library(tidyverse)
diff --git a/omics/week-3/workshop.html b/omics/week-3/workshop.html index 63d048f..4635d3e 100644 --- a/omics/week-3/workshop.html +++ b/omics/week-3/workshop.html @@ -298,14 +298,30 @@
Emma Rand
+Throughout the three workshops we will examine one aspect of each data set as a model:
+See later workshops.
+In this workshop you will
-See later workshops.
+In this workshop you will learn how to get an overview of your pmics data
+distrubution of te values anomalies quality control
data-raw
workshop-1.R
to carry out the rest of the work.tidyverse
tidyverse
(Wickham et al. 2019)The workshops will examine the “least interesting”: - the difference between the control and the FGF treated sibling at S30 - the difference between HSPC and Prog cells
-🐸 Frog development data - xlaevis_counts_S14.csv - xlaevis_counts_S20.csv - xlaevis_counts_S30.csv - meta_data.txt
-🐭 Stem cell data - surfaceome_hspc.csv - surfaceome_prog.csv - surfaceome_lthsc.csv
+🐸 Frog development data:
+ +🐭 Stem cell data:
data-raw
🍂 xxxx data:
+🎬 Save the files to data-raw
and open them in Excel 🎬 Answer the following questions: - Describe how the sets of data are similar and how they are different. - What is in the rows and columns of each file? - How many rows and columns are there in each file? Are these the same? In all cases or some cases? Why? - Google an id. Where does your search take you? How much information is available? 🎬 Did you record all that??
open in understanding the data numbers of variables, organisation
🎬 Import xlaevis_counts_S30.csv, surfaceome_hspc.csv and surfaceome_prog.csv
🎬 Import xlaevis_counts_S30.csv, surfaceome_hspc.csv and surfaceome_prog.csv
🎬 Check these have the number of rows and column you were expecting and that column types and names are as expected.
The
-Distributions
+The first task is to get an overview. We want to know - are there any missing values? If so, how many and how are they distributed? - how may zeros are there and how are they distributed - does it look as tough all the samples/cells were equally “successful”? Can we spot any problematic anomalies? - what is the distribution of values? If our data collection has gone well we would hope to see approximately the same average expression in each sample or cell of the same type. We would also expect to see that the average expression of genes varies. - are there genes which are zero in every cell/sample. We will want to to filter those out.
+We get this overview by looking at:
The distribution of values across the whole dataset
The distribution of values across the sample/cells (i.e., averaged across genes). This allows us to see variation between samples/cells:
The distribution of values across the genes (i.e., averaged across samples/cells). This allows us to see variation between genes.
🐸 Frog development data - 🎬 Make a script file called cont-fgf-s30.R
. This will a be commented analysis of the control vs FGF at S30 comparison. You will build on this each workshop and be able to use it as a template to examine other comparisons. Copy in the appropriate code and comments from workshop-1.R
. Edit to improve your comments where your understanding has developed since you made them.
🐭 Stem cell data - 🎬 Save the files to data-raw
xxxxxxxxxxx
+🎬 Get
+s30 |>
+ pivot_longer(cols = -xenbase_gene_id,
+ names_to = "sample",
+ values_to = "count") |>
+ ggplot(aes(x = count)) +
+ geom_histogram()
xxxxxxxxxxxxxxxxxx
+🎬 Get
+s30 |>
+ pivot_longer(cols = -xenbase_gene_id,
+ names_to = "sample",
+ values_to = "count") |>
+ ggplot(aes(x = log10(count))) +
+ geom_histogram()
xxxxxxxxxxx
+🎬 Get
+hspc |>
+ pivot_longer(cols = -ensembl_gene_id,
+ names_to = "cell",
+ values_to = "expr") |>
+ ggplot(aes(x = expr)) +
+ geom_histogram()
The excess number of low counts indicates we might want to create a cut off for quality control. The removal of low counts is a common processing step in ’omic data. We will revisit this after we have considered the distribution of counts across genes (averaged over the samples).
+Summary statistics including the the number of NAs can be seen using the summary()
. It is most helpful which you have up to about 30 columns. There is nothing special about the number 30, it is just that text summaries of a larger number of columns are difficult to grasp.
🎬 Get a quick overview of the columns:
+ xenbase_gene_id S30_C_5 S30_C_6 S30_C_A
+ Length:11893 Min. : 0.0 Min. : 0.0 Min. : 0.0
+ Class :character 1st Qu.: 14.0 1st Qu.: 14.0 1st Qu.: 23.0
+ Mode :character Median : 70.0 Median : 75.0 Median : 107.0
+ Mean : 317.1 Mean : 335.8 Mean : 426.3
+ 3rd Qu.: 205.0 3rd Qu.: 220.0 3rd Qu.: 301.0
+ Max. :101746.0 Max. :118708.0 Max. :117945.0
+ S30_F_5 S30_F_6 S30_F_A
+ Min. : 0.0 Min. : 0.0 Min. : 0.0
+ 1st Qu.: 19.0 1st Qu.: 17.0 1st Qu.: 16.0
+ Median : 88.0 Median : 84.0 Median : 69.0
+ Mean : 376.2 Mean : 376.5 Mean : 260.4
+ 3rd Qu.: 251.0 3rd Qu.: 246.0 3rd Qu.: 187.0
+ Max. :117573.0 Max. :130672.0 Max. :61531.0
+Notice that: - the minimum count is 0 and the maximums are very high in all the columns - the medians are quite a lot lower than the means so the data are skewed (hump to the left, tail to the right) - there must be quite a lot of zeros - the columns are roughly similar and it doesn’t look like there is an anomalous replicate
+To find out how may zeros there are in a column we can make use of the fact that TRUE
evaluates to 1 and FALSE
evaluates to 0. This means sum(S30_C_5 == 0)
gives the number of ones in the S30_C_5
column
🎬 Find the number of zeros in all six columns:
+s30 |>
+ summarise(sum(S30_C_5 == 0),
+ sum(S30_C_6 == 0),
+ sum(S30_C_A == 0),
+ sum(S30_F_5 == 0),
+ sum(S30_F_6 == 0),
+ sum(S30_F_A == 0))
# A tibble: 1 × 6
+ `sum(S30_C_5 == 0)` `sum(S30_C_6 == 0)` `sum(S30_C_A == 0)`
+ <int> <int> <int>
+1 1340 1361 998
+# ℹ 3 more variables: `sum(S30_F_5 == 0)` <int>, `sum(S30_F_6 == 0)` <int>,
+# `sum(S30_F_A == 0)` <int>
+There is a better way of doing this that saves you having to repeat so much code - especially useful if you have a lot more than 6 columns. We can use pivot_longer()
to put the data in tidy format and then use the group_by()
and summarise()
approach we have used extensively before.
🎬 Find the number of zeros in all columns:
+s30 |>
+ pivot_longer(cols = -xenbase_gene_id,
+ names_to = "sample",
+ values_to = "count") |>
+ group_by(sample) |>
+ summarise(n_zero = sum(count == 0))
# A tibble: 6 × 2
+ sample n_zero
+ <chr> <int>
+1 S30_C_5 1340
+2 S30_C_6 1361
+3 S30_C_A 998
+4 S30_F_5 1210
+5 S30_F_6 1199
+6 S30_F_A 963
+You could expand to get all the summary information
+🎬 Summarise all the samples:
+s30 |>
+ pivot_longer(cols = -xenbase_gene_id,
+ names_to = "sample",
+ values_to = "count") |>
+ group_by(sample) |>
+ summarise(min = min(count),
+ lowerq = quantile(count, 0.25),
+ mean = mean(count),
+ median = median(count),
+ upperq = quantile(count, 0.75),
+ max = max(count),
+ n_zero = sum(count == 0))
# A tibble: 6 × 8
+ sample min lowerq mean median upperq max n_zero
+ <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
+1 S30_C_5 0 14 317. 70 205 101746 1340
+2 S30_C_6 0 14 336. 75 220 118708 1361
+3 S30_C_A 0 23 426. 107 301 117945 998
+4 S30_F_5 0 19 376. 88 251 117573 1210
+5 S30_F_6 0 17 376. 84 246 130672 1199
+6 S30_F_A 0 16 260. 69 187 61531 963
+One advantage this has over using summary()
is that the output is a dataframe. For results, this is useful, and makes it easier to:
ggplot()
🎬 Save the summary as a dataframe, s30_summary
.
We can write to file using write_csv()
🎬 Write s30_summary
to a file called “s30_summary.csv”:
Plotting the distribution of values is perhaps the easiest way to understand the data. We could plot each column separately or we can pipe the tidy format of data into ggplot()
and make use of facet_wrap()
🎬 Write pivot the data and pipe into ggplot
:
s30 |>
+ pivot_longer(cols = -xenbase_gene_id,
+ names_to = "sample",
+ values_to = "count") |>
+ ggplot(aes(count)) +
+ geom_density() +
+ facet_wrap(. ~ sample, nrow = 3)
We have many values (11893) so are not limited to using geom_histogram()
. geom_density()
will give us a smooth distribution.
We have many low values and a few very high ones which makes it tricky to see the distributions. Logging the counts will make these clearer.
+🎬 Repeat the graph but taking the base 10 log of the counts:
+s30 |>
+ pivot_longer(cols = -xenbase_gene_id,
+ names_to = "sample",
+ values_to = "count") |>
+ ggplot(aes(log10(count))) +
+ geom_density() +
+ facet_wrap(. ~ sample, nrow = 3)
I’ve used base 10 only because it easy to convert to the original scale (1 is 10, 2 is 100, 3 is 1000 etc). The warning about rows being removed is expected - these are the counts of 0 since you can’t log a value of 0. The key information to take from these plots is:
+The excess number of low counts indicates we might want to create a cut off for quality control. The removal of low counts is a common processing step in ’omic data. We will revisit this after we have considered the distribution of counts across genes (averaged over the samples).
+We used the summary()
function to get an overview of the columns in the frog data. Let’s try that here.
🎬 Get a quick overview of the columns:
+ ensembl_gene_id HSPC_001 HSPC_002 HSPC_003
+ Length:280 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Mode :character Median : 0.000 Median : 0.000 Median : 0.9929
+ Mean : 2.143 Mean : 1.673 Mean : 2.5964
+ 3rd Qu.: 2.120 3rd Qu.: 2.239 3rd Qu.: 6.1559
+ Max. :12.567 Max. :11.976 Max. :11.1138
+ HSPC_004 HSPC_006 HSPC_008 HSPC_009
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000
+ Median : 0.000 Median : 1.276 Median : 0.000 Median :0.000
+ Mean : 1.851 Mean : 2.338 Mean : 2.375 Mean :2.220
+ 3rd Qu.: 2.466 3rd Qu.: 3.536 3rd Qu.: 3.851 3rd Qu.:3.594
+ Max. :11.133 Max. :10.014 Max. :11.574 Max. :9.997
+ HSPC_011 HSPC_012 HSPC_014 HSPC_015
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 1.750 Median : 0.000 Median : 0.000
+ Mean : 2.285 Mean : 2.431 Mean : 2.295 Mean : 2.515
+ 3rd Qu.: 3.193 3rd Qu.: 3.741 3rd Qu.: 3.150 3rd Qu.: 3.789
+ Max. :11.260 Max. :10.905 Max. :11.051 Max. :10.751
+ HSPC_016 HSPC_017 HSPC_018 HSPC_020
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.9488 Median : 0.000 Median : 1.248 Median : 0.000
+ Mean : 2.6115 Mean : 2.146 Mean : 2.710 Mean : 2.509
+ 3rd Qu.: 5.9412 3rd Qu.: 2.357 3rd Qu.: 6.006 3rd Qu.: 4.470
+ Max. :11.3082 Max. :12.058 Max. :11.894 Max. :11.281
+ HSPC_021 HSPC_022 HSPC_023 HSPC_024
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.170 Mean : 2.287 Mean : 2.314 Mean : 2.195
+ 3rd Qu.: 2.996 3rd Qu.: 3.351 3rd Qu.: 2.749 3rd Qu.: 2.944
+ Max. :10.709 Max. :11.814 Max. :12.113 Max. :11.279
+ HSPC_025 HSPC_026 HSPC_027 HSPC_028
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.572 Median : 1.385 Median : 0.000 Median : 0.000
+ Mean : 2.710 Mean : 2.721 Mean : 2.458 Mean : 1.906
+ 3rd Qu.: 5.735 3rd Qu.: 6.392 3rd Qu.: 5.496 3rd Qu.: 2.037
+ Max. :11.309 Max. :10.865 Max. :11.266 Max. :10.777
+ HSPC_030 HSPC_031 HSPC_033 HSPC_034
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 1.119 Median : 0.9026 Median : 0.000 Median : 0.7984
+ Mean : 2.338 Mean : 2.3049 Mean : 1.938 Mean : 2.3220
+ 3rd Qu.: 3.005 3rd Qu.: 2.9919 3rd Qu.: 2.434 3rd Qu.: 4.8324
+ Max. :11.391 Max. :11.1748 Max. :10.808 Max. :10.6707
+ HSPC_035 HSPC_036 HSPC_037 HSPC_038
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.8879 Median : 1.517 Median : 0.000
+ Mean : 1.810 Mean : 2.6918 Mean : 2.327 Mean : 2.212
+ 3rd Qu.: 2.175 3rd Qu.: 5.9822 3rd Qu.: 3.079 3rd Qu.: 2.867
+ Max. :11.221 Max. :11.3018 Max. :11.399 Max. :12.275
+ HSPC_040 HSPC_041 HSPC_042 HSPC_043
+ Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.8673 Median : 1.342
+ Mean : 2.509 Mean : 2.492 Mean : 2.3673 Mean : 2.420
+ 3rd Qu.: 3.995 3rd Qu.: 3.943 3rd Qu.: 3.8371 3rd Qu.: 3.731
+ Max. :11.863 Max. :11.016 Max. :11.4852 Max. :11.123
+ HSPC_044 HSPC_045 HSPC_046 HSPC_047
+ Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.8452 Median : 2.195
+ Mean : 2.382 Mean : 2.277 Mean : 1.9707 Mean : 2.498
+ 3rd Qu.: 3.998 3rd Qu.: 2.843 3rd Qu.: 2.0656 3rd Qu.: 3.937
+ Max. :10.782 Max. :10.629 Max. :11.0311 Max. :10.180
+ HSPC_048 HSPC_049 HSPC_050 HSPC_051
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 1.108 Median : 1.275 Median : 0.000 Median : 0.9757
+ Mean : 2.289 Mean : 2.453 Mean : 2.673 Mean : 2.2693
+ 3rd Qu.: 2.988 3rd Qu.: 3.819 3rd Qu.: 5.772 3rd Qu.: 3.1644
+ Max. :10.335 Max. :11.844 Max. :11.301 Max. :10.8692
+ HSPC_052 HSPC_053 HSPC_054 HSPC_055
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.509 Median : 0.818 Median : 0.000 Median : 0.000
+ Mean : 2.561 Mean : 2.684 Mean : 2.107 Mean : 1.959
+ 3rd Qu.: 4.644 3rd Qu.: 5.937 3rd Qu.: 2.568 3rd Qu.: 2.573
+ Max. :11.674 Max. :11.624 Max. :10.770 Max. :11.105
+ HSPC_056 HSPC_057 HSPC_058 HSPC_060
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 1.399 Median : 0.000
+ Mean : 2.295 Mean : 2.430 Mean : 2.296 Mean : 2.112
+ 3rd Qu.: 3.721 3rd Qu.: 3.806 3rd Qu.: 3.199 3rd Qu.: 2.201
+ Max. :11.627 Max. :10.575 Max. :11.134 Max. :10.631
+ HSPC_061 HSPC_062 HSPC_063 HSPC_064
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 1.515 Median : 1.101
+ Mean : 1.934 Mean : 2.129 Mean : 2.508 Mean : 2.696
+ 3rd Qu.: 2.489 3rd Qu.: 2.875 3rd Qu.: 4.895 3rd Qu.: 6.412
+ Max. :11.190 Max. :10.433 Max. :10.994 Max. :10.873
+ HSPC_065 HSPC_066 HSPC_067 HSPC_068
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.4852 Median : 0.000 Median : 1.441 Median : 0.000
+ Mean : 2.2676 Mean : 2.136 Mean : 2.480 Mean : 2.449
+ 3rd Qu.: 3.8217 3rd Qu.: 2.632 3rd Qu.: 3.548 3rd Qu.: 4.517
+ Max. :10.9023 Max. :11.608 Max. :11.147 Max. :10.901
+ HSPC_069 HSPC_070 HSPC_071 HSPC_072
+ Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.8949 Median : 0.9272 Median : 1.121
+ Mean : 2.406 Mean : 2.5826 Mean : 2.2844 Mean : 2.545
+ 3rd Qu.: 4.705 3rd Qu.: 5.4749 3rd Qu.: 3.2531 3rd Qu.: 4.939
+ Max. :11.258 Max. :11.6715 Max. :10.7886 Max. :11.397
+ HSPC_073 HSPC_074 HSPC_075 HSPC_076
+ Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.00 Median : 1.674 Median : 0.000
+ Mean : 2.491 Mean : 2.46 Mean : 2.413 Mean : 2.289
+ 3rd Qu.: 4.134 3rd Qu.: 3.40 3rd Qu.: 3.013 3rd Qu.: 2.550
+ Max. :11.844 Max. :11.66 Max. :11.976 Max. :12.136
+ HSPC_077 HSPC_078 HSPC_079 HSPC_080
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.6624 Median : 1.492 Median : 1.384 Median : 1.013
+ Mean : 2.4336 Mean : 2.637 Mean : 2.432 Mean : 2.881
+ 3rd Qu.: 5.4937 3rd Qu.: 5.472 3rd Qu.: 3.617 3rd Qu.: 7.220
+ Max. :11.6020 Max. :10.673 Max. :11.199 Max. :11.836
+ HSPC_081 HSPC_082 HSPC_083 HSPC_084
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.7671 Median : 0.000 Median : 1.896 Median : 1.128
+ Mean : 1.9227 Mean : 2.474 Mean : 2.864 Mean : 2.289
+ 3rd Qu.: 1.6349 3rd Qu.: 3.488 3rd Qu.: 5.101 3rd Qu.: 2.792
+ Max. :11.4681 Max. :11.962 Max. :10.865 Max. :11.834
+ HSPC_085 HSPC_087 HSPC_088 HSPC_089
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.157 Mean : 2.314 Mean : 2.202 Mean : 2.329
+ 3rd Qu.: 3.010 3rd Qu.: 3.245 3rd Qu.: 2.092 3rd Qu.: 3.246
+ Max. :10.809 Max. :10.976 Max. :11.362 Max. :11.301
+ HSPC_090 HSPC_094 HSPC_095 HSPC_096
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000
+ Median : 0.000 Median : 0.000 Median : 2.055 Median :0.000
+ Mean : 2.286 Mean : 2.186 Mean : 2.756 Mean :2.348
+ 3rd Qu.: 4.174 3rd Qu.: 2.002 3rd Qu.: 4.370 3rd Qu.:4.482
+ Max. :11.124 Max. :11.694 Max. :11.385 Max. :9.601
+ HSPC_098 HSPC_099 HSPC_100 HSPC_101
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.007
+ Mean : 2.209 Mean : 2.082 Mean : 2.313 Mean : 2.587
+ 3rd Qu.: 3.354 3rd Qu.: 2.505 3rd Qu.: 2.775 3rd Qu.: 5.334
+ Max. :11.070 Max. :10.200 Max. :11.452 Max. :11.456
+ HSPC_102 HSPC_103 HSPC_104 HSPC_105
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.111 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.210 Mean : 2.853 Mean : 2.099 Mean : 1.893
+ 3rd Qu.: 2.993 3rd Qu.: 6.123 3rd Qu.: 2.720 3rd Qu.: 2.129
+ Max. :11.153 Max. :11.328 Max. :10.746 Max. :10.721
+ HSPC_106 HSPC_107 HSPC_108 HSPC_109
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.595
+ Mean : 1.980 Mean : 2.279 Mean : 2.296 Mean : 2.420
+ 3rd Qu.: 2.425 3rd Qu.: 3.396 3rd Qu.: 3.361 3rd Qu.: 4.006
+ Max. :10.919 Max. :10.982 Max. :11.744 Max. :10.463
+ HSPC_110 HSPC_111 HSPC_114 HSPC_115
+ Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.9173 Median : 2.349
+ Mean : 2.159 Mean : 1.800 Mean : 1.8376 Mean : 2.943
+ 3rd Qu.: 2.667 3rd Qu.: 2.214 3rd Qu.: 1.8741 3rd Qu.: 6.223
+ Max. :11.121 Max. :11.109 Max. :10.4645 Max. :11.124
+ HSPC_117 HSPC_118 HSPC_119 HSPC_120
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.187
+ Mean : 1.919 Mean : 1.855 Mean : 2.289 Mean : 2.041
+ 3rd Qu.: 2.306 3rd Qu.: 2.387 3rd Qu.: 3.292 3rd Qu.: 2.610
+ Max. :14.579 Max. :11.119 Max. :12.534 Max. :11.438
+ HSPC_121 HSPC_122 HSPC_123 HSPC_125
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.803 Mean : 2.072 Mean : 2.200 Mean : 2.116
+ 3rd Qu.: 5.798 3rd Qu.: 2.140 3rd Qu.: 3.215 3rd Qu.: 2.409
+ Max. :11.320 Max. :11.013 Max. :11.163 Max. :11.368
+ HSPC_126 HSPC_127 HSPC_130 HSPC_131
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+ HSPC_334 HSPC_335 HSPC_336 HSPC_337
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+ Max. :11.592 Max. :11.076 Max. :11.246 Max. :10.205
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+ Max. :11.795 Max. :12.071 Max. :11.732 Max. :10.672
+ HSPC_477 HSPC_478 HSPC_479 HSPC_480
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+ Max. :13.948 Max. :10.722 Max. :11.8691 Max. :10.155
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+ Max. :11.271 Max. :11.518 Max. :11.646 Max. :10.366
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+ Max. :11.665 Max. :10.727 Max. :11.4591 Max. :11.5376
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+ HSPC_508 HSPC_509 HSPC_510 HSPC_512
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+ Max. :12.0747 Max. :10.603 Max. :10.885 Max. :12.492
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+ Max. :11.783 Max. :12.193 Max. :12.1718 Max. :11.838
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+ Max. :12.289 Max. :11.712 Max. :10.364 Max. :12.5906
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+ Max. :12.105 Max. :10.870 Max. :12.017 Max. :12.183
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+ Max. :11.549 Max. :11.255 Max. :11.5862 Max. :11.696
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+ Max. :10.793 Max. :10.305 Max. :11.359 Max. :11.755
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+ Max. :12.2963 Max. :11.844 Max. :10.983 Max. :10.7976
+ HSPC_545 HSPC_546 HSPC_547 HSPC_548
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+ Max. :11.815 Max. :11.235 Max. :11.8801 Max. :11.955
+ HSPC_549 HSPC_550 HSPC_551 HSPC_552
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+ 3rd Qu.: 2.289 3rd Qu.: 4.686 3rd Qu.: 4.007 3rd Qu.: 2.669
+ Max. :11.827 Max. :12.064 Max. :11.874 Max. :11.581
+ HSPC_553 HSPC_554 HSPC_555 HSPC_556
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+ HSPC_563 HSPC_566 HSPC_567 HSPC_568
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+ Max. :11.391 Max. :11.167 Max. :10.239 Max. :10.586
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+ Max. :11.1096 Max. :11.2534 Max. :11.008 Max. :11.7228
+ HSPC_762 HSPC_764 HSPC_765 HSPC_766
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+ Max. :12.043 Max. :11.003 Max. :12.757 Max. :10.2002
+ HSPC_767 HSPC_768 HSPC_769 HSPC_770
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+ 3rd Qu.: 1.9730 3rd Qu.: 3.325 3rd Qu.: 3.289 3rd Qu.: 3.122
+ Max. :11.3033 Max. :10.958 Max. :11.176 Max. :10.497
+ HSPC_771 HSPC_772 HSPC_773 HSPC_774
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+ Mean : 2.527 Mean : 2.283 Mean : 1.8628 Mean : 2.5263
+ 3rd Qu.: 3.342 3rd Qu.: 2.828 3rd Qu.: 1.9851 3rd Qu.: 5.4694
+ Max. :11.156 Max. :10.625 Max. :10.7274 Max. :10.7701
+ HSPC_776 HSPC_777 HSPC_778 HSPC_780
+ Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 1.053 Median :1.435 Median : 1.178
+ Mean : 2.315 Mean : 2.110 Mean :2.130 Mean : 2.476
+ 3rd Qu.: 3.788 3rd Qu.: 2.673 3rd Qu.:3.488 3rd Qu.: 3.769
+ Max. :11.105 Max. :11.646 Max. :9.535 Max. :11.265
+ HSPC_781 HSPC_782 HSPC_783 HSPC_784
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.050
+ Mean : 1.911 Mean : 2.416 Mean : 2.254 Mean : 2.175
+ 3rd Qu.: 2.884 3rd Qu.: 3.872 3rd Qu.: 2.548 3rd Qu.: 2.468
+ Max. :11.445 Max. :10.161 Max. :10.970 Max. :10.958
+ HSPC_785 HSPC_786 HSPC_787 HSPC_788
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 0.000 Median : 1.148 Median : 0.000 Median : 0.9386
+ Mean : 2.230 Mean : 2.467 Mean : 2.100 Mean : 1.9749
+ 3rd Qu.: 2.466 3rd Qu.: 3.899 3rd Qu.: 2.991 3rd Qu.: 2.6662
+ Max. :11.041 Max. :11.080 Max. :10.690 Max. :11.1078
+ HSPC_789 HSPC_790 HSPC_791 HSPC_794
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.181 Median : 1.353 Median : 1.790 Median : 1.113
+ Mean : 2.225 Mean : 2.255 Mean : 2.699 Mean : 2.225
+ 3rd Qu.: 2.876 3rd Qu.: 2.852 3rd Qu.: 4.931 3rd Qu.: 2.768
+ Max. :11.245 Max. :11.558 Max. :11.104 Max. :11.118
+ HSPC_795 HSPC_796 HSPC_797 HSPC_798
+ Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.8317 Median : 0.7001 Median : 0.8722 Median : 1.531
+ Mean : 2.3985 Mean : 2.6865 Mean : 2.6172 Mean : 2.485
+ 3rd Qu.: 3.4461 3rd Qu.: 5.6688 3rd Qu.: 5.3078 3rd Qu.: 3.098
+ Max. :11.0956 Max. :11.0829 Max. :11.4339 Max. :10.933
+ HSPC_799 HSPC_800 HSPC_801 HSPC_802
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.033
+ Mean : 2.179 Mean : 2.173 Mean : 2.427 Mean : 2.613
+ 3rd Qu.: 3.517 3rd Qu.: 2.865 3rd Qu.: 4.665 3rd Qu.: 3.780
+ Max. :11.666 Max. :11.263 Max. :10.905 Max. :10.864
+ HSPC_803 HSPC_804 HSPC_806 HSPC_807
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 0.000 Median : 0.000 Median : 2.103 Median : 0.7501
+ Mean : 2.395 Mean : 2.301 Mean : 2.222 Mean : 2.2476
+ 3rd Qu.: 3.883 3rd Qu.: 3.167 3rd Qu.: 3.445 3rd Qu.: 2.3481
+ Max. :10.766 Max. :11.298 Max. :10.326 Max. :11.2700
+ HSPC_808 HSPC_809 HSPC_810 HSPC_812
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 0.6619 Median : 1.788 Median : 1.651 Median : 0.8459
+ Mean : 2.1677 Mean : 2.544 Mean : 2.471 Mean : 2.2960
+ 3rd Qu.: 2.5355 3rd Qu.: 3.730 3rd Qu.: 3.662 3rd Qu.: 2.6906
+ Max. :10.9302 Max. :11.791 Max. :10.829 Max. :11.5500
+ HSPC_813 HSPC_814 HSPC_815 HSPC_816
+ Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.6278 Median : 1.110 Median : 0.9631 Median : 1.346
+ Mean : 2.2448 Mean : 2.762 Mean : 2.4587 Mean : 2.341
+ 3rd Qu.: 2.3066 3rd Qu.: 5.996 3rd Qu.: 3.4228 3rd Qu.: 2.842
+ Max. :12.0043 Max. :10.406 Max. :11.4527 Max. :11.151
+ HSPC_818 HSPC_819 HSPC_820 HSPC_821
+ Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.9967 Median : 1.365 Median : 0.8099 Median : 1.382
+ Mean : 2.3081 Mean : 2.426 Mean : 2.1063 Mean : 2.532
+ 3rd Qu.: 2.9942 3rd Qu.: 3.632 3rd Qu.: 2.4643 3rd Qu.: 3.462
+ Max. :11.9931 Max. :10.672 Max. :11.2412 Max. :12.126
+ HSPC_822 HSPC_824 HSPC_825 HSPC_826
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.387 Median : 0.000 Median : 1.386 Median : 1.324
+ Mean : 2.503 Mean : 2.084 Mean : 2.162 Mean : 2.398
+ 3rd Qu.: 3.799 3rd Qu.: 2.342 3rd Qu.: 2.897 3rd Qu.: 3.150
+ Max. :11.892 Max. :11.365 Max. :11.498 Max. :11.198
+ HSPC_827 HSPC_828 HSPC_831 HSPC_832
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.746 Median : 1.003 Median : 1.304 Median : 1.035
+ Mean : 2.239 Mean : 2.145 Mean : 2.589 Mean : 2.384
+ 3rd Qu.: 2.638 3rd Qu.: 2.326 3rd Qu.: 3.866 3rd Qu.: 3.450
+ Max. :12.101 Max. :10.710 Max. :10.839 Max. :10.686
+ HSPC_833 HSPC_834 HSPC_835 HSPC_836
+ Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.7166 Median : 0.9245 Median : 1.006 Median : 0.000
+ Mean : 2.3553 Mean : 2.0872 Mean : 2.552 Mean : 2.471
+ 3rd Qu.: 3.9364 3rd Qu.: 2.4568 3rd Qu.: 4.034 3rd Qu.: 3.994
+ Max. :11.1695 Max. :11.1803 Max. :11.779 Max. :11.316
+ HSPC_837 HSPC_838 HSPC_839 HSPC_840
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.312 Median : 0.9838 Median : 1.083 Median : 1.867
+ Mean : 2.590 Mean : 2.5281 Mean : 2.380 Mean : 2.548
+ 3rd Qu.: 4.443 3rd Qu.: 3.5551 3rd Qu.: 3.743 3rd Qu.: 3.609
+ Max. :10.672 Max. :11.2707 Max. :10.966 Max. :10.867
+ HSPC_841 HSPC_842 HSPC_843 HSPC_844
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.439 Median : 1.774 Median : 1.257 Median : 1.584
+ Mean : 2.408 Mean : 2.380 Mean : 2.845 Mean : 2.627
+ 3rd Qu.: 3.494 3rd Qu.: 3.490 3rd Qu.: 6.768 3rd Qu.: 3.951
+ Max. :10.930 Max. :11.137 Max. :11.933 Max. :11.446
+ HSPC_845 HSPC_846 HSPC_848 HSPC_849
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.227 Median : 1.401 Median : 0.000 Median : 1.602
+ Mean : 2.464 Mean : 2.240 Mean : 2.152 Mean : 2.402
+ 3rd Qu.: 3.377 3rd Qu.: 2.920 3rd Qu.: 2.554 3rd Qu.: 2.920
+ Max. :10.535 Max. :11.519 Max. :11.266 Max. :11.678
+ HSPC_851 HSPC_852
+ Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000
+ Mean : 2.319 Mean : 2.143
+ 3rd Qu.: 3.373 3rd Qu.: 2.901
+ Max. :11.602 Max. :11.469
+Hmmmm, not very useful. We are only seeing the minimums. This is because we have 701 cells - we only have 6 samples for the frogs. Le’s at least get a summary for the first few columns
+🎬 Get a quick overview the first 20 columns:
+ ensembl_gene_id HSPC_001 HSPC_002 HSPC_003
+ Length:280 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Mode :character Median : 0.000 Median : 0.000 Median : 0.9929
+ Mean : 2.143 Mean : 1.673 Mean : 2.5964
+ 3rd Qu.: 2.120 3rd Qu.: 2.239 3rd Qu.: 6.1559
+ Max. :12.567 Max. :11.976 Max. :11.1138
+ HSPC_004 HSPC_006 HSPC_008 HSPC_009
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000
+ Median : 0.000 Median : 1.276 Median : 0.000 Median :0.000
+ Mean : 1.851 Mean : 2.338 Mean : 2.375 Mean :2.220
+ 3rd Qu.: 2.466 3rd Qu.: 3.536 3rd Qu.: 3.851 3rd Qu.:3.594
+ Max. :11.133 Max. :10.014 Max. :11.574 Max. :9.997
+ HSPC_011 HSPC_012 HSPC_014 HSPC_015
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 1.750 Median : 0.000 Median : 0.000
+ Mean : 2.285 Mean : 2.431 Mean : 2.295 Mean : 2.515
+ 3rd Qu.: 3.193 3rd Qu.: 3.741 3rd Qu.: 3.150 3rd Qu.: 3.789
+ Max. :11.260 Max. :10.905 Max. :11.051 Max. :10.751
+ HSPC_016 HSPC_017 HSPC_018 HSPC_020
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.9488 Median : 0.000 Median : 1.248 Median : 0.000
+ Mean : 2.6115 Mean : 2.146 Mean : 2.710 Mean : 2.509
+ 3rd Qu.: 5.9412 3rd Qu.: 2.357 3rd Qu.: 6.006 3rd Qu.: 4.470
+ Max. :11.3082 Max. :12.058 Max. :11.894 Max. :11.281
+ HSPC_021 HSPC_022 HSPC_023 HSPC_024
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.170 Mean : 2.287 Mean : 2.314 Mean : 2.195
+ 3rd Qu.: 2.996 3rd Qu.: 3.351 3rd Qu.: 2.749 3rd Qu.: 2.944
+ Max. :10.709 Max. :11.814 Max. :12.113 Max. :11.279
+Notice that: - the maximum value is much less high and has decimals. That logged (to base 2) normalised counts, not raw counts as they are in the frog data set. - the minimum count is 0 - at least some of the medians are zeros - there must be quite a lot of zeros - the few columns we can see are roughly similar - it would not be very practical to plot the distribution of values in cell cell using facet_wrap()
.
In this data set, there is even more of an advantage of using the pivot_longer()
, group_by()
and summarise()
approach. We will be to open the dataframe in the Viewer and make plots to examine whether the distributions are similar across cells.
🎬 Summarise all the cells:
+hspc_summary <- hspc |>
+ pivot_longer(cols = -ensembl_gene_id,
+ names_to = "cell",
+ values_to = "expr") |>
+ group_by(cell) |>
+ summarise(min = min(expr),
+ lowerq = quantile(expr, 0.25),
+ mean = mean(expr),
+ median = median(expr),
+ sd = sd(expr),
+ upperq = quantile(expr, 0.75),
+ max = max(expr),
+ n_zero = sum(expr == 0))
Notice that I have used cell
as the column name rather than sample
and expr
(expression) rather than count
. I’ve also added the standard deviation.
🎬 View the hspc_summary
dataframe
All cells have quite a few zeros and the lower quartile is 0 for al cells, i.e., every cell has many genes with zero expression.
+To get a better understanding of the distribution of expressions in cells we can create a ggplot using the pointrange geom. Pointrange puts a dot at the mean and a line between a minimum and a maximum such as +/-s.d. Not unlike a boxplot but when you need the boxes too be very narrow!
+🎬 Create a pointrange plot.
+hspc_summary |>
+ ggplot(aes(x = cell, y = mean)) +
+ geom_pointrange(aes(ymin = mean - sd,
+ ymax = mean + sd ),
+ size = 0.1)
You will need to use the Zoom button to pop the plot window out so you can make it as wide as possible
+The things to notice are:
+The default order of cell
is alphabetical. It can be easier to see these (non) effects if we order the lines by size
🎬 Order a pointrange plot with reorder(variable_to_order, order_by)
.
hspc_summary |>
+ ggplot(aes(x = reorder(cell, mean), y = mean)) +
+ geom_pointrange(aes(ymin = mean - sd,
+ ymax = mean + sd ),
+ size = 0.1)
reorder()
arranges cell
in increasing size of mean
It is more important to remove odd samples/cells when you have relatively few of them.
+🎬 Write hspc_summary
to a file called “hspc_summary.csv”:
all samples are fine; some genes with low counts should be removed
+🎬 Make a script file called cont-fgf-s30.R
. This will a be commented analysis of the control vs FGF at S30 comparison. You will build on this each workshop and be able to use it as a template to examine other comparisons. Copy in the appropriate code and comments from workshop-1.R
. Edit to improve your comments where your understanding has developed since you made them.
🎬 Save the files to data-raw
NOTES - to be checked removed once drafted 🎬 Make a note of the cut off value that seems appropriate. I’m going to use 10 (i.e., 1 on the graph). I think 3 (0.5 on the graph) would be a reasonable choice too. Don’t attempt to be over precise. - what kind of values are they, how many missing - qc plots - removing the useless, writing to files
🎬
You’re finished!