000, 0.00…\n$ HSPC_146 <dbl> 0.000000, 2.277948, 2.277948, 3.120975, 1.548407, 0.00…\n$ HSPC_148 <dbl> 0.000000, 0.000000, 2.428270, 0.000000, 0.000000, 0.00…\n$ HSPC_149 <dbl> 0.000000, 0.000000, 0.000000, 7.370990, 9.039081, 0.00…\n$ HSPC_151 <dbl> 0.000000, 0.000000, 1.942220, 0.000000, 6.477871, 0.00…\n$ HSPC_152 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.948497, 0.00…\n$ HSPC_153 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…\n$ HSPC_154 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 6.567172, 0.00…\n$ HSPC_155 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 5.981102, 2.73…\n$ HSPC_156 <dbl> 0.0000000, 0.5544683, 0.5544683, 0.0000000, 1.2665659,…\n$ HSPC_157 <dbl> 0.000000, 1.992960, 3.313476, 0.000000, 6.056835, 2.79…\n$ HSPC_158 <dbl> 0.000000, 1.681202, 1.681202, 0.000000, 2.436668, 1.68…\n$ HSPC_159 <dbl> 0.000000, 0.000000, 0.000000, 4.189894, 9.099914, 9.05…\n$ HSPC_161 <dbl> 0.000000, 0.000000, 1.701367, 0.000000, 3.323133, 8.08…\n$ HSPC_162 <dbl> 0.715249, 0.715249, 1.191268, 0.000000, 6.603848, 1.54…\n$ HSPC_164 <dbl> 0.000000, 1.780116, 4.173269, 0.000000, 3.053739, 0.00…\n$ HSPC_165 <dbl> 0.000000, 0.000000, 2.521243, 0.000000, 1.752913, 2.52…\n$ HSPC_166 <dbl> 0.000000, 1.680351, 3.830900, 0.000000, 2.435661, 0.00…\n$ HSPC_168 <dbl> 0.000000, 0.000000, 2.587724, 0.000000, 1.587172, 0.00…\n$ HSPC_169 <dbl> 0.000000, 2.231654, 2.231654, 0.000000, 2.840202, 1.15…\n$ HSPC_170 <dbl> 0.000000, 2.323845, 3.702651, 0.000000, 0.000000, 0.00…\n$ HSPC_171 <dbl> 0.000000, 2.249267, 0.000000, 0.000000, 6.986101, 0.00…\n$ HSPC_172 <dbl> 0.0000000, 0.0000000, 1.2663678, 1.6361385, 3.8432543,…\n$ HSPC_173 <dbl> 0.000000, 0.000000, 2.202687, 0.000000, 2.202687, 0.00…\n$ HSPC_174 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 1.25…\n$ HSPC_175 <dbl> 0.000000, 0.000000, 2.214143, 0.000000, 3.959792, 1.49…\n$ HSPC_176 <dbl> 0.000000, 0.000000, 2.023933, 0.000000, 6.539273, 2.02…\n$ HSPC_177 <dbl> 0.000000, 1.971046, 1.971046, 0.000000, 3.664721, 0.00…\n$ HSPC_178 <dbl> 0.000000, 1.002574, 1.002574, 0.000000, 8.129245, 0.00…\n$ HSPC_179 <dbl> 0.0000000, 0.8360365, 1.7464921, 0.0000000, 2.3003274,…\n$ HSPC_180 <dbl> 0.000000, 0.000000, 2.753903, 0.000000, 6.624564, 6.08…\n$ HSPC_181 <dbl> 0.000000, 0.000000, 5.739487, 0.000000, 5.969896, 0.00…\n$ HSPC_182 <dbl> 0.000000, 0.000000, 3.778793, 0.000000, 3.245451, 3.24…\n$ HSPC_183 <dbl> 0.000000, 0.000000, 3.206129, 1.020477, 7.802033, 0.00…\n$ HSPC_185 <dbl> 0.000000, 0.000000, 5.514029, 6.498157, 9.703008, 0.00…\n$ HSPC_186 <dbl> 0.000000, 1.605849, 9.300294, 0.000000, 3.729280, 0.00…\n$ HSPC_187 <dbl> 0.000000, 0.000000, 3.016892, 0.000000, 8.206576, 2.18…\n$ HSPC_189 <dbl> 2.651507, 1.465231, 0.000000, 0.000000, 1.465231, 2.17…\n$ HSPC_190 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…\n$ HSPC_191 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.406826, 0.00…\n$ HSPC_192 <dbl> 0.000000, 0.000000, 2.111369, 0.000000, 0.000000, 1.41…\n$ HSPC_193 <dbl> 0.0000000, 2.2721907, 2.9455106, 0.0000000, 9.1712813,…\n$ HSPC_195 <dbl> 0.0000000, 0.0000000, 2.4446955, 0.0000000, 1.4738612,…\n$ HSPC_196 <dbl> 0.000000, 1.379440, 2.888830, 0.000000, 0.000000, 0.00…\n$ HSPC_198 <dbl> 2.155150, 0.000000, 3.506202, 1.105265, 1.105265, 0.00…\n$ HSPC_199 <dbl> 1.676720, 0.000000, 2.102827, 4.774209, 2.102827, 1.06…\n$ HSPC_200 <dbl> 1.572132, 2.306518, 0.000000, 0.000000, 2.790837, 1.57…\n$ HSPC_202 <dbl> 1.3178909, 0.0000000, 0.0000000, 0.0000000, 1.6957804,…\n$ HSPC_203 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…\n$ HSPC_204 <dbl> 0.000000, 1.342435, 1.342435, 0.000000, 2.487324, 1.34…\n$ HSPC_205 <dbl> 0.000000, 1.996780, 0.000000, 0.000000, 2.803674, 4.95…\n$ HSPC_206 <dbl> 1.076411, 0.000000, 1.076411, 0.000000, 1.076411, 0.00…\n$ HSPC_207 <dbl> 0.000000, 0.000000, 1.482035, 0.000000, 9.592779, 0.00…\n$ HSPC_208 <dbl> 0.000000, 0.000000, 3.104133, 0.000000, 2.262852, 2.26…\n$ HSPC_210 <dbl> 0.000000, 1.021329, 1.021329, 0.000000, 1.613332, 1.02…\n$ HSPC_211 <dbl> 0.000000, 0.000000, 3.567889, 1.351210, 6.755381, 2.03…\n$ HSPC_212 <dbl> 0.000000, 0.000000, 2.570024, 0.000000, 0.000000, 0.00…\n$ HSPC_213 <dbl> 0.000000, 1.935211, 1.935211, 0.000000, 1.270332, 1.27…\n$ HSPC_214 <dbl> 1.4766124, 0.0000000, 1.8775155, 0.0000000, 1.4766124,…\n$ HSPC_215 <dbl> 0.000000, 0.000000, 3.551326, 0.000000, 2.403099, 6.60…\n$ HSPC_216 <dbl> 0.0000000, 1.3146495, 0.8021597, 0.8021597, 6.2917837,…\n$ HSPC_218 <dbl> 0.000000, 2.567330, 3.440186, 0.000000, 3.980149, 0.00…\n$ HSPC_219 <dbl> 0.000000, 1.620715, 2.040160, 0.000000, 2.364726, 0.00…\n$ HSPC_220 <dbl> 0.000000, 0.000000, 4.645420, 0.000000, 6.439808, 0.00…\n$ HSPC_221 <dbl> 0.000000, 1.932925, 3.617433, 1.268520, 8.054842, 1.26…\n$ HSPC_222 <dbl> 0.000000, 2.630291, 3.270905, 0.000000, 2.157369, 1.44…\n$ HSPC_223 <dbl> 0.000000, 3.195293, 3.195293, 0.000000, 3.195293, 0.00…\n$ HSPC_224 <dbl> 0.000000, 0.000000, 3.235910, 0.000000, 2.381448, 3.23…\n$ HSPC_225 <dbl> 0.000000, 1.892444, 0.000000, 0.000000, 0.000000, 1.89…\n$ HSPC_227 <dbl> 0.000000, 0.000000, 2.269803, 0.000000, 1.541657, 0.00…\n$ HSPC_228 <dbl> 0.000000, 0.000000, 2.193480, 0.000000, 2.193480, 3.02…\n$ HSPC_229 <dbl> 0.000000, 2.483864, 4.276180, 0.000000, 3.348781, 0.00…\n$ HSPC_230 <dbl> 0.000000, 0.000000, 2.376256, 0.000000, 9.125464, 6.25…\n$ HSPC_231 <dbl> 0.000000, 2.442191, 3.838346, 0.000000, 10.045009, 2.4…\n$ HSPC_232 <dbl> 0.000000, 1.868824, 3.834078, 0.000000, 4.080153, 0.00…\n$ HSPC_233 <dbl> 0.000000, 0.000000, 3.219022, 0.000000, 3.219022, 0.00…\n$ HSPC_235 <dbl> 0.000000, 0.000000, 6.066406, 0.000000, 3.382760, 0.00…\n$ HSPC_236 <dbl> 0.000000, 0.000000, 3.747550, 0.000000, 5.733689, 3.08…\n$ HSPC_237 <dbl> 0.000000, 2.058995, 2.058995, 0.000000, 2.874584, 2.05…\n$ HSPC_239 <dbl> 0.000000, 2.206777, 3.951001, 0.000000, 3.951001, 0.00…\n$ HSPC_240 <dbl> 0.0000000, 0.0000000, 1.8347816, 0.0000000, 1.4390643,…\n$ HSPC_243 <dbl> 0.000000, 1.118272, 0.000000, 0.000000, 1.740598, 0.00…\n$ HSPC_244 <dbl> 0.7871557, 0.7871557, 0.7871557, 0.0000000, 8.1826024,…\n$ HSPC_245 <dbl> 0.000000, 1.459488, 0.000000, 0.000000, 1.459488, 0.00…\n$ HSPC_246 <dbl> 0.000000, 0.000000, 1.629406, 0.000000, 0.000000, 0.00…\n$ HSPC_247 <dbl> 0.000000, 0.000000, 4.129612, 0.000000, 6.233964, 3.74…\n$ HSPC_248 <dbl> 0.000000, 0.000000, 3.844645, 0.000000, 0.000000, 1.69…\n$ HSPC_249 <dbl> 0.000000, 0.000000, 1.595836, 0.000000, 1.595836, 2.33…\n$ HSPC_250 <dbl> 0.000000, 1.277977, 3.022356, 0.000000, 4.572877, 7.32…\n$ HSPC_251 <dbl> 0.0000000, 0.0000000, 2.2758529, 0.0000000, 1.9568480,…\n$ HSPC_253 <dbl> 0.000000, 0.000000, 1.265413, 0.000000, 8.470854, 0.00…\n$ HSPC_254 <dbl> 0.000000, 2.017054, 2.826829, 2.017054, 5.231539, 0.00…\n$ HSPC_255 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.8624297,…\n$ HSPC_256 <dbl> 0.824835, 1.346341, 9.308939, 0.000000, 1.346341, 0.82…\n$ HSPC_257 <dbl> 0.000000, 0.000000, 1.897882, 0.000000, 6.591455, 0.00…\n$ HSPC_258 <dbl> 0.0000000, 0.8526372, 0.0000000, 0.0000000, 7.0828772,…\n$ HSPC_261 <dbl> 0.0000000, 0.9300646, 0.9300646, 0.0000000, 1.4909411,…\n$ HSPC_263 <dbl> 1.537503, 0.000000, 1.537503, 0.000000, 0.000000, 2.26…\n$ HSPC_264 <dbl> 0.000000, 1.425741, 1.425741, 0.000000, 0.000000, 8.70…\n$ HSPC_265 <dbl> 0.000000, 2.672373, 4.308280, 0.000000, 3.554532, 7.62…\n$ HSPC_266 <dbl> 0.000000, 0.000000, 3.498974, 0.000000, 8.164818, 2.62…\n$ HSPC_267 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.9510360,…\n$ HSPC_268 <dbl> 0.0000000, 0.7786889, 3.0278105, 0.0000000, 1.9494543,…\n$ HSPC_269 <dbl> 0.0000000, 0.0000000, 1.9511222, 0.0000000, 2.5642396,…\n$ HSPC_270 <dbl> 0.0000000, 0.0000000, 2.2633366, 0.0000000, 6.3366603,…\n$ HSPC_271 <dbl> 1.351781, 1.351781, 4.404598, 1.351781, 2.037221, 6.96…\n$ HSPC_274 <dbl> 0.000000, 0.000000, 2.990962, 0.000000, 9.428474, 8.26…\n$ HSPC_275 <dbl> 0.5251597, 6.3066729, 1.8617796, 0.0000000, 6.2986480,…\n$ HSPC_276 <dbl> 0.000000, 1.155802, 2.836006, 0.000000, 0.000000, 8.56…\n$ HSPC_278 <dbl> 0.000000, 1.185387, 2.881903, 0.000000, 2.269850, 3.64…\n$ HSPC_279 <dbl> 3.563514, 1.487393, 2.204027, 0.000000, 7.109467, 1.48…\n$ HSPC_280 <dbl> 0.000000, 0.000000, 2.837358, 0.000000, 3.939531, 0.00…\n$ HSPC_281 <dbl> 3.488013, 0.000000, 3.488013, 0.000000, 3.488013, 0.00…\n$ HSPC_282 <dbl> 0.0000000, 1.1722813, 2.0476063, 0.0000000, 1.1722813,…\n$ HSPC_283 <dbl> 0.000000, 3.590649, 4.529495, 1.911441, 1.911441, 0.00…\n$ HSPC_285 <dbl> 0.0000000, 0.0000000, 3.0705949, 0.0000000, 8.1491892,…\n$ HSPC_286 <dbl> 0.000000, 0.000000, 2.259352, 0.000000, 2.259352, 0.00…\n$ HSPC_287 <dbl> 0.000000, 1.048672, 3.408389, 1.649500, 1.649500, 0.00…\n$ HSPC_288 <dbl> 0.0000000, 1.3879010, 3.0490024, 0.0000000, 8.9178718,…\n$ HSPC_289 <dbl> 0.000000, 0.000000, 3.641902, 0.000000, 3.940529, 0.00…\n$ HSPC_290 <dbl> 0.000000, 0.000000, 2.594545, 0.000000, 7.607623, 2.59…\n$ HSPC_291 <dbl> 0.000000, 1.175842, 1.175842, 0.000000, 9.326333, 0.00…\n$ HSPC_292 <dbl> 0.000000, 0.000000, 2.804302, 0.000000, 5.422785, 8.43…\n$ HSPC_293 <dbl> 1.077036, 0.000000, 2.711161, 7.608787, 4.047517, 1.07…\n$ HSPC_294 <dbl> 0.0000000, 0.0000000, 0.9161348, 0.0000000, 1.8723003,…\n$ HSPC_295 <dbl> 0.0000000, 1.8799200, 3.0264680, 0.9210673, 4.2380463,…\n$ HSPC_296 <dbl> 0.000000, 2.157001, 2.157001, 2.157001, 5.287566, 0.00…\n$ HSPC_297 <dbl> 0.000000, 1.687785, 2.444464, 0.000000, 0.000000, 0.00…\n$ HSPC_298 <dbl> 0.0000000, 0.7784226, 2.4036840, 0.0000000, 2.4036840,…\n$ HSPC_299 <dbl> 0.000000, 1.099428, 1.716067, 0.000000, 8.068194, 2.74…\n$ HSPC_300 <dbl> 0.0000000, 0.0000000, 2.0342975, 0.0000000, 2.0342975,…\n$ HSPC_301 <dbl> 0.000000, 3.073359, 6.232145, 0.000000, 4.938720, 0.00…\n$ HSPC_302 <dbl> 3.078836, 0.000000, 4.545226, 0.000000, 3.078836, 3.07…\n$ HSPC_303 <dbl> 0.000000, 0.000000, 3.009431, 0.000000, 7.544693, 0.00…\n$ HSPC_304 <dbl> 0.0000000, 1.5484583, 2.7602490, 0.0000000, 5.4055991,…\n$ HSPC_305 <dbl> 0.000000, 0.000000, 2.558256, 0.000000, 4.669580, 1.15…\n$ HSPC_306 <dbl> 0.000000, 0.000000, 3.185666, 0.000000, 5.689777, 3.33…\n$ HSPC_307 <dbl> 0.000000, 1.976472, 4.777034, 0.000000, 3.293648, 2.78…\n$ HSPC_308 <dbl> 0.000000, 0.000000, 3.975296, 6.548101, 3.526101, 0.00…\n$ HSPC_309 <dbl> 0.000000, 1.804026, 3.455193, 0.000000, 3.083349, 1.80…\n$ HSPC_310 <dbl> 1.743404, 0.000000, 2.510071, 0.000000, 2.510071, 1.74…\n$ HSPC_312 <dbl> 0.0000000, 1.4203264, 1.8133978, 0.0000000, 7.5148795,…\n$ HSPC_313 <dbl> 0.000000, 1.591589, 4.096808, 1.591589, 2.329876, 0.00…\n$ HSPC_314 <dbl> 0.000000, 0.000000, 3.527058, 1.634863, 3.236083, 7.90…\n$ HSPC_315 <dbl> 0.000000, 2.261631, 2.261631, 2.261631, 3.630638, 0.00…\n$ HSPC_317 <dbl> 0.000000, 2.335038, 3.715556, 0.000000, 2.335038, 0.00…\n$ HSPC_318 <dbl> 0.000000, 0.000000, 3.357051, 0.000000, 0.000000, 0.00…\n$ HSPC_320 <dbl> 0.000000, 0.000000, 3.189910, 2.339927, 3.479868, 3.18…\n$ HSPC_321 <dbl> 0.000000, 1.191188, 1.191188, 0.000000, 2.616741, 1.19…\n$ HSPC_322 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.9762562,…\n$ HSPC_323 <dbl> 0.0000000, 0.0000000, 0.9890293, 0.0000000, 2.5666314,…\n$ HSPC_324 <dbl> 0.000000, 1.088036, 2.728840, 0.000000, 2.728840, 0.00…\n$ HSPC_325 <dbl> 1.428402, 1.428402, 4.810635, 0.000000, 2.131908, 0.00…\n$ HSPC_326 <dbl> 0.000000, 0.000000, 5.321205, 0.000000, 6.303048, 0.00…\n$ HSPC_327 <dbl> 0.0000000, 0.6422346, 2.7240929, 0.0000000, 3.1457469,…\n$ HSPC_328 <dbl> 0.000000, 0.000000, 3.620927, 0.000000, 2.733670, 1.27…\n$ HSPC_329 <dbl> 1.954249, 0.000000, 3.800965, 0.000000, 1.954249, 0.00…\n$ HSPC_330 <dbl> 1.870221, 0.000000, 3.695045, 0.000000, 2.317254, 2.65…\n$ HSPC_331 <dbl> 0.000000, 0.000000, 2.953080, 0.000000, 2.953080, 0.00…\n$ HSPC_332 <dbl> 0.000000, 1.044806, 2.883335, 0.000000, 6.444771, 1.04…\n$ HSPC_333 <dbl> 0.8059525, 1.3199644, 1.3199644, 0.0000000, 1.9975449,…\n$ HSPC_334 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.931089, 0.00…\n$ HSPC_335 <dbl> 0.000000, 0.000000, 2.751133, 0.000000, 7.599859, 0.00…\n$ HSPC_336 <dbl> 0.000000, 0.000000, 1.959737, 0.000000, 0.000000, 3.27…\n$ HSPC_337 <dbl> 0.000000, 0.000000, 5.147478, 0.000000, 4.187618, 0.00…\n$ HSPC_338 <dbl> 0.0000000, 0.0000000, 10.1024666, 0.0000000, 7.9043216…\n$ HSPC_339 <dbl> 0.0000000, 0.9599543, 2.5175886, 5.1304754, 7.6196067,…\n$ HSPC_341 <dbl> 0.0000000, 0.0000000, 2.2497917, 7.5891885, 2.2497917,…\n$ HSPC_342 <dbl> 0.0000000, 0.7849552, 1.4893359, 0.4452176, 8.7833506,…\n$ HSPC_343 <dbl> 0.0000000, 0.7869497, 0.7869497, 0.7869497, 1.9640992,…\n$ HSPC_344 <dbl> 0.000000, 1.425890, 1.425890, 0.000000, 8.886625, 2.59…\n$ HSPC_345 <dbl> 0.000000, 0.000000, 2.093666, 1.397341, 0.000000, 1.39…\n$ HSPC_346 <dbl> 0.000000, 0.000000, 1.637347, 0.000000, 1.637347, 1.03…\n$ HSPC_348 <dbl> 0.000000, 1.733815, 2.768747, 7.794259, 2.166424, 1.73…\n$ HSPC_349 <dbl> 0.000000, 0.000000, 3.434533, 0.000000, 2.783890, 0.00…\n$ HSPC_350 <dbl> 0.000000, 0.000000, 4.536066, 5.820193, 0.000000, 0.00…\n$ HSPC_351 <dbl> 0.000000, 1.910297, 2.704369, 0.000000, 7.827990, 1.91…\n$ HSPC_352 <dbl> 0.000000, 0.000000, 1.855727, 0.000000, 0.000000, 0.00…\n$ HSPC_353 <dbl> 0.000000, 0.000000, 0.899194, 0.000000, 0.000000, 0.89…\n$ HSPC_354 <dbl> 0.000000, 2.331123, 4.869071, 0.000000, 7.191315, 0.00…\n$ HSPC_356 <dbl> 0.000000, 0.000000, 4.138465, 0.000000, 1.914037, 0.00…\n$ HSPC_358 <dbl> 0.000000, 1.322769, 3.323190, 1.322769, 3.524842, 0.00…\n$ HSPC_359 <dbl> 0.0000000, 0.9071742, 1.8584210, 0.0000000, 8.1305324,…\n$ HSPC_360 <dbl> 0.000000, 0.000000, 2.544055, 0.000000, 9.351724, 0.00…\n$ HSPC_361 <dbl> 0.000000, 0.000000, 4.073667, 0.000000, 9.554073, 2.05…\n$ HSPC_362 <dbl> 0.000000, 0.000000, 7.448528, 0.000000, 4.768353, 0.00…\n$ HSPC_363 <dbl> 0.000000, 0.000000, 2.749745, 8.987973, 7.432788, 7.45…\n$ HSPC_365 <dbl> 0.000000, 0.000000, 4.965616, 2.594802, 9.358295, 8.09…\n$ HSPC_367 <dbl> 0.000000, 2.528168, 4.327223, 0.000000, 6.909409, 5.10…\n$ HSPC_368 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.951178, 0.00…\n$ HSPC_370 <dbl> 0.000000, 0.000000, 1.307598, 0.000000, 0.797134, 0.00…\n$ HSPC_371 <dbl> 0.000000, 1.257081, 1.257081, 0.000000, 1.257081, 1.25…\n$ HSPC_372 <dbl> 0.000000, 0.000000, 2.221252, 0.000000, 8.278550, 0.00…\n$ HSPC_373 <dbl> 0.000000, 0.000000, 4.730281, 2.884763, 3.783634, 6.93…\n$ HSPC_374 <dbl> 0.000000, 0.000000, 2.862633, 0.000000, 10.146380, 7.7…\n$ HSPC_376 <dbl> 0.000000, 0.000000, 3.171475, 8.276931, 2.537496, 0.00…\n$ HSPC_377 <dbl> 0.000000, 0.000000, 3.297929, 0.000000, 8.087149, 1.30…\n$ HSPC_380 <dbl> 1.219391, 0.000000, 2.934029, 1.219391, 3.165253, 1.21…\n$ HSPC_382 <dbl> 0.0000000, 0.0000000, 1.5486445, 0.0000000, 9.8626042,…\n$ HSPC_383 <dbl> 0.000000, 0.000000, 3.684053, 0.000000, 2.521370, 1.75…\n$ HSPC_386 <dbl> 0.000000, 1.412425, 2.934964, 3.837412, 2.112261, 4.60…\n$ HSPC_387 <dbl> 0.000000, 0.000000, 2.464120, 0.000000, 2.004298, 2.00…\n$ HSPC_388 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.0965225,…\n$ HSPC_389 <dbl> 0.000000, 0.000000, 8.902540, 0.000000, 1.263307, 0.00…\n$ HSPC_390 <dbl> 0.000000, 1.531571, 3.386125, 7.733304, 3.625976, 0.00…\n$ HSPC_391 <dbl> 0.000000, 1.423235, 2.125560, 0.000000, 2.125560, 0.00…\n$ HSPC_392 <dbl> 1.166291, 0.000000, 2.242760, 0.000000, 0.000000, 1.16…\n$ HSPC_393 <dbl> 0.000000, 1.110847, 8.026679, 0.000000, 7.310363, 1.11…\n$ HSPC_395 <dbl> 0.000000, 0.000000, 1.923747, 0.000000, 1.923747, 1.26…\n$ HSPC_396 <dbl> 0.0000000, 0.9279096, 2.8713748, 0.0000000, 8.4108606,…\n$ HSPC_398 <dbl> 0.000000, 1.052478, 2.405000, 0.000000, 1.654517, 7.24…\n$ HSPC_399 <dbl> 0.000000, 0.000000, 1.198818, 0.000000, 9.267652, 0.00…\n$ HSPC_400 <dbl> 0.000000, 0.000000, 2.368347, 0.000000, 1.255700, 0.00…\n$ HSPC_402 <dbl> 0.000000, 0.000000, 2.004052, 0.000000, 8.950833, 8.75…\n$ HSPC_403 <dbl> 0.000000, 0.000000, 2.797093, 0.000000, 0.000000, 0.00…\n$ HSPC_404 <dbl> 0.0000000, 0.0000000, 3.7817437, 0.0000000, 4.2260011,…\n$ HSPC_405 <dbl> 0.000000, 1.099247, 1.715831, 0.000000, 1.099247, 5.31…\n$ HSPC_406 <dbl> 0.000000, 0.000000, 2.135083, 1.430987, 3.245124, 0.00…\n$ HSPC_407 <dbl> 0.0000000, 0.5650490, 1.5448568, 0.0000000, 3.8016573,…\n$ HSPC_408 <dbl> 0.0000000, 1.5700909, 10.4839578, 0.0000000, 8.0221142…\n$ HSPC_409 <dbl> 0.000000, 0.000000, 3.044399, 0.000000, 3.044399, 0.00…\n$ HSPC_410 <dbl> 0.904751, 0.000000, 1.854660, 0.000000, 5.165464, 0.00…\n$ HSPC_411 <dbl> 0.0000000, 0.0000000, 0.9059253, 0.0000000, 7.5067273,…\n$ HSPC_412 <dbl> 0.0000000, 0.0000000, 0.6614079, 8.5121379, 7.3252186,…\n$ HSPC_413 <dbl> 0.000000, 1.169683, 1.807043, 1.169683, 9.674198, 0.00…\n$ HSPC_415 <dbl> 0.000000, 0.000000, 5.273790, 0.000000, 3.093809, 0.00…\n$ HSPC_416 <dbl> 0.000000, 1.337731, 3.110643, 0.000000, 0.000000, 0.00…\n$ HSPC_417 <dbl> 0.0000000, 0.7221578, 1.5601978, 0.0000000, 1.2011916,…\n$ HSPC_418 <dbl> 0.000000, 0.000000, 2.348200, 0.000000, 1.241207, 0.00…\n$ HSPC_419 <dbl> 0.000000, 0.000000, 1.158302, 0.692403, 0.000000, 0.69…\n$ HSPC_420 <dbl> 0.0000000, 0.7442525, 1.5970870, 0.0000000, 1.5970870,…\n$ HSPC_421 <dbl> 0.000000, 0.000000, 3.682417, 0.000000, 1.985374, 0.00…\n$ HSPC_422 <dbl> 0.000000, 0.000000, 1.630076, 1.033967, 1.630076, 7.51…\n$ HSPC_423 <dbl> 0.0000000, 0.0000000, 1.4797402, 0.0000000, 1.1326799,…\n$ HSPC_424 <dbl> 0.000000, 1.267474, 2.728919, 0.000000, 3.439860, 0.00…\n$ HSPC_425 <dbl> 0.000000, 0.000000, 1.016015, 0.000000, 6.474436, 0.00…\n$ HSPC_426 <dbl> 0.000000, 0.000000, 3.133563, 0.000000, 0.000000, 1.55…\n$ HSPC_427 <dbl> 0.000000, 2.152076, 4.835791, 0.000000, 7.220338, 0.00…\n$ HSPC_431 <dbl> 0.000000, 2.150174, 3.639547, 1.101833, 5.618926, 2.97…\n$ HSPC_432 <dbl> 0.000000, 0.000000, 1.949682, 0.000000, 3.261333, 4.18…\n$ HSPC_435 <dbl> 0.000000, 1.097512, 2.970692, 0.000000, 7.647274, 6.35…\n$ HSPC_436 <dbl> 0.0000000, 0.0000000, 1.1114827, 0.0000000, 5.7023734,…\n$ HSPC_440 <dbl> 0.000000, 1.385247, 1.385247, 0.000000, 2.545103, 5.12…\n$ HSPC_441 <dbl> 0.000000, 0.000000, 1.696075, 1.084126, 8.589432, 1.08…\n$ HSPC_442 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 3.6713853,…\n$ HSPC_443 <dbl> 0.000000, 1.546895, 2.276125, 0.000000, 2.276125, 1.54…\n$ HSPC_444 <dbl> 1.374089, 0.000000, 0.000000, 0.000000, 1.374089, 0.00…\n$ HSPC_446 <dbl> 0.000000, 2.439029, 1.683195, 0.000000, 1.683195, 1.68…\n$ HSPC_447 <dbl> 0.000000, 1.113089, 1.113089, 0.000000, 2.166474, 7.12…\n$ HSPC_448 <dbl> 0.000000, 1.139444, 0.000000, 0.000000, 9.257634, 0.00…\n$ HSPC_449 <dbl> 0.000000, 1.344056, 2.489522, 0.000000, 5.057946, 2.02…\n$ HSPC_450 <dbl> 0.000000, 0.000000, 2.979455, 0.000000, 5.551901, 0.00…\n$ HSPC_451 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.322231, 1.75…\n$ HSPC_453 <dbl> 0.0000000, 0.0000000, 1.4937200, 0.0000000, 2.4699783,…\n$ HSPC_454 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 9.1805553,…\n$ HSPC_455 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.204120, 0.00…\n$ HSPC_456 <dbl> 0.0000000, 0.0000000, 1.4363752, 0.0000000, 7.5943576,…\n$ HSPC_457 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.535876, 7.75…\n$ HSPC_459 <dbl> 0.000000, 1.282562, 1.282562, 1.282562, 9.214660, 1.95…\n$ HSPC_460 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.601133, 0.00…\n$ HSPC_461 <dbl> 0.000000, 0.000000, 0.000000, 7.294303, 7.224373, 0.00…\n$ HSPC_462 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 5.732804, 0.00…\n$ HSPC_463 <dbl> 0.000000, 0.000000, 1.566104, 0.725680, 0.000000, 0.00…\n$ HSPC_465 <dbl> 0.000000, 4.053991, 0.000000, 3.894882, 2.159925, 0.00…\n$ HSPC_466 <dbl> 0.000000, 1.163197, 0.000000, 0.000000, 3.274518, 0.00…\n$ HSPC_467 <dbl> 0.000000, 1.176891, 1.176891, 1.176891, 8.628113, 2.25…\n$ HSPC_468 <dbl> 0.000000, 1.297938, 1.297938, 0.000000, 10.108305, 0.0…\n$ HSPC_470 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 3.23…\n$ HSPC_471 <dbl> 0.000000, 0.000000, 1.535633, 0.000000, 5.948904, 0.00…\n$ HSPC_472 <dbl> 0.0000000, 0.0000000, 0.0000000, 3.3032627, 0.9896601,…\n$ HSPC_473 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 6.756343, 6.88…\n$ HSPC_474 <dbl> 1.040938, 1.040938, 0.000000, 0.000000, 2.386883, 0.00…\n$ HSPC_475 <dbl> 7.447608, 0.000000, 3.318667, 0.000000, 3.318667, 1.99…\n$ HSPC_477 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.3981912,…\n$ HSPC_478 <dbl> 0.000000, 0.000000, 4.083810, 0.000000, 2.884169, 0.00…\n$ HSPC_479 <dbl> 0.000000, 0.000000, 3.027550, 0.000000, 8.194229, 1.94…\n$ HSPC_480 <dbl> 0.000000, 1.034469, 3.059791, 0.000000, 2.376690, 1.03…\n$ HSPC_482 <dbl> 6.620967, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…\n$ HSPC_483 <dbl> 0.000000, 0.000000, 3.006419, 0.000000, 8.010120, 2.17…\n$ HSPC_485 <dbl> 0.0000000, 1.0279164, 0.6036333, 0.6036333, 0.6036333,…\n$ HSPC_486 <dbl> 0.000000, 0.000000, 1.410572, 0.000000, 1.410572, 5.75…\n$ HSPC_488 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.802316, 0.00…\n$ HSPC_489 <dbl> 0.000000, 1.882197, 0.000000, 0.000000, 0.000000, 0.00…\n$ HSPC_490 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.790270, 1.30…\n$ HSPC_491 <dbl> 0.000000, 1.469639, 0.000000, 4.081147, 8.733647, 0.00…\n$ HSPC_492 <dbl> 1.163132, 0.000000, 0.000000, 0.000000, 9.050494, 0.00…\n$ HSPC_493 <dbl> 0.000000, 0.000000, 2.223299, 0.000000, 3.586020, 4.73…\n$ HSPC_494 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.680132, 8.44…\n$ HSPC_495 <dbl> 0.000000, 2.419005, 0.000000, 0.000000, 5.543935, 2.41…\n$ HSPC_496 <dbl> 0.000000, 0.000000, 0.000000, 1.788287, 1.788287, 10.6…\n$ HSPC_497 <dbl> 0.000000, 0.000000, 1.945149, 0.000000, 1.945149, 1.94…\n$ HSPC_498 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.967779, 0.00…\n$ HSPC_499 <dbl> 0.000000, 0.000000, 1.614671, 0.000000, 8.977937, 0.00…\n$ HSPC_500 <dbl> 0.000000, 1.779610, 1.779610, 0.000000, 7.269594, 2.55…\n$ HSPC_501 <dbl> 0.000000, 2.304226, 0.000000, 0.000000, 2.304226, 0.00…\n$ HSPC_502 <dbl> 0.0000000, 0.0000000, 1.4698900, 0.0000000, 7.2171885,…\n$ HSPC_503 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.932863, 8.79…\n$ HSPC_504 <dbl> 0.000000, 0.000000, 2.201356, 0.000000, 9.317511, 2.20…\n$ HSPC_505 <dbl> 0.000000, 0.000000, 2.180019, 0.000000, 2.180019, 0.00…\n$ HSPC_506 <dbl> 0.000000, 0.000000, 1.878328, 0.000000, 2.667432, 0.00…\n$ HSPC_507 <dbl> 0.000000, 1.878372, 2.667483, 0.000000, 3.549225, 1.87…\n$ HSPC_508 <dbl> 0.0000000, 0.2296644, 0.2296644, 0.2296644, 4.6628473,…\n$ HSPC_509 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.942680, 7.82…\n$ HSPC_510 <dbl> 0.000000, 1.166340, 1.802743, 6.721521, 1.166340, 6.59…\n$ HSPC_512 <dbl> 0.000000, 0.000000, 1.738345, 0.000000, 0.000000, 1.11…\n$ HSPC_514 <dbl> 1.237955, 0.000000, 2.962215, 0.000000, 8.674516, 0.00…\n$ HSPC_515 <dbl> 1.108707, 1.108707, 1.108707, 0.000000, 1.108707, 8.57…\n$ HSPC_516 <dbl> 0.0000000, 0.0000000, 1.4299322, 0.0000000, 9.6466954,…\n$ HSPC_518 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.617584, 0.00…\n$ HSPC_520 <dbl> 0.000000, 0.000000, 1.644413, 0.000000, 8.591871, 0.00…\n$ HSPC_521 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.290520, 0.00…\n$ HSPC_522 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.182446, 0.00…\n$ HSPC_523 <dbl> 0.0000000, 0.0000000, 0.7295826, 9.9573376, 1.8610172,…\n$ HSPC_524 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.266931, 0.00…\n$ HSPC_526 <dbl> 1.301071, 1.301071, 0.000000, 0.000000, 1.301071, 0.00…\n$ HSPC_527 <dbl> 0.0000000, 0.0000000, 0.0000000, 1.2793196, 7.6901603,…\n$ HSPC_528 <dbl> 0.000000, 0.000000, 1.669689, 7.592221, 2.423018, 0.00…\n$ HSPC_530 <dbl> 1.746336, 0.000000, 0.000000, 0.000000, 8.986799, 1.74…\n$ HSPC_532 <dbl> 0.000000, 0.000000, 0.000000, 1.176792, 10.454235, 0.0…\n$ HSPC_533 <dbl> 0.0000000, 0.0000000, 0.7536668, 0.0000000, 1.9046925,…\n$ HSPC_534 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.227399, 0.00…\n$ HSPC_535 <dbl> 0.000000, 0.000000, 3.918443, 0.000000, 8.372526, 0.00…\n$ HSPC_537 <dbl> 0.000000, 0.000000, 1.665647, 1.665647, 6.776472, 2.91…\n$ HSPC_538 <dbl> 0.000000, 0.000000, 2.242435, 0.000000, 2.851982, 8.64…\n$ HSPC_539 <dbl> 0.000000, 3.354010, 1.725182, 7.588085, 7.535255, 0.00…\n$ HSPC_540 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 1.9846071,…\n$ HSPC_541 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 10.016742, 0.0…\n$ HSPC_543 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.753904, 7.96…\n$ HSPC_544 <dbl> 0.0000000, 0.0000000, 0.8221823, 0.0000000, 7.1295118,…\n$ HSPC_545 <dbl> 0.000000, 0.000000, 0.000000, 1.484735, 3.764537, 2.20…\n$ HSPC_546 <dbl> 0.000000, 2.211853, 3.047085, 0.000000, 0.000000, 0.00…\n$ HSPC_547 <dbl> 0.000000, 0.000000, 0.000000, 0.654750, 9.253559, 0.65…\n$ HSPC_548 <dbl> 0.000000, 0.000000, 2.165553, 1.455855, 1.455855, 0.00…\n$ HSPC_549 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.557210, 1.55…\n$ HSPC_550 <dbl> 1.750214, 0.000000, 1.750214, 0.000000, 3.680372, 0.00…\n$ HSPC_551 <dbl> 0.000000, 0.000000, 1.287115, 0.000000, 5.908242, 8.49…\n$ HSPC_552 <dbl> 0.000000, 0.000000, 1.226425, 0.000000, 4.650625, 8.64…\n$ HSPC_553 <dbl> 0.0000000, 0.4708726, 0.4708726, 0.0000000, 7.2724626,…\n$ HSPC_554 <dbl> 1.449277, 1.449277, 1.449277, 0.000000, 1.449277, 9.03…\n$ HSPC_555 <dbl> 0.000000, 0.000000, 1.802172, 0.000000, 0.000000, 0.00…\n$ HSPC_556 <dbl> 0.0000000, 0.0000000, 0.9369457, 0.0000000, 0.9369457,…\n$ HSPC_557 <dbl> 1.141542, 0.000000, 1.141542, 0.000000, 1.770759, 0.00…\n$ HSPC_559 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.411402, 0.00…\n$ HSPC_560 <dbl> 0.000000, 2.929589, 1.680915, 0.000000, 6.116774, 6.74…\n$ HSPC_562 <dbl> 0.000000, 0.000000, 2.717569, 0.000000, 0.000000, 8.61…\n$ HSPC_563 <dbl> 0.000000, 1.199800, 1.845653, 0.000000, 1.845653, 0.00…\n$ HSPC_566 <dbl> 0.000000, 0.000000, 2.959197, 0.000000, 2.959197, 2.13…\n$ HSPC_567 <dbl> 0.000000, 0.000000, 1.812523, 0.000000, 1.812523, 6.68…\n$ HSPC_568 <dbl> 0.000000, 0.000000, 2.956854, 0.000000, 2.131637, 2.95…\n$ HSPC_569 <dbl> 0.0000000, 1.2707229, 0.7709662, 0.0000000, 1.2707229,…\n$ HSPC_571 <dbl> 0.000000, 1.041987, 1.041987, 0.000000, 1.041987, 1.64…\n$ HSPC_573 <dbl> 0.000000, 0.000000, 1.461676, 0.000000, 3.288566, 0.00…\n$ HSPC_574 <dbl> 0.0000000, 0.7547277, 1.9066029, 0.0000000, 5.9897178,…\n$ HSPC_575 <dbl> 0.000000, 1.183227, 2.604870, 4.577438, 2.878570, 1.18…\n$ HSPC_576 <dbl> 0.000000, 0.000000, 2.741129, 0.000000, 10.233110, 6.7…\n$ HSPC_577 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.834036, 1.60…\n$ HSPC_578 <dbl> 0.000000, 0.000000, 1.404000, 7.371858, 2.101881, 1.40…\n$ HSPC_579 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.491585, 1.82…\n$ HSPC_580 <dbl> 0.000000, 0.000000, 1.510090, 0.000000, 1.510090, 0.00…\n$ HSPC_582 <dbl> 4.342803, 9.749104, 0.000000, 0.000000, 5.826065, 1.65…\n$ HSPC_584 <dbl> 0.000000, 0.000000, 2.565813, 0.000000, 3.733595, 7.76…\n$ HSPC_585 <dbl> 0.000000, 0.000000, 1.449121, 0.000000, 1.449121, 5.35…\n$ HSPC_586 <dbl> 0.0000000, 0.8914714, 1.4383643, 0.0000000, 1.8339836,…\n$ HSPC_589 <dbl> 0.000000, 1.963326, 2.765365, 0.000000, 7.352066, 0.00…\n$ HSPC_590 <dbl> 0.000000, 0.000000, 1.835966, 1.192227, 1.192227, 6.95…\n$ HSPC_592 <dbl> 0.000000, 0.000000, 0.000000, 5.165700, 1.227806, 0.00…\n$ HSPC_593 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.554248, 0.00…\n$ HSPC_594 <dbl> 1.950098, 0.000000, 1.950098, 0.000000, 4.395615, 5.71…\n$ HSPC_595 <dbl> 1.756544, 0.000000, 1.756544, 0.000000, 2.796431, 3.54…\n$ HSPC_596 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.086691, 7.95…\n$ HSPC_597 <dbl> 0.000000, 0.000000, 1.563392, 0.000000, 0.000000, 2.29…\n$ HSPC_598 <dbl> 0.000000, 0.000000, 1.220673, 1.220673, 0.000000, 1.22…\n$ HSPC_599 <dbl> 0.000000, 1.349651, 2.034571, 0.000000, 1.349651, 0.00…\n$ HSPC_600 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.377207, 0.00…\n$ HSPC_601 <dbl> 0.000000, 0.000000, 2.865429, 0.000000, 2.376203, 1.63…\n$ HSPC_602 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.806407, 2.41…\n$ HSPC_603 <dbl> 0.000000, 2.004660, 1.589106, 0.000000, 9.490018, 0.00…\n$ HSPC_604 <dbl> 1.748474, 0.000000, 2.516030, 0.000000, 1.748474, 0.00…\n$ HSPC_606 <dbl> 0.000000, 0.000000, 1.075087, 0.000000, 2.111214, 0.00…\n$ HSPC_607 <dbl> 0.000000, 0.000000, 3.717957, 0.000000, 9.191362, 0.00…\n$ HSPC_608 <dbl> 0.000000, 1.574605, 1.574605, 0.000000, 3.156098, 1.57…\n$ HSPC_610 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.00…\n$ HSPC_612 <dbl> 0.000000, 1.356545, 2.043143, 0.000000, 6.275658, 0.00…\n$ HSPC_613 <dbl> 0.000000, 0.000000, 1.438703, 6.081511, 2.144549, 6.95…\n$ HSPC_614 <dbl> 0.000000, 1.315161, 3.077500, 0.000000, 4.543775, 1.31…\n$ HSPC_615 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.450652, 0.00…\n$ HSPC_617 <dbl> 0.0000000, 1.3847809, 2.3300729, 0.0000000, 0.8525275,…\n$ HSPC_618 <dbl> 0.000000, 0.000000, 3.542103, 0.000000, 2.885428, 2.39…\n$ HSPC_620 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.517983, 0.00…\n$ HSPC_623 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.705469, 1.70…\n$ HSPC_624 <dbl> 0.0000000, 0.7200995, 0.0000000, 0.0000000, 8.9490659,…\n$ HSPC_625 <dbl> 0.000000, 0.000000, 0.000000, 3.458561, 8.847668, 2.93…\n$ HSPC_626 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.424363, 0.00…\n$ HSPC_627 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.000538, 6.99…\n$ HSPC_628 <dbl> 0.000000, 1.638219, 0.000000, 0.000000, 1.638219, 0.00…\n$ HSPC_629 <dbl> 1.196665, 0.000000, 0.000000, 0.000000, 1.841645, 0.00…\n$ HSPC_630 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.098177, 2.09…\n$ HSPC_631 <dbl> 0.000000, 1.893172, 1.237108, 0.000000, 9.108643, 1.23…\n$ HSPC_633 <dbl> 0.000000, 0.000000, 2.731996, 0.000000, 2.731996, 0.00…\n$ HSPC_634 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 8.501517, 0.00…\n$ HSPC_635 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.892953, 0.00…\n$ HSPC_636 <dbl> 0.000000, 1.087599, 1.700617, 1.087599, 6.627788, 0.00…\n$ HSPC_637 <dbl> 0.0000000, 0.9389313, 2.4816963, 0.0000000, 7.3021856,…\n$ HSPC_638 <dbl> 0.0000000, 0.0000000, 1.1842890, 0.0000000, 2.5023010,…\n$ HSPC_639 <dbl> 0.996580, 1.994867, 2.579258, 0.000000, 2.316452, 0.99…\n$ HSPC_640 <dbl> 0.000000, 1.025432, 2.362406, 0.000000, 2.627103, 1.02…\n$ HSPC_641 <dbl> 0.000000, 0.000000, 2.000512, 0.000000, 2.000512, 1.00…\n$ HSPC_643 <dbl> 0.000000, 2.465561, 1.705628, 0.000000, 9.092947, 1.70…\n$ HSPC_644 <dbl> 0.0000000, 0.4904320, 0.8557752, 0.0000000, 6.1750519,…\n$ HSPC_645 <dbl> 0.0000000, 0.0000000, 0.7156666, 0.0000000, 2.0742346,…\n$ HSPC_646 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.9959057, 9.3159931,…\n$ HSPC_648 <dbl> 0.000000, 2.242718, 0.000000, 0.000000, 3.081640, 1.51…\n$ HSPC_649 <dbl> 0.0000000, 0.7139349, 0.7139349, 0.0000000, 0.7139349,…\n$ HSPC_651 <dbl> 0.000000, 2.253619, 7.476806, 0.000000, 6.023902, 2.73…\n$ HSPC_652 <dbl> 1.352094, 0.000000, 2.500412, 0.000000, 2.037610, 0.00…\n$ HSPC_654 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.886385, 7.53…\n$ HSPC_656 <dbl> 0.000000, 1.355844, 1.355844, 0.000000, 0.000000, 0.00…\n$ HSPC_657 <dbl> 0.000000, 2.238724, 3.077173, 0.000000, 6.951322, 3.07…\n$ HSPC_658 <dbl> 0.000000, 0.000000, 2.796922, 3.846645, 8.942041, 0.00…\n$ HSPC_660 <dbl> 0.000000, 1.253314, 1.253314, 0.000000, 7.108572, 1.25…\n$ HSPC_661 <dbl> 0.000000, 1.491329, 3.043680, 1.491329, 9.643608, 0.00…\n$ HSPC_662 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.688146, 4.39…\n$ HSPC_663 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 0.000000, 6.57…\n$ HSPC_664 <dbl> 0.0000000, 1.5053124, 0.9406784, 0.0000000, 1.5053124,…\n$ HSPC_665 <dbl> 0.000000, 1.290390, 1.290390, 0.000000, 6.107672, 1.29…\n$ HSPC_666 <dbl> 0.000000, 1.130141, 0.000000, 7.631839, 8.362422, 6.31…\n$ HSPC_667 <dbl> 0.000000, 0.000000, 1.776997, 0.000000, 6.465588, 1.77…\n$ HSPC_668 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 9.302290, 0.00…\n$ HSPC_669 <dbl> 0.000000, 0.000000, 2.425594, 0.000000, 1.671860, 0.00…\n$ HSPC_670 <dbl> 0.000000, 2.337848, 3.718794, 0.000000, 0.000000, 2.33…\n$ HSPC_671 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.158776, 0.00…\n$ HSPC_672 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 1.038159, 0.00…\n$ HSPC_673 <dbl> 0.000000, 1.519487, 1.519487, 0.000000, 8.786551, 5.68…\n$ HSPC_674 <dbl> 0.000000, 1.235988, 0.000000, 0.000000, 1.891750, 1.89…\n$ HSPC_676 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.975163, 1.74…\n$ HSPC_678 <dbl> 0.000000, 2.249420, 1.524795, 0.000000, 3.089130, 0.00…\n$ HSPC_679 <dbl> 2.256522, 2.256522, 0.000000, 1.530665, 2.737162, 1.53…\n$ HSPC_680 <dbl> 0.000000, 2.323337, 0.000000, 5.359270, 8.465280, 0.00…\n$ HSPC_681 <dbl> 0.0000000, 1.6627807, 0.0000000, 0.0000000, 1.9591579,…\n$ HSPC_682 <dbl> 0.000000, 1.043463, 0.000000, 7.477477, 2.064713, 1.04…\n$ HSPC_683 <dbl> 0.000000, 0.000000, 2.261955, 0.000000, 3.631015, 0.00…\n$ HSPC_687 <dbl> 0.000000, 0.000000, 3.811941, 9.810273, 3.430744, 2.09…\n$ HSPC_689 <dbl> 0.000000, 0.000000, 0.000000, 2.085635, 10.065718, 2.0…\n$ HSPC_690 <dbl> 2.196289, 2.196289, 3.029625, 0.000000, 6.808290, 7.65…\n$ HSPC_692 <dbl> 2.555176, 0.000000, 4.919547, 0.000000, 5.637318, 2.55…\n$ HSPC_695 <dbl> 0.0000000, 0.2681148, 1.3971816, 0.2681148, 10.7977224…\n$ HSPC_696 <dbl> 0.000000, 2.265383, 4.020705, 0.000000, 8.976227, 0.00…\n$ HSPC_697 <dbl> 0.000000, 1.271303, 3.012385, 0.000000, 0.000000, 7.59…\n$ HSPC_698 <dbl> 0.000000, 0.000000, 4.694712, 0.000000, 0.000000, 0.00…\n$ HSPC_699 <dbl> 0.000000, 0.000000, 2.930008, 0.000000, 0.000000, 0.00…\n$ HSPC_700 <dbl> 0.000000, 0.000000, 1.083768, 0.000000, 1.695606, 0.00…\n$ HSPC_701 <dbl> 0.000000, 1.156584, 3.066217, 1.156584, 5.095538, 1.79…\n$ HSPC_702 <dbl> 0.000000, 0.000000, 3.185800, 0.000000, 2.336223, 2.33…\n$ HSPC_703 <dbl> 0.000000, 3.026458, 2.193467, 4.488271, 7.306130, 6.31…\n$ HSPC_704 <dbl> 0.000000, 1.708092, 1.708092, 0.000000, 1.708092, 8.37…\n$ HSPC_705 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 7.8292660,…\n$ HSPC_706 <dbl> 0.000000, 1.272727, 0.000000, 1.272727, 6.221902, 7.03…\n$ HSPC_707 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 3.917498, 1.36…\n$ HSPC_708 <dbl> 0.000000, 0.000000, 0.000000, 4.944545, 9.429525, 9.22…\n$ HSPC_709 <dbl> 0.000000, 0.000000, 6.392596, 0.000000, 5.005898, 0.00…\n$ HSPC_714 <dbl> 0.000000, 0.000000, 3.784587, 0.000000, 2.395136, 1.64…\n$ HSPC_716 <dbl> 0.000000, 0.000000, 0.000000, 1.311350, 8.040385, 1.31…\n$ HSPC_717 <dbl> 0.000000, 1.153655, 1.153655, 0.000000, 2.224752, 0.00…\n$ HSPC_719 <dbl> 0.000000, 0.000000, 3.146515, 6.492252, 4.618557, 0.00…\n$ HSPC_720 <dbl> 0.0000000, 0.8205612, 1.3403833, 0.0000000, 1.7217133,…\n$ HSPC_721 <dbl> 0.000000, 2.935568, 3.456262, 0.000000, 0.000000, 2.11…\n$ HSPC_722 <dbl> 0.000000, 0.000000, 2.268643, 0.000000, 9.095591, 1.18…\n$ HSPC_723 <dbl> 0.8420975, 0.8420975, 2.5250483, 0.0000000, 4.8843811,…\n$ HSPC_724 <dbl> 0.0000000, 1.0942930, 1.7093650, 0.0000000, 3.2499204,…\n$ HSPC_725 <dbl> 1.577147, 0.000000, 3.159495, 0.000000, 2.312545, 2.79…\n$ HSPC_727 <dbl> 0.000000, 1.576167, 3.894561, 0.000000, 8.810183, 0.00…\n$ HSPC_729 <dbl> 0.0000000, 0.0000000, 1.9363149, 0.0000000, 7.5873035,…\n$ HSPC_730 <dbl> 1.134736, 1.761952, 2.197661, 0.000000, 7.311461, 0.00…\n$ HSPC_731 <dbl> 0.000000, 1.129768, 1.129768, 4.039758, 1.755516, 5.09…\n$ HSPC_732 <dbl> 0.0000000, 0.6937047, 2.2340688, 0.6937047, 2.0310980,…\n$ HSPC_733 <dbl> 0.000000, 0.000000, 1.435585, 0.000000, 4.173862, 2.14…\n$ HSPC_734 <dbl> 0.000000, 1.217180, 1.578880, 0.000000, 9.362294, 1.21…\n$ HSPC_735 <dbl> 0.000000, 0.000000, 2.048558, 0.000000, 9.326379, 2.04…\n$ HSPC_736 <dbl> 0.0000000, 0.0000000, 0.6789402, 0.0000000, 2.2034453,…\n$ HSPC_737 <dbl> 0.0000000, 0.0000000, 0.7110779, 0.0000000, 7.7355892,…\n$ HSPC_738 <dbl> 0.000000, 2.715943, 0.000000, 0.000000, 3.362289, 8.20…\n$ HSPC_740 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.620463, 6.54…\n$ HSPC_742 <dbl> 0.0000000, 0.8436636, 0.8436636, 0.0000000, 1.7586463,…\n$ HSPC_743 <dbl> 1.122102, 0.000000, 0.000000, 0.000000, 0.000000, 6.91…\n$ HSPC_744 <dbl> 0.000000, 7.472712, 1.212953, 0.000000, 1.212953, 0.00…\n$ HSPC_745 <dbl> 0.000000, 0.000000, 0.000000, 2.402561, 2.402561, 0.00…\n$ HSPC_746 <dbl> 0.000000, 2.183740, 0.000000, 0.000000, 6.837761, 0.00…\n$ HSPC_747 <dbl> 0.000000, 0.000000, 1.777355, 0.000000, 7.778192, 2.54…\n$ HSPC_748 <dbl> 0.000000, 2.263816, 0.000000, 0.000000, 3.303450, 0.00…\n$ HSPC_749 <dbl> 0.000000, 0.000000, 1.443774, 0.000000, 3.501043, 0.00…\n$ HSPC_750 <dbl> 0.000000, 1.029692, 0.000000, 0.000000, 8.102961, 0.00…\n$ HSPC_751 <dbl> 0.000000, 0.000000, 3.435201, 0.000000, 3.435201, 7.28…\n$ HSPC_752 <dbl> 2.227867, 0.000000, 2.227867, 0.000000, 3.065024, 9.23…\n$ HSPC_753 <dbl> 0.000000, 0.000000, 2.092163, 0.000000, 2.420031, 3.10…\n$ HSPC_755 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 5.3881500,…\n$ HSPC_756 <dbl> 0.000000, 1.730911, 0.000000, 0.000000, 6.980096, 6.68…\n$ HSPC_757 <dbl> 0.000000, 1.394821, 1.394821, 0.000000, 3.193425, 0.00…\n$ HSPC_758 <dbl> 0.0000000, 0.8647569, 1.4016663, 0.0000000, 7.1436038,…\n$ HSPC_759 <dbl> 1.5063802, 0.9414682, 1.9112870, 0.9414682, 2.2271023,…\n$ HSPC_760 <dbl> 0.000000, 9.073516, 1.653873, 0.000000, 1.051989, 0.00…\n$ HSPC_761 <dbl> 0.0000000, 2.0270678, 1.5086587, 0.0000000, 2.4076406,…\n$ HSPC_762 <dbl> 0.000000, 1.935807, 1.935807, 0.000000, 7.772123, 0.00…\n$ HSPC_764 <dbl> 0.000000, 1.784134, 1.784134, 0.000000, 3.969445, 1.78…\n$ HSPC_765 <dbl> 0.000000, 2.116018, 2.116018, 0.000000, 11.306195, 2.1…\n$ HSPC_766 <dbl> 0.0000000, 0.9827582, 0.9827582, 0.0000000, 3.5531934,…\n$ HSPC_767 <dbl> 0.6646406, 0.0000000, 1.4623246, 0.0000000, 10.4507620…\n$ HSPC_768 <dbl> 0.000000, 1.703127, 1.703127, 1.703127, 9.992206, 2.46…\n$ HSPC_769 <dbl> 0.000000, 2.429943, 4.213728, 0.000000, 4.772537, 2.42…\n$ HSPC_770 <dbl> 0.000000, 1.760248, 1.760248, 1.760248, 6.134208, 2.52…\n$ HSPC_771 <dbl> 0.000000, 0.000000, 1.818104, 0.000000, 8.774881, 1.81…\n$ HSPC_772 <dbl> 0.000000, 1.600797, 0.000000, 0.000000, 2.340909, 0.00…\n$ HSPC_773 <dbl> 0.0000000, 0.9900713, 1.5717090, 0.0000000, 5.2659904,…\n$ HSPC_774 <dbl> 0.0000000, 0.0000000, 1.5496423, 0.0000000, 2.5406754,…\n$ HSPC_776 <dbl> 0.000000, 0.000000, 3.691348, 2.314052, 8.369556, 0.00…\n$ HSPC_777 <dbl> 0.000000, 0.000000, 2.079090, 0.000000, 8.370977, 1.05…\n$ HSPC_778 <dbl> 0.000000, 0.000000, 3.691686, 1.434589, 4.532296, 0.00…\n$ HSPC_780 <dbl> 0.000000, 1.178249, 1.818049, 0.000000, 7.874420, 0.00…\n$ HSPC_781 <dbl> 0.000000, 2.884261, 2.884261, 0.000000, 3.783095, 2.88…\n$ HSPC_782 <dbl> 0.000000, 0.000000, 2.691145, 0.000000, 7.913867, 0.00…\n$ HSPC_783 <dbl> 0.000000, 0.000000, 1.775679, 0.000000, 8.938539, 9.20…\n$ HSPC_784 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 2.668054, 1.05…\n$ HSPC_785 <dbl> 0.000000, 0.000000, 2.465903, 0.000000, 3.530782, 1.32…\n$ HSPC_786 <dbl> 0.000000, 1.148465, 1.779704, 0.000000, 2.552651, 8.42…\n$ HSPC_787 <dbl> 0.000000, 0.000000, 3.897091, 2.161763, 3.513976, 4.84…\n$ HSPC_788 <dbl> 0.000000, 0.000000, 3.844884, 0.000000, 1.877270, 6.68…\n$ HSPC_789 <dbl> 0.000000, 0.000000, 1.822053, 0.000000, 2.264164, 1.18…\n$ HSPC_790 <dbl> 0.000000, 0.000000, 2.851872, 0.000000, 9.272098, 0.00…\n$ HSPC_791 <dbl> 1.789964, 0.000000, 4.930584, 0.000000, 6.443477, 2.56…\n$ HSPC_794 <dbl> 0.000000, 1.112719, 0.000000, 1.112719, 2.498279, 3.78…\n$ HSPC_795 <dbl> 0.0000000, 0.0000000, 0.0000000, 0.0000000, 2.2924736,…\n$ HSPC_796 <dbl> 0.0000000, 0.7001062, 1.5229964, 1.1694432, 6.1361361,…\n$ HSPC_797 <dbl> 0.000000, 0.872225, 1.411951, 0.872225, 3.217981, 0.87…\n$ HSPC_798 <dbl> 0.000000, 1.531160, 0.000000, 2.257120, 4.314828, 0.00…\n$ HSPC_799 <dbl> 0.000000, 0.000000, 4.059222, 2.637723, 3.516894, 0.00…\n$ HSPC_800 <dbl> 0.000000, 0.000000, 1.766326, 0.000000, 5.615808, 3.03…\n$ HSPC_801 <dbl> 0.000000, 1.335456, 0.000000, 0.000000, 1.335456, 0.00…\n$ HSPC_802 <dbl> 0.000000, 0.000000, 0.000000, 2.881726, 4.081151, 0.00…\n$ HSPC_803 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 7.326296, 0.00…\n$ HSPC_804 <dbl> 0.000000, 0.000000, 1.871913, 0.000000, 2.660005, 6.23…\n$ HSPC_806 <dbl> 0.000000, 0.000000, 0.000000, 0.000000, 4.774769, 0.00…\n$ HSPC_807 <dbl> 0.0000000, 1.2411117, 0.0000000, 0.0000000, 2.3480676,…\n$ HSPC_808 <dbl> 0.0000000, 0.0000000, 1.1139506, 2.5001649, 1.1139506,…\n$ HSPC_809 <dbl> 1.788499, 0.000000, 0.000000, 1.788499, 3.064135, 1.78…\n$ HSPC_810 <dbl> 0.000000, 0.000000, 1.651345, 0.000000, 3.549216, 0.00…\n$ HSPC_812 <dbl> 0.0000000, 0.0000000, 2.0668334, 0.0000000, 8.1782380,…\n$ HSPC_813 <dbl> 0.0000000, 0.0000000, 0.6277581, 4.3663804, 1.8975541,…\n$ HSPC_814 <dbl> 0.000000, 1.109763, 2.161664, 0.000000, 0.000000, 0.00…\n$ HSPC_815 <dbl> 0.9630613, 0.9630613, 2.7434401, 0.9630613, 7.9310753,…\n$ HSPC_816 <dbl> 0.000000, 0.000000, 1.346034, 1.346034, 6.498939, 1.34…\n$ HSPC_818 <dbl> 0.0000000, 0.0000000, 3.4534483, 0.9967122, 9.8653293,…\n$ HSPC_819 <dbl> 0.000000, 0.000000, 3.149725, 1.364557, 7.496230, 2.05…\n$ HSPC_820 <dbl> 0.0000000, 0.8098885, 2.4642646, 0.0000000, 9.0793660,…\n$ HSPC_821 <dbl> 0.000000, 0.000000, 3.792154, 0.000000, 3.175252, 0.00…\n$ HSPC_822 <dbl> 0.000000, 1.386534, 3.957031, 0.000000, 2.080316, 0.00…\n$ HSPC_824 <dbl> 0.000000, 1.236780, 1.236780, 2.342030, 2.342030, 1.23…\n$ HSPC_825 <dbl> 0.000000, 0.000000, 2.079118, 0.000000, 5.922947, 2.07…\n$ HSPC_826 <dbl> 0.000000, 2.002994, 2.002994, 0.000000, 4.811203, 0.00…\n$ HSPC_827 <dbl> 1.746375, 0.000000, 3.381363, 0.000000, 7.353191, 1.74…\n$ HSPC_828 <dbl> 0.000000, 0.000000, 1.588492, 0.000000, 2.326162, 0.00…\n$ HSPC_831 <dbl> 0.000000, 1.304384, 0.000000, 0.000000, 6.405611, 6.06…\n$ HSPC_832 <dbl> 0.000000, 0.000000, 2.376979, 0.000000, 9.076790, 1.03…\n$ HSPC_833 <dbl> 0.0000000, 0.0000000, 1.8371808, 0.0000000, 0.0000000,…\n$ HSPC_834 <dbl> 0.0000000, 0.9245023, 1.8852183, 0.0000000, 8.9614736,…\n$ HSPC_835 <dbl> 0.000000, 0.000000, 2.595404, 0.000000, 6.141626, 6.76…\n$ HSPC_836 <dbl> 0.000000, 0.000000, 3.994019, 0.000000, 4.386241, 0.00…\n$ HSPC_837 <dbl> 0.000000, 0.000000, 2.792826, 0.000000, 0.000000, 0.00…\n$ HSPC_838 <dbl> 0.0000000, 0.0000000, 3.2926469, 0.0000000, 0.9838061,…\n$ HSPC_839 <dbl> 0.000000, 0.000000, 2.452494, 0.000000, 2.720721, 1.69…\n$ HSPC_840 <dbl> 0.000000, 0.000000, 3.534585, 0.000000, 1.866730, 5.03…\n$ HSPC_841 <dbl> 0.000000, 0.000000, 1.438567, 0.000000, 1.438567, 1.43…\n$ HSPC_842 <dbl> 0.000000, 2.545827, 2.545827, 0.000000, 7.622029, 2.54…\n$ HSPC_843 <dbl> 1.256817, 0.000000, 1.256817, 0.000000, 1.256817, 0.00…\n$ HSPC_844 <dbl> 0.000000, 0.000000, 1.584047, 0.000000, 7.433263, 0.00…\n$ HSPC_845 <dbl> 0.000000, 1.227393, 1.880830, 7.669286, 6.989762, 0.00…\n$ HSPC_846 <dbl> 0.000000, 1.401354, 1.401354, 0.000000, 2.919525, 7.30…\n$ HSPC_848 <dbl> 0.000000, 0.000000, 1.391594, 0.000000, 2.553619, 7.94…\n$ HSPC_849 <dbl> 0.000000, 0.000000, 1.601976, 0.000000, 8.387312, 2.82…\n$ HSPC_851 <dbl> 0.000000, 0.000000, 3.910752, 0.000000, 6.824279, 7.81…\n$ HSPC_852 <dbl> 0.000000, 1.658355, 2.409562, 1.658355, 1.658355, 0.00…\n\n\n\n\n\n#---CODING ANSWER---\n#| include: false\nglimpse(prog_hspc_results)\n\nRows: 280\nColumns: 6\n$ Top <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…\n$ p.value <dbl> 7.038138e-117, 4.736622e-90, 1.832630e-88, 4.211954e-7…\n$ FDR <dbl> 1.970679e-114, 6.631271e-88, 1.710455e-86, 2.948368e-7…\n$ summary.logFC <dbl> 1.596910, 3.035165, 3.261056, -2.146491, -3.056730, 3.…\n$ logFC.hspc <dbl> 1.596910, 3.035165, 3.261056, -2.146491, -3.056730, 3.…\n$ ensembl_gene_id <chr> \"ENSMUSG00000028639\", \"ENSMUSG00000024053\", \"ENSMUSG00…\n\n\n\n\n\n\nIt is useful to have this information in a single dataframe to which we will add the gene information from Ensembl Having all the information together will make it easier to interpret the results and select genes of interest.\n🎬 Merge the two dataframes:\n\n# merge stats results with normalise values\nprog_hspc_results <- prog_hspc_results |> \n left_join(prog_hspc, by = \"ensembl_gene_id\")\n\nThis means you have the counts for each sample along with the statistical results for each gene."
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"title": "Workshop",
- "section": "Add gene information from the NCBI using biomaRt",
- "text": "Add gene information from the NCBI using biomaRt\nadd the gene information using biomart\nEnsembl (Birney et al. 2004) is a bioinformatics project to organise all the biological information around the sequences of large genomes. The are a large number of databases but BioMart (smedley2009?) provides a consistent interface to the material. There are web-based tools to use these but the R package biomaRtr (Durinck et al. 2009) enables you to rapidly access and integrate information into your R data structures.\n\nlibrary(biomaRt)\n\n\n# Connect to the mouse database\n\nensembl <- useMart(biomart = \"ensembl\", \n dataset = \"mmusculus_gene_ensembl\")\n\nSee what info we can retrieve:\n\nlistAttributes(mart = ensembl) |> View()\n\nError in .External2(C_dataviewer, x, title): unable to start data viewer\n\n\nWe need ensembl_gene_id: because we will need to merge the info external_gene_name: name for gene description: description\n\ngene_info <- getBM(filters = \"ensembl_gene_id\",\n attributes = c(\"ensembl_gene_id\",\n \"external_gene_name\",\n \"description\"),\n values = prog_hspc_results$ensembl_gene_id,\n mart = ensembl)\n\nNotice the dataframe returned only has 279 rows, not 280. Which one is missing?\n\nprog_hspc_results |> select(ensembl_gene_id) |> \n filter(!ensembl_gene_id %in% gene_info$ensembl_gene_id)\n\nError:\n! [conflicted] select found in 2 packages.\nEither pick the one you want with `::`:\n• biomaRt::select\n• plotly::select\nOr declare a preference with `conflicts_prefer()`:\n• `conflicts_prefer(biomaRt::select)`\n• `conflicts_prefer(plotly::select)`\n\n\n\nprog_hspc_results |> dplyr::select(ensembl_gene_id) |> \n filter(!ensembl_gene_id %in% gene_info$ensembl_gene_id)\n\n# A tibble: 1 × 1\n ensembl_gene_id \n <chr> \n1 ENSMUSG00000029386\n\n\nWe might want to look that up. Google it. Let’s worry about it later if it turns out to be something important.\nmerge the gene info with the results\n\nprog_hspc_results <- prog_hspc_results |> \n left_join(gene_info, by = \"ensembl_gene_id\")\n\nWe now have datatframe with all the info we need, normalised counts, log2 normalised counts, statistical comparisons with fold changes and p values, information about the gene other than just the id"
+ "section": "Add gene information from Ensembl using biomaRt",
+ "text": "Add gene information from Ensembl using biomaRt\nEnsembl (Martin et al. 2023; Birney et al. 2004)is a bioinformatics project to organise all the biological information around the sequences of large genomes. The are a large number of databases but BioMart (Smedley et al. 2009) provides a consistent interface to the material. There are web-based tools to use these but the R package biomaRt (Durinck et al. 2009) gives you programmatic access making it easier to integrate information into R dataframes\n🎬 Load the biomaRt (Durinck et al. 2009) package:\n\nlibrary(biomaRt)\n\n🎬 Connect to the mouse database and see what information we can retrieve:\n\n# Connect to the mouse database\nensembl <- useMart(biomart = \"ensembl\", \n dataset = \"mmusculus_gene_ensembl\")\n\n# See what information we can retrieve\nlistAttributes(mart = ensembl) |> View()\n\nError in .External2(C_dataviewer, x, title): unable to start data viewer\n\n\nThis may take a moment\nWe use the getBM() function to retrieve information from the database. The filters argument is used to specified what kind of identifier we are supplying to retrieve information. The attributes argument is used to select the information we want to retrieve. The values argument is used to specify the identifers. The mart argument is used to specify the connection we created.\n🎬 Get the gene information:\n\ngene_info <- getBM(filters = \"ensembl_gene_id\",\n attributes = c(\"ensembl_gene_id\",\n \"external_gene_name\",\n \"description\"),\n values = prog_hspc_results$ensembl_gene_id,\n mart = ensembl)\n\nWe are getting the gene name and and a description. We also need to get the id because we will use that to merge the gene_info dataframe with the prog_hspc_results dataframe. Notice the dataframe returned only has 279 rows - one of the ids does not have information.\n🎬 We can find which is missing with:\n\nprog_hspc_results |> select(ensembl_gene_id) |> \n filter(!ensembl_gene_id %in% gene_info$ensembl_gene_id)\n\nError:\n! [conflicted] select found in 2 packages.\nEither pick the one you want with `::`:\n• biomaRt::select\n• plotly::select\nOr declare a preference with `conflicts_prefer()`:\n• `conflicts_prefer(biomaRt::select)`\n• `conflicts_prefer(plotly::select)`\n\n\nOh, conflicted has flagged a conflict for us.\n🎬 Take the appropriate action to resolve the conflict:\n❓ What is the id which is missing information?\n\n\nWe might want to look that up - but let’s worry about it later if it turns out to be something important.\n🎬 Merge the gene information with the results:\n\nprog_hspc_results <- prog_hspc_results |> \n left_join(gene_info, by = \"ensembl_gene_id\")\n\nI recommend viewing the dataframe to see the new columns. We now have dataframe with all the info we need, normalised counts, log2 normalised counts, statistical comparisons with fold changes and p values, information about the gene other than just the id"
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"section": "Write the significant genes to file",
- "text": "Write the significant genes to file\nsave the most sig genes to file\n\nprog_hspc_results_sig0.01 <- prog_hspc_results |> \n filter(FDR <= 0.01)\n\n168 genes\nwrite to csv file\n\nwrite_csv(prog_hspc_results_sig0.01, \n file = \"results/prog_hspc_results_sig0.01.csv\")\n\n\nprog_hspc_results_sig0.05 <- prog_hspc_results |> \n filter(FDR <= 0.05)\n\n182 genes\nwrite to csv file\n\nwrite_csv(prog_hspc_results_sig0.05, \n file = \"results/prog_hspc_results_sig0.05.csv\")"
+ "text": "Write the significant genes to file\nWe will create dateframe of the signifcant genes and write them to file. These are the files you want to examine in more detail along with the visualisations to select your genes of interest.\n🎬 Create a dataframe of the genes significant at the 0.01 level:\n\nprog_hspc_results_sig0.01 <- prog_hspc_results |> \n filter(FDR <= 0.01)\n\n🎬 Write the dataframe to file\n\n#---CODING ANSWER---\n#| include: false\nwrite_csv(prog_hspc_results_sig0.01, \n file = \"results/prog_hspc_results_sig0.01.csv\")\n\n🎬 Create a dataframe of the genes significant at the 0.05 level and write to file:\n\n#---CODING ANSWER---\n#| include: false\n\nprog_hspc_results_sig0.05 <- prog_hspc_results |> \n filter(FDR <= 0.05)\n\n# write to csv file\nwrite_csv(prog_hspc_results_sig0.05, \n file = \"results/prog_hspc_results_sig0.05.csv\")\n\n❓How many genes are significant at the 0.01 and 0.05 levels?"
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"section": "View the relationship between cells using PCA",
- "text": "View the relationship between cells using PCA\nwe do this on the log2 transformed normalised counts or the regularized the log transformed counts\ntranspose the data we are reducing he number of dimensions from 280\n\nprog_hspc_trans <- prog_hspc_results |> \n dplyr::select(starts_with(c(\"Prog_\", \"HSPC_\"))) |>\n t() |> \n data.frame()\n\n\ncolnames(prog_hspc_trans) <- prog_hspc_results$ensembl_gene_id\n\nperform PCA using standard functions\n\npca <- prog_hspc_trans |>\n prcomp(scale. = TRUE) \n\n\nsummary(pca)\n\nImportance of components:\n PC1 PC2 PC3 PC4 PC5 PC6 PC7\nStandard deviation 4.3892 3.08797 2.25263 2.13943 1.96659 1.76697 1.62753\nProportion of Variance 0.0688 0.03406 0.01812 0.01635 0.01381 0.01115 0.00946\nCumulative Proportion 0.0688 0.10286 0.12098 0.13733 0.15114 0.16229 0.17175\n PC8 PC9 PC10 PC11 PC12 PC13 PC14\nStandard deviation 1.47668 1.46595 1.44342 1.42486 1.41429 1.39361 1.37089\nProportion of Variance 0.00779 0.00768 0.00744 0.00725 0.00714 0.00694 0.00671\nCumulative Proportion 0.17954 0.18721 0.19466 0.20191 0.20905 0.21599 0.22270\n PC15 PC16 PC17 PC18 PC19 PC20 PC21\nStandard deviation 1.36576 1.33322 1.32697 1.32019 1.31398 1.30389 1.30191\nProportion of Variance 0.00666 0.00635 0.00629 0.00622 0.00617 0.00607 0.00605\nCumulative Proportion 0.22936 0.23571 0.24200 0.24822 0.25439 0.26046 0.26651\n PC22 PC23 PC24 PC25 PC26 PC27 PC28\nStandard deviation 1.2962 1.29071 1.28268 1.27844 1.2745 1.26791 1.2634\nProportion of Variance 0.0060 0.00595 0.00588 0.00584 0.0058 0.00574 0.0057\nCumulative Proportion 0.2725 0.27846 0.28434 0.29018 0.2960 0.30172 0.3074\n PC29 PC30 PC31 PC32 PC33 PC34 PC35\nStandard deviation 1.25770 1.24910 1.24288 1.23382 1.23236 1.22745 1.22563\nProportion of Variance 0.00565 0.00557 0.00552 0.00544 0.00542 0.00538 0.00536\nCumulative Proportion 0.31307 0.31864 0.32416 0.32959 0.33502 0.34040 0.34576\n PC36 PC37 PC38 PC39 PC40 PC41 PC42\nStandard deviation 1.2178 1.21186 1.20980 1.20808 1.20140 1.19771 1.19167\nProportion of Variance 0.0053 0.00524 0.00523 0.00521 0.00515 0.00512 0.00507\nCumulative Proportion 0.3511 0.35631 0.36153 0.36675 0.37190 0.37702 0.38210\n PC43 PC44 PC45 PC46 PC47 PC48 PC49\nStandard deviation 1.18693 1.1830 1.17952 1.17629 1.16840 1.16561 1.16494\nProportion of Variance 0.00503 0.0050 0.00497 0.00494 0.00488 0.00485 0.00485\nCumulative Proportion 0.38713 0.3921 0.39709 0.40204 0.40691 0.41176 0.41661\n PC50 PC51 PC52 PC53 PC54 PC55 PC56\nStandard deviation 1.16276 1.1597 1.15720 1.15025 1.14323 1.13645 1.1348\nProportion of Variance 0.00483 0.0048 0.00478 0.00473 0.00467 0.00461 0.0046\nCumulative Proportion 0.42144 0.4262 0.43102 0.43575 0.44042 0.44503 0.4496\n PC57 PC58 PC59 PC60 PC61 PC62 PC63\nStandard deviation 1.13171 1.12596 1.1226 1.12127 1.11630 1.11416 1.11191\nProportion of Variance 0.00457 0.00453 0.0045 0.00449 0.00445 0.00443 0.00442\nCumulative Proportion 0.45420 0.45873 0.4632 0.46772 0.47217 0.47661 0.48102\n PC64 PC65 PC66 PC67 PC68 PC69 PC70\nStandard deviation 1.10848 1.10387 1.09837 1.09610 1.09477 1.08975 1.08839\nProportion of Variance 0.00439 0.00435 0.00431 0.00429 0.00428 0.00424 0.00423\nCumulative Proportion 0.48541 0.48976 0.49407 0.49836 0.50264 0.50688 0.51111\n PC71 PC72 PC73 PC74 PC75 PC76 PC77\nStandard deviation 1.08334 1.08041 1.07919 1.07612 1.07293 1.06974 1.06652\nProportion of Variance 0.00419 0.00417 0.00416 0.00414 0.00411 0.00409 0.00406\nCumulative Proportion 0.51531 0.51947 0.52363 0.52777 0.53188 0.53597 0.54003\n PC78 PC79 PC80 PC81 PC82 PC83 PC84\nStandard deviation 1.06480 1.06249 1.05454 1.05217 1.04951 1.04646 1.0447\nProportion of Variance 0.00405 0.00403 0.00397 0.00395 0.00393 0.00391 0.0039\nCumulative Proportion 0.54408 0.54811 0.55208 0.55604 0.55997 0.56388 0.5678\n PC85 PC86 PC87 PC88 PC89 PC90 PC91\nStandard deviation 1.04048 1.03910 1.03605 1.03344 1.03033 1.02706 1.02398\nProportion of Variance 0.00387 0.00386 0.00383 0.00381 0.00379 0.00377 0.00374\nCumulative Proportion 0.57165 0.57550 0.57934 0.58315 0.58694 0.59071 0.59445\n PC92 PC93 PC94 PC95 PC96 PC97 PC98\nStandard deviation 1.02269 1.0178 1.01520 1.01022 1.00512 1.0043 1.00011\nProportion of Variance 0.00374 0.0037 0.00368 0.00364 0.00361 0.0036 0.00357\nCumulative Proportion 0.59819 0.6019 0.60557 0.60921 0.61282 0.6164 0.62000\n PC99 PC100 PC101 PC102 PC103 PC104 PC105\nStandard deviation 0.99823 0.99478 0.99302 0.9901 0.98821 0.98275 0.98033\nProportion of Variance 0.00356 0.00353 0.00352 0.0035 0.00349 0.00345 0.00343\nCumulative Proportion 0.62356 0.62709 0.63061 0.6341 0.63760 0.64105 0.64448\n PC106 PC107 PC108 PC109 PC110 PC111 PC112\nStandard deviation 0.97805 0.97199 0.96996 0.96693 0.96433 0.96244 0.95904\nProportion of Variance 0.00342 0.00337 0.00336 0.00334 0.00332 0.00331 0.00328\nCumulative Proportion 0.64790 0.65127 0.65463 0.65797 0.66129 0.66460 0.66789\n PC113 PC114 PC115 PC116 PC117 PC118 PC119\nStandard deviation 0.95590 0.95299 0.95160 0.94787 0.9468 0.94142 0.94086\nProportion of Variance 0.00326 0.00324 0.00323 0.00321 0.0032 0.00317 0.00316\nCumulative Proportion 0.67115 0.67439 0.67763 0.68084 0.6840 0.68720 0.69036\n PC120 PC121 PC122 PC123 PC124 PC125 PC126\nStandard deviation 0.93911 0.93573 0.93081 0.92879 0.92768 0.92383 0.92272\nProportion of Variance 0.00315 0.00313 0.00309 0.00308 0.00307 0.00305 0.00304\nCumulative Proportion 0.69351 0.69664 0.69973 0.70282 0.70589 0.70894 0.71198\n PC127 PC128 PC129 PC130 PC131 PC132 PC133\nStandard deviation 0.92121 0.91999 0.91773 0.91295 0.91056 0.90720 0.90358\nProportion of Variance 0.00303 0.00302 0.00301 0.00298 0.00296 0.00294 0.00292\nCumulative Proportion 0.71501 0.71803 0.72104 0.72402 0.72698 0.72992 0.73283\n PC134 PC135 PC136 PC137 PC138 PC139 PC140\nStandard deviation 0.9009 0.89932 0.89834 0.89301 0.89167 0.89028 0.88693\nProportion of Variance 0.0029 0.00289 0.00288 0.00285 0.00284 0.00283 0.00281\nCumulative Proportion 0.7357 0.73862 0.74150 0.74435 0.74719 0.75002 0.75283\n PC141 PC142 PC143 PC144 PC145 PC146 PC147\nStandard deviation 0.88267 0.88149 0.87828 0.87515 0.87308 0.87031 0.86696\nProportion of Variance 0.00278 0.00278 0.00275 0.00274 0.00272 0.00271 0.00268\nCumulative Proportion 0.75561 0.75839 0.76114 0.76388 0.76660 0.76931 0.77199\n PC148 PC149 PC150 PC151 PC152 PC153 PC154\nStandard deviation 0.86536 0.86323 0.86248 0.86061 0.85821 0.8531 0.85071\nProportion of Variance 0.00267 0.00266 0.00266 0.00265 0.00263 0.0026 0.00258\nCumulative Proportion 0.77466 0.77733 0.77998 0.78263 0.78526 0.7879 0.79044\n PC155 PC156 PC157 PC158 PC159 PC160 PC161\nStandard deviation 0.84901 0.84521 0.84208 0.84179 0.83835 0.8370 0.83336\nProportion of Variance 0.00257 0.00255 0.00253 0.00253 0.00251 0.0025 0.00248\nCumulative Proportion 0.79302 0.79557 0.79810 0.80063 0.80314 0.8056 0.80812\n PC162 PC163 PC164 PC165 PC166 PC167 PC168\nStandard deviation 0.83207 0.82803 0.82685 0.82371 0.82099 0.8189 0.81575\nProportion of Variance 0.00247 0.00245 0.00244 0.00242 0.00241 0.0024 0.00238\nCumulative Proportion 0.81060 0.81304 0.81549 0.81791 0.82032 0.8227 0.82509\n PC169 PC170 PC171 PC172 PC173 PC174 PC175\nStandard deviation 0.81407 0.81227 0.80721 0.8026 0.80146 0.79810 0.79633\nProportion of Variance 0.00237 0.00236 0.00233 0.0023 0.00229 0.00227 0.00226\nCumulative Proportion 0.82745 0.82981 0.83214 0.8344 0.83673 0.83901 0.84127\n PC176 PC177 PC178 PC179 PC180 PC181 PC182\nStandard deviation 0.79349 0.79025 0.78951 0.78582 0.78058 0.77895 0.77538\nProportion of Variance 0.00225 0.00223 0.00223 0.00221 0.00218 0.00217 0.00215\nCumulative Proportion 0.84352 0.84575 0.84798 0.85018 0.85236 0.85453 0.85667\n PC183 PC184 PC185 PC186 PC187 PC188 PC189\nStandard deviation 0.77191 0.77111 0.76859 0.7669 0.7666 0.76160 0.76145\nProportion of Variance 0.00213 0.00212 0.00211 0.0021 0.0021 0.00207 0.00207\nCumulative Proportion 0.85880 0.86093 0.86303 0.8651 0.8672 0.86931 0.87138\n PC190 PC191 PC192 PC193 PC194 PC195 PC196\nStandard deviation 0.75890 0.75735 0.75547 0.75079 0.7489 0.74442 0.74130\nProportion of Variance 0.00206 0.00205 0.00204 0.00201 0.0020 0.00198 0.00196\nCumulative Proportion 0.87343 0.87548 0.87752 0.87953 0.8815 0.88352 0.88548\n PC197 PC198 PC199 PC200 PC201 PC202 PC203\nStandard deviation 0.73911 0.73641 0.73232 0.73086 0.7299 0.72752 0.72384\nProportion of Variance 0.00195 0.00194 0.00192 0.00191 0.0019 0.00189 0.00187\nCumulative Proportion 0.88743 0.88937 0.89128 0.89319 0.8951 0.89698 0.89885\n PC204 PC205 PC206 PC207 PC208 PC209 PC210\nStandard deviation 0.72191 0.72004 0.71646 0.71404 0.71147 0.70617 0.70423\nProportion of Variance 0.00186 0.00185 0.00183 0.00182 0.00181 0.00178 0.00177\nCumulative Proportion 0.90071 0.90257 0.90440 0.90622 0.90803 0.90981 0.91158\n PC211 PC212 PC213 PC214 PC215 PC216 PC217\nStandard deviation 0.70298 0.69954 0.69657 0.69582 0.69276 0.69270 0.68833\nProportion of Variance 0.00176 0.00175 0.00173 0.00173 0.00171 0.00171 0.00169\nCumulative Proportion 0.91335 0.91509 0.91683 0.91856 0.92027 0.92198 0.92368\n PC218 PC219 PC220 PC221 PC222 PC223 PC224\nStandard deviation 0.68455 0.68008 0.67851 0.67558 0.67106 0.66645 0.66592\nProportion of Variance 0.00167 0.00165 0.00164 0.00163 0.00161 0.00159 0.00158\nCumulative Proportion 0.92535 0.92700 0.92864 0.93027 0.93188 0.93347 0.93505\n PC225 PC226 PC227 PC228 PC229 PC230 PC231\nStandard deviation 0.66215 0.65987 0.65570 0.65358 0.65119 0.6488 0.64568\nProportion of Variance 0.00157 0.00156 0.00154 0.00153 0.00151 0.0015 0.00149\nCumulative Proportion 0.93662 0.93817 0.93971 0.94124 0.94275 0.9443 0.94574\n PC232 PC233 PC234 PC235 PC236 PC237 PC238\nStandard deviation 0.64343 0.64096 0.63918 0.63427 0.63107 0.62967 0.6260\nProportion of Variance 0.00148 0.00147 0.00146 0.00144 0.00142 0.00142 0.0014\nCumulative Proportion 0.94722 0.94869 0.95015 0.95158 0.95301 0.95442 0.9558\n PC239 PC240 PC241 PC242 PC243 PC244 PC245\nStandard deviation 0.6253 0.62063 0.61999 0.61433 0.61119 0.60898 0.60787\nProportion of Variance 0.0014 0.00138 0.00137 0.00135 0.00133 0.00132 0.00132\nCumulative Proportion 0.9572 0.95859 0.95997 0.96131 0.96265 0.96397 0.96529\n PC246 PC247 PC248 PC249 PC250 PC251 PC252\nStandard deviation 0.6030 0.60131 0.59776 0.59573 0.59070 0.58844 0.5805\nProportion of Variance 0.0013 0.00129 0.00128 0.00127 0.00125 0.00124 0.0012\nCumulative Proportion 0.9666 0.96788 0.96916 0.97043 0.97167 0.97291 0.9741\n PC253 PC254 PC255 PC256 PC257 PC258 PC259\nStandard deviation 0.5791 0.57666 0.57115 0.57036 0.56503 0.56091 0.5561\nProportion of Variance 0.0012 0.00119 0.00117 0.00116 0.00114 0.00112 0.0011\nCumulative Proportion 0.9753 0.97650 0.97766 0.97882 0.97996 0.98109 0.9822\n PC260 PC261 PC262 PC263 PC264 PC265 PC266\nStandard deviation 0.55234 0.54999 0.54348 0.53916 0.53751 0.53097 0.52478\nProportion of Variance 0.00109 0.00108 0.00105 0.00104 0.00103 0.00101 0.00098\nCumulative Proportion 0.98328 0.98436 0.98542 0.98646 0.98749 0.98849 0.98948\n PC267 PC268 PC269 PC270 PC271 PC272 PC273\nStandard deviation 0.51696 0.51540 0.51156 0.5013 0.48840 0.47896 0.46889\nProportion of Variance 0.00095 0.00095 0.00093 0.0009 0.00085 0.00082 0.00079\nCumulative Proportion 0.99043 0.99138 0.99232 0.9932 0.99406 0.99488 0.99567\n PC274 PC275 PC276 PC277 PC278 PC279 PC280\nStandard deviation 0.46518 0.45568 0.43304 0.42194 0.40659 0.36945 0.34821\nProportion of Variance 0.00077 0.00074 0.00067 0.00064 0.00059 0.00049 0.00043\nCumulative Proportion 0.99644 0.99718 0.99785 0.99849 0.99908 0.99957 1.00000\n\n\nImportance of components: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Standard deviation 4.3892 3.08797 2.25263 2.13943 1.96659 1.76697 1.62753 1.47668 Proportion of Variance 0.0688 0.03406 0.01812 0.01635 0.01381 0.01115 0.00946 0.00779 Cumulative Proportion 0.0688 0.10286 0.12098 0.13733 0.15114 0.16229 0.17175 0.17954\n\npca_labelled <- data.frame(pca$x,\n cell_id = row.names(prog_hspc_trans))\n\nadd the cell type information so we can label points split cell_id into cell type and replicate and keep cell_id column\n\npca_labelled <- pca_labelled |> \n extract(cell_id, \n remove = FALSE,\n c(\"cell_type\", \"cell_number\"),\n \"([a-zA-Z]{4})_([0-9]{3})\")\n\n\npca <- pca_labelled |> \n ggplot(aes(x = PC1, y = PC2, \n colour = cell_type)) +\n geom_point(alpha = 0.4) +\n scale_colour_viridis_d(end = 0.8, begin = 0.15,\n name = \"Cell type\") +\n theme_classic()\n\nFairly good separation of cell types but plenty of overlap\n\nggsave(\"figures/prog_hspc-pca.png\",\n plot = pca,\n height = 3, \n width = 4,\n units = \"in\",\n device = \"png\")\n\ntSNE ??"
+ "text": "View the relationship between cells using PCA\nWe have 280 genes in our dataset. PCA will allow us to plot our cells in the “gene expression” space so we can see if Prog cells cluster together and HSPC cells cluster together as we would expect. We do this on the log2 transformed normalised counts.\nOur data have genes in rows and samples in columns which is a common organisation for gene expression data. However, PCA expects cells in rows and genes, the variables, in columns. We can transpose the data to get it in the correct format.\n🎬 Transpose the log2 transformed normalised counts:\n\nprog_hspc_trans <- prog_hspc_results |> \n dplyr::select(starts_with(c(\"Prog_\", \"HSPC_\"))) |>\n t() |> \n data.frame()\n\nWe have used the select() function to select all the columns that start with Prog_ or HSPC_. We then use the t() function to transpose the dataframe. We then convert the resulting matrix to a dataframe using data.frame(). If you view that dataframe you’ll see it has default column name which we can fix using colnames() to set the column names to the gene ids.\n🎬 Set the column names to the gene ids:\n\ncolnames(prog_hspc_trans) <- prog_hspc_results$ensembl_gene_id\n\nperform PCA using standard functions\n\npca <- prog_hspc_trans |>\n prcomp(rank. = 15) \n\nThe rank. argument tells prcomp() to only calculate the first 15 principal components. This is useful for visualisation as we can only plot in 2 or 3 dimensions. We can see the results of the PCA by viewing the summary() of the pca object.\n\nsummary(pca)\n\nImportance of first k=15 (out of 280) components:\n PC1 PC2 PC3 PC4 PC5 PC6 PC7\nStandard deviation 12.5612 8.36646 5.98988 5.41386 4.55730 4.06142 3.84444\nProportion of Variance 0.1099 0.04874 0.02498 0.02041 0.01446 0.01149 0.01029\nCumulative Proportion 0.1099 0.15861 0.18359 0.20400 0.21846 0.22995 0.24024\n PC8 PC9 PC10 PC11 PC12 PC13 PC14\nStandard deviation 3.70848 3.66899 3.5549 3.48508 3.44964 3.42393 3.37882\nProportion of Variance 0.00958 0.00937 0.0088 0.00846 0.00829 0.00816 0.00795\nCumulative Proportion 0.24982 0.25919 0.2680 0.27645 0.28473 0.29290 0.30085\n PC15\nStandard deviation 3.33622\nProportion of Variance 0.00775\nCumulative Proportion 0.30860\n\n\nThe Proportion of Variance tells us how much of the variance is explained by each component. We can see that the first component explains 0.1099 of the variance, the second 0.04874, and the third 0.2498. Together the first three components explain 18% of the total variance in the data. Plotting PC1 against PC2 will capture about 16% of the variance. This is not that high but it likely better than we would get plotting any two genes against each other. To plot the PC1 against PC2 we will need to extract the PC1 and PC2 score from the pca object and add labels for the cells.\n🎬 Create a dataframe of the PC1 and PC2 scores which are in pca$x and add the cell ids:\n\npca_labelled <- data.frame(pca$x,\n cell_id = row.names(prog_hspc_trans))\n\nIt will be helpful to add a column for the cell type so we can label points. One way to do this is to extract the information in the cell_id column into two columns.\n🎬 Extract the cell type and cell number from the cell_id column (keeping the cell_id column):\n\npca_labelled <- pca_labelled |> \n extract(cell_id, \n remove = FALSE,\n c(\"cell_type\", \"cell_number\"),\n \"([a-zA-Z]{4})_([0-9]{3})\")\n\n\"([a-zA-Z]{4})_([0-9]{3})\" is a regular expression - or regex. [a-zA-Z] means any lower or upper case letter, {4} means 4 of them, and [0-9] means any number, {3} means 3 of them. The brackets around the two parts of the regex mean we want to extract those parts. The first part goes into cell_type and the second part goes into cell_number. The _ between the two patterns matches the underscore and the fact it isn’t in a bracket means we don’t want to keep it.\nWe can now plot the PC1 and PC2 scores.\n🎬 Plot PC1 against PC2 and colour the points by cell type:\n\npca <- pca_labelled |> \n ggplot(aes(x = PC1, y = PC2, \n colour = cell_type)) +\n geom_point(alpha = 0.4) +\n scale_colour_viridis_d(end = 0.8, begin = 0.15,\n name = \"Cell type\") +\n theme_classic()\npca\n\n\n\n\nFairly good separation of cell types but plenty of overlap\n🎬 Save the plot to file:\n\nggsave(\"figures/prog_hspc-pca.png\",\n plot = pca,\n height = 3, \n width = 4,\n units = \"in\",\n device = \"png\")"
},
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"objectID": "omics/week-5/workshop.html#visualise-the-expression-of-the-most-significant-genes-using-a-heatmap-1",
@@ -466,7 +473,7 @@
"href": "core/week-2/workshop.html#rstudio-terminal",
"title": "Workshop",
"section": "RStudio terminal",
- "text": "RStudio terminal\nThe RStudio terminal is a convenient interface to the shell without leaving RStudio. It is useful for running commands that are not available in R. For example, you can use it to run other programs like fasqc, git, ftp, ssh\nNavigating your file system\nSeveral commands are frequently used to create, inspect, rename, and delete files and directories.\n$\nThe dollar sign is the prompt (like > on the R console), which shows us that the shell is waiting for input.\nYou can find out where you are using the pwd command, which stands for “print working directory”.\n\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2\n\n\nYou can find out what you can see with ls which stands for “list”.\n\nls\n\ndata\nimages\noverview.qmd\nstudy_after_workshop.qmd\nstudy_before_workshop.ipynb\nstudy_before_workshop.qmd\nworkshop.qmd\nworkshop.rmarkdown\n\n\nYou might have noticed that unlike R, the commands do not have brackets after them. Instead, options (or switches) are given after the command. For example, we can modify the ls command to give us more information with the -l option, which stands for “long”.\n\nls -l\n\ntotal 64\ndrwxr-xr-x 2 runner docker 4096 Oct 23 16:29 data\ndrwxr-xr-x 2 runner docker 4096 Oct 23 16:29 images\n-rw-r--r-- 1 runner docker 1597 Oct 23 16:29 overview.qmd\n-rw-r--r-- 1 runner docker 184 Oct 23 16:29 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4807 Oct 23 16:29 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13029 Oct 23 16:29 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8550 Oct 23 16:29 workshop.qmd\n-rw-r--r-- 1 runner docker 8564 Oct 23 16:32 workshop.rmarkdown\n\n\nYou can use more than one option at once. The -h option stands for “human readable” and makes the file sizes easier to understand for humans:\n\nls -hl\n\ntotal 64K\ndrwxr-xr-x 2 runner docker 4.0K Oct 23 16:29 data\ndrwxr-xr-x 2 runner docker 4.0K Oct 23 16:29 images\n-rw-r--r-- 1 runner docker 1.6K Oct 23 16:29 overview.qmd\n-rw-r--r-- 1 runner docker 184 Oct 23 16:29 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4.7K Oct 23 16:29 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13K Oct 23 16:29 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct 23 16:29 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct 23 16:32 workshop.rmarkdown\n\n\nThe -a option stands for “all” and shows us all the files, including hidden files.\n\nls -alh\n\ntotal 72K\ndrwxr-xr-x 4 runner docker 4.0K Oct 23 16:32 .\ndrwxr-xr-x 5 runner docker 4.0K Oct 23 16:29 ..\ndrwxr-xr-x 2 runner docker 4.0K Oct 23 16:29 data\ndrwxr-xr-x 2 runner docker 4.0K Oct 23 16:29 images\n-rw-r--r-- 1 runner docker 1.6K Oct 23 16:29 overview.qmd\n-rw-r--r-- 1 runner docker 184 Oct 23 16:29 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4.7K Oct 23 16:29 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13K Oct 23 16:29 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct 23 16:29 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct 23 16:32 workshop.rmarkdown\n\n\nYou can move about with the cd command, which stands for “change directory”. You can use it to move into a directory by specifying the path to the directory:\n\ncd data\npwd\ncd ..\npwd\ncd data\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2/data\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2/data\n\n\nhead 1cq2.pdb\nHEADER OXYGEN STORAGE/TRANSPORT 04-AUG-99 1CQ2 \nTITLE NEUTRON STRUCTURE OF FULLY DEUTERATED SPERM WHALE MYOGLOBIN AT 2.0 \nTITLE 2 ANGSTROM \nCOMPND MOL_ID: 1; \nCOMPND 2 MOLECULE: MYOGLOBIN; \nCOMPND 3 CHAIN: A; \nCOMPND 4 ENGINEERED: YES; \nCOMPND 5 OTHER_DETAILS: PROTEIN IS FULLY DEUTERATED \nSOURCE MOL_ID: 1; \nSOURCE 2 ORGANISM_SCIENTIFIC: PHYSETER CATODON; \nhead -20 data/1cq2.pdb\nHEADER OXYGEN STORAGE/TRANSPORT 04-AUG-99 1CQ2 \nTITLE NEUTRON STRUCTURE OF FULLY DEUTERATED SPERM WHALE MYOGLOBIN AT 2.0 \nTITLE 2 ANGSTROM \nCOMPND MOL_ID: 1; \nCOMPND 2 MOLECULE: MYOGLOBIN; \nCOMPND 3 CHAIN: A; \nCOMPND 4 ENGINEERED: YES; \nCOMPND 5 OTHER_DETAILS: PROTEIN IS FULLY DEUTERATED \nSOURCE MOL_ID: 1; \nSOURCE 2 ORGANISM_SCIENTIFIC: PHYSETER CATODON; \nSOURCE 3 ORGANISM_COMMON: SPERM WHALE; \nSOURCE 4 ORGANISM_TAXID: 9755; \nSOURCE 5 EXPRESSION_SYSTEM: ESCHERICHIA COLI; \nSOURCE 6 EXPRESSION_SYSTEM_TAXID: 562; \nSOURCE 7 EXPRESSION_SYSTEM_VECTOR_TYPE: PLASMID; \nSOURCE 8 EXPRESSION_SYSTEM_PLASMID: PET15A \nKEYWDS HELICAL, GLOBULAR, ALL-HYDROGEN CONTAINING STRUCTURE, OXYGEN STORAGE- \nKEYWDS 2 TRANSPORT COMPLEX \nEXPDTA NEUTRON DIFFRACTION \nAUTHOR F.SHU,V.RAMAKRISHNAN,B.P.SCHOENBORN \nless 1cq2.pdb\nless is a program that displays the contents of a file, one page at a time. It is useful for viewing large files because it does not load the whole file into memory before displaying it. Instead, it reads and displays a few lines at a time. You can navigate forward through the file with the spacebar, and backwards with the b key. Press q to quit.\nA wildcard is a character that can be used as a substitute for any of a class of characters in a search, The most common wildcard characters are the asterisk (*) and the question mark (?).\nls *.csv\ncp stands for “copy”. You can copy a file from one directory to another by giving cp the path to the file you want to copy and the path to the destination directory.\ncp 1cq2.pdb copy_of_1cq2.pdb\ncp 1cq2.pdb ../copy_of_1cq2.pdb\ncp 1cq2.pdb ../bob.txt\nTo delete a file use the rm command, which stands for “remove”.\nrm ../bob.txt\nbut be careful because the file will be gone forever. There is no “are you sure?” or undo.\nTo move a file from one directory to another, use the mv command. mv works like cp except that it also deletes the original file.\nmv ../copy_of_1cq2.pdb .\nMake a directory\nmkdir mynewdir"
+ "text": "RStudio terminal\nThe RStudio terminal is a convenient interface to the shell without leaving RStudio. It is useful for running commands that are not available in R. For example, you can use it to run other programs like fasqc, git, ftp, ssh\nNavigating your file system\nSeveral commands are frequently used to create, inspect, rename, and delete files and directories.\n$\nThe dollar sign is the prompt (like > on the R console), which shows us that the shell is waiting for input.\nYou can find out where you are using the pwd command, which stands for “print working directory”.\n\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2\n\n\nYou can find out what you can see with ls which stands for “list”.\n\nls\n\ndata\nimages\noverview.qmd\nstudy_after_workshop.qmd\nstudy_before_workshop.ipynb\nstudy_before_workshop.qmd\nworkshop.qmd\nworkshop.rmarkdown\n\n\nYou might have noticed that unlike R, the commands do not have brackets after them. Instead, options (or switches) are given after the command. For example, we can modify the ls command to give us more information with the -l option, which stands for “long”.\n\nls -l\n\ntotal 64\ndrwxr-xr-x 2 runner docker 4096 Oct 24 11:41 data\ndrwxr-xr-x 2 runner docker 4096 Oct 24 11:41 images\n-rw-r--r-- 1 runner docker 1597 Oct 24 11:41 overview.qmd\n-rw-r--r-- 1 runner docker 184 Oct 24 11:41 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4807 Oct 24 11:41 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13029 Oct 24 11:41 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8550 Oct 24 11:41 workshop.qmd\n-rw-r--r-- 1 runner docker 8564 Oct 24 11:44 workshop.rmarkdown\n\n\nYou can use more than one option at once. The -h option stands for “human readable” and makes the file sizes easier to understand for humans:\n\nls -hl\n\ntotal 64K\ndrwxr-xr-x 2 runner docker 4.0K Oct 24 11:41 data\ndrwxr-xr-x 2 runner docker 4.0K Oct 24 11:41 images\n-rw-r--r-- 1 runner docker 1.6K Oct 24 11:41 overview.qmd\n-rw-r--r-- 1 runner docker 184 Oct 24 11:41 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4.7K Oct 24 11:41 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13K Oct 24 11:41 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct 24 11:41 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct 24 11:44 workshop.rmarkdown\n\n\nThe -a option stands for “all” and shows us all the files, including hidden files.\n\nls -alh\n\ntotal 72K\ndrwxr-xr-x 4 runner docker 4.0K Oct 24 11:44 .\ndrwxr-xr-x 5 runner docker 4.0K Oct 24 11:41 ..\ndrwxr-xr-x 2 runner docker 4.0K Oct 24 11:41 data\ndrwxr-xr-x 2 runner docker 4.0K Oct 24 11:41 images\n-rw-r--r-- 1 runner docker 1.6K Oct 24 11:41 overview.qmd\n-rw-r--r-- 1 runner docker 184 Oct 24 11:41 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4.7K Oct 24 11:41 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13K Oct 24 11:41 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct 24 11:41 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct 24 11:44 workshop.rmarkdown\n\n\nYou can move about with the cd command, which stands for “change directory”. You can use it to move into a directory by specifying the path to the directory:\n\ncd data\npwd\ncd ..\npwd\ncd data\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2/data\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2/data\n\n\nhead 1cq2.pdb\nHEADER OXYGEN STORAGE/TRANSPORT 04-AUG-99 1CQ2 \nTITLE NEUTRON STRUCTURE OF FULLY DEUTERATED SPERM WHALE MYOGLOBIN AT 2.0 \nTITLE 2 ANGSTROM \nCOMPND MOL_ID: 1; \nCOMPND 2 MOLECULE: MYOGLOBIN; \nCOMPND 3 CHAIN: A; \nCOMPND 4 ENGINEERED: YES; \nCOMPND 5 OTHER_DETAILS: PROTEIN IS FULLY DEUTERATED \nSOURCE MOL_ID: 1; \nSOURCE 2 ORGANISM_SCIENTIFIC: PHYSETER CATODON; \nhead -20 data/1cq2.pdb\nHEADER OXYGEN STORAGE/TRANSPORT 04-AUG-99 1CQ2 \nTITLE NEUTRON STRUCTURE OF FULLY DEUTERATED SPERM WHALE MYOGLOBIN AT 2.0 \nTITLE 2 ANGSTROM \nCOMPND MOL_ID: 1; \nCOMPND 2 MOLECULE: MYOGLOBIN; \nCOMPND 3 CHAIN: A; \nCOMPND 4 ENGINEERED: YES; \nCOMPND 5 OTHER_DETAILS: PROTEIN IS FULLY DEUTERATED \nSOURCE MOL_ID: 1; \nSOURCE 2 ORGANISM_SCIENTIFIC: PHYSETER CATODON; \nSOURCE 3 ORGANISM_COMMON: SPERM WHALE; \nSOURCE 4 ORGANISM_TAXID: 9755; \nSOURCE 5 EXPRESSION_SYSTEM: ESCHERICHIA COLI; \nSOURCE 6 EXPRESSION_SYSTEM_TAXID: 562; \nSOURCE 7 EXPRESSION_SYSTEM_VECTOR_TYPE: PLASMID; \nSOURCE 8 EXPRESSION_SYSTEM_PLASMID: PET15A \nKEYWDS HELICAL, GLOBULAR, ALL-HYDROGEN CONTAINING STRUCTURE, OXYGEN STORAGE- \nKEYWDS 2 TRANSPORT COMPLEX \nEXPDTA NEUTRON DIFFRACTION \nAUTHOR F.SHU,V.RAMAKRISHNAN,B.P.SCHOENBORN \nless 1cq2.pdb\nless is a program that displays the contents of a file, one page at a time. It is useful for viewing large files because it does not load the whole file into memory before displaying it. Instead, it reads and displays a few lines at a time. You can navigate forward through the file with the spacebar, and backwards with the b key. Press q to quit.\nA wildcard is a character that can be used as a substitute for any of a class of characters in a search, The most common wildcard characters are the asterisk (*) and the question mark (?).\nls *.csv\ncp stands for “copy”. You can copy a file from one directory to another by giving cp the path to the file you want to copy and the path to the destination directory.\ncp 1cq2.pdb copy_of_1cq2.pdb\ncp 1cq2.pdb ../copy_of_1cq2.pdb\ncp 1cq2.pdb ../bob.txt\nTo delete a file use the rm command, which stands for “remove”.\nrm ../bob.txt\nbut be careful because the file will be gone forever. There is no “are you sure?” or undo.\nTo move a file from one directory to another, use the mv command. mv works like cp except that it also deletes the original file.\nmv ../copy_of_1cq2.pdb .\nMake a directory\nmkdir mynewdir"
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- "text": "This week we cover how to visualise and interpret the results of your differential expression analysis. The independent study will allow you to check you have what you should have following the Omics 2: Statistical Analysis workshop and Consolidation study. It will also summarise the the methods and plots we will go through in the workshop. In the workshop, we will learn how to merge gene information into out results, conduct a Principle Component Analysis (PCA) and plot the results as well as how to create a nicely formatted Volcano plot and heatmap.\nWe suggest you sit together with your group in the workshop.\n\nLearning objectives\nThe successful student will be able to:\n\nverify they have the required RStudio Project set up and the data and code files from the previous Workshop and Consolidation study\nexplain where gene information came from and add it to their results\nperform a PCA and understand how to interpret them\ncreate a heatmap and understand how to interpret them\ncreate a volcano plot and understand how to interpret them\n\n\n\nInstructions\n\nPrepare\n\n📖 Read what you should have so far and about concepts in PCA, volcano plots and heatmaps.\n\nWorkshop\n\n💻 Add gene information to the results of DE\n💻 Perform and plot a PCA\n💻 Visualise results with a heatmap\n💻 Visualise all the results with a volcano plot\nLook after future you!\n\nConsolidate\n\n💻 Use the work you completed in the workshop as a template to apply to a new case.\n\n\n\n\nReferences"
+ "text": "This week we cover how to visualise and interpret the results of your differential expression analysis. The independent study will allow you to check you have what you should have following the Omics 2: Statistical Analysis workshop and Consolidation study. It will also summarise the the methods and plots we will go through in the workshop. In the workshop, we will learn how to merge gene information into our results, conduct a Principle Component Analysis (PCA) and plot the results as well as how to create a nicely formatted Volcano plot and heatmap.\nWe suggest you sit together with your group in the workshop.\n\nLearning objectives\nThe successful student will be able to:\n\nverify they have the required RStudio Project set up and the data and code files from the previous Workshop and Consolidation study\nexplain where gene information came from and add it to their results\nperform a PCA and understand how to interpret them\ncreate a heatmap and understand how to interpret them\ncreate a volcano plot and understand how to interpret them\n\n\n\nInstructions\n\nPrepare\n\n📖 Read what you should have so far and about concepts in PCA, volcano plots and heatmaps.\n\nWorkshop\n\n💻 Add gene information to the results of DE\n💻 Perform and plot a PCA\n💻 Visualise results with a heatmap\n💻 Visualise all the results with a volcano plot\nLook after future you!\n\nConsolidate\n\n💻 Use the work you completed in the workshop as a template to apply to a new case.\n\n\n\n\nReferences"
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