diff --git a/core/week-1/images/future_you.png b/core/week-1/images/future_you.png new file mode 100644 index 0000000..4bf8a20 Binary files /dev/null and b/core/week-1/images/future_you.png differ diff --git a/core/week-1/images/xkcd-comic-file-names.png b/core/week-1/images/xkcd-comic-file-names.png new file mode 100644 index 0000000..885acdc Binary files /dev/null and b/core/week-1/images/xkcd-comic-file-names.png differ diff --git a/core/week-1/workshop.html b/core/week-1/workshop.html index 0183a68..c35cb8b 100644 --- a/core/week-1/workshop.html +++ b/core/week-1/workshop.html @@ -8,7 +8,7 @@ -Data Analysis for Group Project - Workshop - 🚧 still in Construction +Data Analysis for Group Project - Workshop - @@ -73,8 +69,7 @@ - - - - - +} -
-
-
-
-
- -
-
+

Independent Study to consolidate this week

Core 2

@@ -293,24 +272,18 @@

Independent Study to consolidate this week

-
- -
-

Set up

+

Set up

If you have just opened RStudio you will want to load the packages and import the data.

-
library(tidyverse)
-library(readxl)
+
  1. 💻 xx.
-
- -
-
- - \ No newline at end of file diff --git a/core/week-2/workshop.html b/core/week-2/workshop.html index e03b88b..da9ab37 100644 --- a/core/week-2/workshop.html +++ b/core/week-2/workshop.html @@ -1,13 +1,10 @@ - + - - - Data Analysis for Group Project - Workshop - @@ -58,8 +54,7 @@ - - - - - +} -
-
-
-
-
- -
-
+

Workshop

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

@@ -301,57 +279,26 @@

Workshop

-
- -
-

Introduction

-
-

Session overview

+

Introduction

+

Session overview

In this workshop you will

-
-
-
- -
-
-Key -
-
-
- -
-
-
-
-
-

Getting started

-
-
-

Exercises

+

File formats

+

Data files. - Sequences data - Image data - Structure data

+

Similarities and differences

🎬

You’re finished!

-
-
-

🥳 Well Done! 🎉

-
-
-

Independent study following the workshop

+

🥳 Well Done! 🎉

+

Independent study following the workshop

Consolidate

-
-
-

The Code file

+

The Code file

These contain all the code needed in the workshop even where it is not visible on the webpage.

The workshop.qmd file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interleaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Qmd file in RStudio. Alternatively, View in Browser. Coding and thinking answers are marked with #---CODING ANSWER--- and #---THINKING ANSWER---

Pages made with R (R Core Team 2023), Quarto (Allaire et al. 2022), knitr (Xie 2022), kableExtra (Zhu 2021)

-
-
- - +
-
-

References

+

References

Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2022. Quarto. https://doi.org/10.5281/zenodo.5960048.
@@ -364,8 +311,7 @@

The Code file

Zhu, Hao. 2021. “kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.” https://CRAN.R-project.org/package=kableExtra.
-
-
- - \ No newline at end of file diff --git a/core/week-6/study_after_workshop.html b/core/week-6/study_after_workshop.html index 9c9abcb..5e00dcd 100644 --- a/core/week-6/study_after_workshop.html +++ b/core/week-6/study_after_workshop.html @@ -1,13 +1,10 @@ - + - - - Data Analysis for Group Project - Independent Study to consolidate this week - @@ -73,8 +69,7 @@ - - - - - +} -
-
-
-
-
- -
-
+

Independent Study to consolidate this week

Core 3 Reproducible Reporting

@@ -293,24 +272,18 @@

Independent Study to consolidate this week

-
- -
-

Set up

+

Set up

If you have just opened RStudio you will want to load the packages and import the data.

-
library(tidyverse)
-library(readxl)
+
  1. 💻 xx.
-
- -
-
- - \ No newline at end of file diff --git a/core/week-6/workshop.html b/core/week-6/workshop.html index b8dd6fb..d822f2f 100644 --- a/core/week-6/workshop.html +++ b/core/week-6/workshop.html @@ -1,13 +1,10 @@ - + - - - Data Analysis for Group Project - Workshop - @@ -58,8 +54,7 @@ - - - - - +} -
-
-
-
-
- -
-
+

Workshop

Reproducible Reporting

@@ -301,12 +280,8 @@

Workshop

-
- -
-

Introduction

-
-

Session overview

+

Introduction

+

Session overview

In this workshop you will

@@ -321,37 +296,22 @@

Session overview

-
-
-
-

Getting started

-
-
-

Exercises

+

Getting started

+

Exercises

🎬

You’re finished!

-
-
-

🥳 Well Done! 🎉

-
-
-

Independent study following the workshop

+

🥳 Well Done! 🎉

+

Independent study following the workshop

Consolidate

-
-
-

The Code file

+

The Code file

These contain all the code needed in the workshop even where it is not visible on the webpage.

The workshop.qmd file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interleaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Qmd file in RStudio. Alternatively, View in Browser. Coding and thinking answers are marked with #---CODING ANSWER--- and #---THINKING ANSWER---

Pages made with R (R Core Team 2023), Quarto (Allaire et al. 2022), knitr (Xie 2022), kableExtra (Zhu 2021)

-
-
- +
-
- -

References

+

References

Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2022. Quarto. https://doi.org/10.5281/zenodo.5960048.
@@ -364,8 +324,7 @@

The Code file

Zhu, Hao. 2021. “kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.” https://CRAN.R-project.org/package=kableExtra.
-
-
- - \ No newline at end of file diff --git a/omics/week-3/workshop.html b/omics/week-3/workshop.html index 0d477a0..86afa8e 100644 --- a/omics/week-3/workshop.html +++ b/omics/week-3/workshop.html @@ -1,13 +1,10 @@ - + - - - Data Analysis for Group Project - Workshop - @@ -92,8 +88,7 @@ - - - - - +} -
-
-
-
-
- -
-
+

Workshop

Omics 1: Hello data!

@@ -367,20 +360,12 @@

Workshop

-
- -
-

Introduction

-
-

Session overview

+

Introduction

+

Session overview

In this workshop you will learn what steps to take to get a good understanding of your ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.

You should examine all three data sets because the comparisons will give you a stronger understanding of your own project data.

-
-
-
-

Exercises

-
-

Set up a Project

+

Exercises

+

Set up a Project

🎬 Start RStudio from the Start menu

🎬 Make an RStudio project. Be deliberate about where you create it so that it is a good place for you

🎬 Use the Files pane to make new folders for the data. I suggest data-raw and data-processed

@@ -388,11 +373,9 @@

Set up a Project

🎬 Record what you do and what you find out. All of it!

🎬 Load tidyverse (Wickham et al. 2019) for importing, summarising, plotting and filtering.

-
library(tidyverse)
+
-
-
-

Examine the data in a spreadsheet

+

Examine the data in a spreadsheet

These are the three datasets. Each set compromises several files.

🐸 Frog development data:

    @@ -421,27 +404,23 @@

    Examine
  • Google an id. Where does your search take you? How much information is available?

🎬 Did you record all that??

-
-
-

Import

+

Import

Now let’s get the data into R and visualise it.

🎬 Import xlaevis_counts_S30.csv, surfaceome_hspc.csv and xxxxxxxx

-
# 🐸 import the s30 data
-s30 <- read_csv("data-raw/xlaevis_counts_S30.csv")
+
# 🐸 import the s30 data
+s30 <- read_csv("data-raw/xlaevis_counts_S30.csv")
-
# 🐭 import the hspc data
-hspc <- read_csv("data-raw/surfaceome_hspc.csv")
+
# 🐭 import the hspc data
+hspc <- read_csv("data-raw/surfaceome_hspc.csv")
-
# 🍂 xxxx import the xxxx data
-# prog <- read_csv("")
+
# 🍂 xxxx import the xxxx data
+# prog <- read_csv("")

🎬 Check these have the number of rows and column you were expecting and that column types and names are as expected.

-
-
-

Explore

+

Explore

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?
    • @@ -456,19 +435,17 @@

    Explore

  • 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.

-
-

Distribution of values across the whole dataset

-

In all data sets, the values are spread over multiple columns so in order to plot the distribution as a whole, we will need to first use pivot_longer() to put the data in ‘tidy’ format (Wickham 2014) by stacking the columns. We could save a copy of the stacked data and then plot it, but here, I have just piped the stacked data straight into ggplot().

-
-

🐸 Frogs

-

🎬 Pivot the counts (stack the columns) so all the counts are in a single column (count) and pipe into ggplot() to create a histogram:

+

Distribution of values across the whole dataset

+

In all data sets, the values are spread over multiple columns so in order to plot the distribution as a whole, we will need to first use pivot_longer() to put the data in ‘tidy’ format (Wickham 2014) by stacking the columns. We could save a copy of the stacked data and then plot it, but here, I have just piped the stacked data straight into ggplot().

+

🐸 Frogs

+

🎬 Pivot the counts (stack the columns) so all the counts are in a single column (count) and pipe into ggplot() to create a histogram:

-
s30 |>
-  pivot_longer(cols = -xenbase_gene_id,
-               names_to = "sample",
-               values_to = "count") |>
-  ggplot(aes(x = count)) +
-  geom_histogram()
+
s30 |>
+  pivot_longer(cols = -xenbase_gene_id,
+               names_to = "sample",
+               values_to = "count") |>
+  ggplot(aes(x = count)) +
+  geom_histogram()

@@ -476,47 +453,41 @@

🐸 Frogs

This data is very skewed - there are so many low values that we can’t see the the tiny bars for the higher values. Logging the counts is a way to make the distribution more visible.

🎬 Repeat the plot on log of the counts.

-
s30 |>
-  pivot_longer(cols = -xenbase_gene_id,
-               names_to = "sample",
-               values_to = "count") |>
-  ggplot(aes(x = log10(count))) +
-  geom_histogram()
+
s30 |>
+  pivot_longer(cols = -xenbase_gene_id,
+               names_to = "sample",
+               values_to = "count") |>
+  ggplot(aes(x = log10(count))) +
+  geom_histogram()

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 peak at zero suggests quite a few counts of 1. We would expect we would expect the distribution of counts to be roughly log normal because this is expression of all the genes in the genome1. That small peak near the low end suggests that these lower counts might be anomalies.

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 samples and genes.

-
-
-

🐭 Mice

-

🎬 Pivot the expression values (stack the columns) so all the counts are in a single column (expr) and pipe into ggplot() to create a histogram:

+

🐭 Mice

+

🎬 Pivot the expression values (stack the columns) so all the counts are in a single column (expr) and pipe into ggplot() to create a histogram:

-
hspc |>
-  pivot_longer(cols = -ensembl_gene_id,
-               names_to = "cell",
-               values_to = "expr") |> 
-  ggplot(aes(x = expr)) +
-  geom_histogram()
+
hspc |>
+  pivot_longer(cols = -ensembl_gene_id,
+               names_to = "cell",
+               values_to = "expr") |> 
+  ggplot(aes(x = expr)) +
+  geom_histogram()

This is a very striking distribution. Is it what we are expecting? Again,the excess number of low values is almost certainly anomalous. They will be inaccurate measure and we will want to exclude expression values below (about) 1. We will revisit this after we have considered the distribution of expression across cells and genes.

What about the bimodal appearance of the the ‘real’ values? If we had the whole genome we would not expect to see such a pattern - we’d expect to see a roughly normal distribution2. However, this is a subset of the genome and the nature of the subsetting has had an influence here. These are a subset of cell surface proteins that show a significant difference between at least two of twelve cell subtypes. That is, all of these genes are either high or low.

-
-
-
-

Distribution of values across the sample/cells

-
-

🐸 Frog 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.

+

Distribution of values across the sample/cells

+

🐸 Frog 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:

-
# examine all the columns quickly
-# works well with smaller numbers of column
-summary(s30)
+
# examine all the columns quickly
+# works well with smaller numbers of column
+summary(s30)
 xenbase_gene_id       S30_C_5            S30_C_6            S30_C_A        
  Length:11893       Min.   :     0.0   Min.   :     0.0   Min.   :     0.0  
@@ -538,13 +509,13 @@ 
🐸 Frog samples

 
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 0 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))

+
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)`
@@ -554,15 +525,15 @@ 
🐸 Frog samples

 #   `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.

+
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))

+
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
@@ -578,18 +549,18 @@ 
🐸 Frog samples

 
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))

+
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
@@ -603,46 +574,47 @@ 
🐸 Frog samples

 

 

 
The mean count ranges from 260 to 426.

-
One advantage this has over using summary() is that the output is a dataframe. For results, this is useful, and makes it easier to:

+
One advantage this has over using summary() is that the output is a dataframe. For results, this is useful, and makes it easier to:

 
    
 
  • write to file
    • 
-
  • use in ggplot()
    • 
+
  • use in ggplot()
+
    • 
 
  • format in a Quarto report
    • 
 

 
🎬 Save the summary as a dataframe, s30_summary_samp.

-
We can write to file using write_csv()

+
We can write to file using write_csv()

 
🎬 Write s30_summary_samp_by to a file called “s30_summary_samp_by.csv”:

 

-
write_csv(s30_summary_samp_by, file = "data-processed/s30_summary_samp_by.csv")

+
write_csv(s30_summary_samp_by, file = "data-processed/s30_summary_samp_by.csv")

 

 
Error in eval(expr, envir, enclos): object 's30_summary_samp_by' not found

 

 

-
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()

+
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()

 
🎬 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)

+
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 we are not limited to using geom_histogram(). geom_density() gives us a smooth distribution.

+
We have many values (11893) so we are not limited to using geom_histogram(). geom_density() gives 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)

+
s30 |>
+  pivot_longer(cols = -xenbase_gene_id,
+               names_to = "sample",
+               values_to = "count") |>
+  ggplot(aes(log10(count))) +
+  geom_density() +
+  facet_wrap(. ~ sample, nrow = 3)

 

 

 

@@ -654,13 +626,11 @@ 
🐸 Frog samples

 
  • since we would expect the distribution of counts in each sample to be roughly log normal so that the small rise near the low end, even before the peak at zero, suggests that these lower counts might be anomalies.
    • 
 
 
    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).
    
-
    -
    -

    🐭 Mouse cells

    -

    We used the summary() function to get an overview of the columns in the frog data. Let’s try that here.

    +

    🐭 Mouse cells

    +

    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:

    -
    summary(hspc)
    +
    summary(hspc)
     ensembl_gene_id       HSPC_001         HSPC_002         HSPC_003      
  Length:280         Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
@@ -1899,7 +1869,7 @@ 
    🐭 Mouse cells
    
 
    Hmmmm, did you get all that? Nope, me neither! We have 701 cells but we only have 6 samples for the frogs. We will need a different approach to get an overview but I find it is still useful to look at the few columns
    
 
    🎬 Get a quick overview the first 20 columns:
    
 
    
-
    summary(hspc[1:20])
    
+
    summary(hspc[1:20])
    
 
    
 
     ensembl_gene_id       HSPC_001         HSPC_002         HSPC_003      
  Length:280         Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
@@ -1944,24 +1914,24 @@ 
    🐭 Mouse cells
    
 
  • a minimum value of 0 appears in all 20 columns - perhaps that is true across the whole dataset (or at least common)
    • 
 
  • at least some of the medians are zeros so 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 distributions of values in cell cell using facet_wrap().
    • 
+
  • it would not be very practical to plot the distributions 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 able to open the dataframe in the Viewer and make plots to examine whether the distributions are similar across cells.
    
+
    In this data set, there is even more of an advantage of using the pivot_longer(), group_by() and summarise() approach. We will be able 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_samp <- 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))
    
+
    hspc_summary_samp <- 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_samp dataframe (click on it in the environment).
    
@@ -1969,11 +1939,11 @@ 
    🐭 Mouse cells
    
 
    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 +/- one s.d. Not unlike a boxplot, but when you need the boxes too be very narrow!
    
 
    🎬 Create a pointrange plot.
    
 
    
-
    hspc_summary_samp |> 
-  ggplot(aes(x = cell, y = mean)) +
-  geom_pointrange(aes(ymin = mean - sd, 
-                      ymax = mean + sd ),
-                  size = 0.1)
    
+
    hspc_summary_samp |> 
+  ggplot(aes(x = cell, y = mean)) +
+  geom_pointrange(aes(ymin = mean - sd, 
+                      ymax = mean + sd ),
+                  size = 0.1)
    
 
    
 
    
 
    
@@ -1987,40 +1957,36 @@ 
    🐭 Mouse cells
    
 
    The default order of cell is alphabetical. It can be easier to see these (non-) effects if we order the lines by the size of the mean.
    
 
    🎬 Order a pointrange plot with reorder(variable_to_order, order_by).
    
 
    
-
    hspc_summary_samp |> 
-  ggplot(aes(x = reorder(cell, mean), y = mean)) +
-  geom_pointrange(aes(ymin = mean - sd, 
-                      ymax = mean + sd ),
-                  size = 0.1)
    
+
    hspc_summary_samp |> 
+  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
    
+
    reorder() arranges cell in increasing size of mean
    
 
    🎬 Write hspc_summary_samp to a file called “hspc_summary_samp.csv”:
    
-
    -
    -
    -

    Distribution of values across the genes

    -
    -

    🐸 Frog genes

    +

    Distribution of values across the genes

    +

    🐸 Frog genes

    There are lots of genes in this dataset therefore we will take the same approach as that we took for the distributions across mouse cells. We will pivot the data to tidy and then summarise the counts for each gene.

    🎬 Summarise the counts for each genes:

    -
    s30_summary_gene <- s30 |>
-  pivot_longer(cols = -xenbase_gene_id,
-               names_to = "sample",
-               values_to = "count") |>
-  group_by(xenbase_gene_id) |>
-  summarise(min = min(count),
-            lowerq = quantile(count, 0.25),
-            sd = sd(count),
-            mean = mean(count),
-            median = median(count),
-            upperq = quantile(count, 0.75),
-            max = max(count),
-            total = sum(count),
-            n_zero = sum(count == 0))
    +
    s30_summary_gene <- s30 |>
+  pivot_longer(cols = -xenbase_gene_id,
+               names_to = "sample",
+               values_to = "count") |>
+  group_by(xenbase_gene_id) |>
+  summarise(min = min(count),
+            lowerq = quantile(count, 0.25),
+            sd = sd(count),
+            mean = mean(count),
+            median = median(count),
+            upperq = quantile(count, 0.75),
+            max = max(count),
+            total = sum(count),
+            n_zero = sum(count == 0))

    I have calculated the values we used before with one addition: the sum of the counts (total).

    🎬 View the s30_summary_gene dataframe.

    @@ -2032,13 +1998,13 @@

    🐸 Frog genes

    These should be filtered out because they are unreliable - or, at the least, uninformative. The goal of our downstream analysis will be to see if there is a signifcance difference in gene expression between the control and FGF-treated sibling. Since we have only three replicates in each group, having one or two unreliable, missing or zero values, makes such a determination impossible for a particular gene. We will use the total counts and the number of samples with non-zero values to filter our genes later.

    As we have a lot of genes, it is again helpful to plot the mean counts with pointrange to get an overview. We will plot the log of the counts - we saw earlier that logging made it easier to understand the distribution of counts over such a wide range. We will also order the genes from lowest to highest mean count.

    -

    🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():

    +

    🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():

    -
    s30_summary_gene |> 
-  ggplot(aes(x = reorder(xenbase_gene_id, mean), y = log10(mean))) +
-  geom_pointrange(aes(ymin = log10(mean - sd), 
-                      ymax = log10(mean + sd )),
-                  size = 0.1)
    +
    s30_summary_gene |> 
+  ggplot(aes(x = reorder(xenbase_gene_id, mean), y = log10(mean))) +
+  geom_pointrange(aes(ymin = log10(mean - sd), 
+                      ymax = log10(mean + sd )),
+                  size = 0.1)

    @@ -2046,26 +2012,24 @@

    🐸 Frog genes

    (Remember, the warning is expected since we have zeros).

    You can see we also have quite a few genes with means less than 1 (log below zero). Note that the variability between genes (average counts between 0 and 102586) is far greater than between samples (average counts from 260 to 426) which is exactly what we would expect to see.

    🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:

    -
    -
    -

    🐭 Mouse genes

    +

    🐭 Mouse genes

    There are fewer genes in this dataset, but still more than you can understand without the overview provided by a plot. We will again pivot the data to tidy and then summarise the expression for each gene.

    🎬 Summarise the expression for each genes:

    -
    hspc_summary_gene <- hspc |>
-  pivot_longer(cols = -ensembl_gene_id,
-               names_to = "cell",
-               values_to = "expr") |>
-  group_by(ensembl_gene_id) |>
-  summarise(min = min(expr),
-            lowerq = quantile(expr, 0.25),
-            sd = sd(expr),
-            mean = mean(expr),
-            median = median(expr),
-            upperq = quantile(expr, 0.75),
-            max = max(expr),
-            total = sum(expr),
-            n_zero = sum(expr == 0))
    +
    hspc_summary_gene <- hspc |>
+  pivot_longer(cols = -ensembl_gene_id,
+               names_to = "cell",
+               values_to = "expr") |>
+  group_by(ensembl_gene_id) |>
+  summarise(min = min(expr),
+            lowerq = quantile(expr, 0.25),
+            sd = sd(expr),
+            mean = mean(expr),
+            median = median(expr),
+            upperq = quantile(expr, 0.75),
+            max = max(expr),
+            total = sum(expr),
+            n_zero = sum(expr == 0))

    🎬 View the hspc_summary_gene dataframe. Remember these are normalised and logged (base 2) so we should not see very large values.

    Notice that we have:

    @@ -2075,92 +2039,66 @@

    🐭 Mouse genes

  • quite a few genes with zero in many cells but this matters less than zeros in the frog samples because we had just 6 samples and we have 701 cells.

    • As we have a lot of genes, it is again helpful to plot the mean expression with pointrange to get an overview. We do not need to log the values but ordering the genes will help.

    -

    🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():

    +

    🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():

    -
    hspc_summary_gene |> 
-  ggplot(aes(x = reorder(ensembl_gene_id, mean), y = mean))+
-  geom_pointrange(aes(ymin = mean - sd, 
-                      ymax = mean + sd),
-                  size = 0.1)
    +
    hspc_summary_gene |> 
+  ggplot(aes(x = reorder(ensembl_gene_id, mean), y = mean))+
+  geom_pointrange(aes(ymin = mean - sd, 
+                      ymax = mean + sd),
+                  size = 0.1)

    Note again that the variability between genes (average expression between 0.02 and and 10.03) is far greater than between cells (average expression from1.46 to 3.18) which is expected.

    🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:

    -
    -
    -
    -
    -

    Filtering for QC

    -
    -

    🐸 Frog filtering

    +

    Filtering for QC

    +

    🐸 Frog filtering

    Our samples look to be similarly well sequenced. There are no samples we should remove. However, some genes are not express or the expression values are so low in for a gene that they are uninformative. We will filter the s30_summary_gene dataframe to obtain a list of xenbase_gene_id we can use to filter s30.

    My suggestion is to include only the genes with counts in all 6 or 5 out of 6 samples and those with total counts above 20.

    🎬 Filter the summary by gene dataframe:

    -
    s30_summary_gene_filtered <- s30_summary_gene |> 
-  filter(total > 20) |> 
-  filter(n_zero < 2)
    +
    s30_summary_gene_filtered <- s30_summary_gene |> 
+  filter(total > 20) |> 
+  filter(n_zero < 2)

    🎬 Write the filtered summary by gene to file:

    -
    write_csv(s30_summary_gene_filtered, 
-          file = "data-processed/s30_summary_gene_filtered.csv")
    +
    write_csv(s30_summary_gene_filtered, 
+          file = "data-processed/s30_summary_gene_filtered.csv")

    🎬 Use the list of xenbase_gene_id in the filtered summary to filter the original dataset:

    -
    s30_filtered <- s30 |> 
-  filter(xenbase_gene_id %in%  s30_summary_gene_filtered$xenbase_gene_id)
    +
    s30_filtered <- s30 |> 
+  filter(xenbase_gene_id %in%  s30_summary_gene_filtered$xenbase_gene_id)

    🎬 Write the filtered original to file:

    -
    write_csv(s30_filtered, 
-          file = "data-processed/s30_filtered.csv")
    +
    write_csv(s30_filtered, 
+          file = "data-processed/s30_filtered.csv")
    -
    -
    -

    🐭 Mouse filtering

    +

    🐭 Mouse filtering

    I would take a different approach to filtering the single cell data. For the Frog samples we are examining the control and the FGF treated samples. This means have a low number of counts overall means the gene is not really expressed (detected) in any condition, and filtering out those genes is removing things that definitely are not interesting. For the mice, we have examined only one cell type but will be making comparisons between cells types. It may be that low expression of a gene in this cell type tells us something if that gene is highly expressed in another cell type. Instead, we will make statistical comparisons between the cell types and then filter based on overall expression, the difference in expression between cell types and whether that difference is significant.

    The number of “replicates” is also important. When you have only three in each group it is not possible to make statistical comparisons when several replicates are zero. This is less of an issue with single cell data.

    -
    -
    -
    -

    🤗 Look after future you!

    +

    🤗 Look after future you!

    You need only do the section for your own project data

    -
    -

    🐸 Frogs and future you

    +

    🐸 Frogs and future you

    🎬 Create a new Project, frogs-88H, populated with folders and your 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. Make sure you can close down RStudio, reopen it and run your whole script again.

    -
    -
    -

    🐭 Mice and future you

    +

    🐭 Mice and future you

    🎬 Create a new Project, mice-88H, populated with folders and your data. Make a script file called hspc-prog.R. This will a be commented analysis of the hspc cells vs the prog cells. At this point you will have only code for the hspc cells. 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. Make sure you can close down RStudio, reopen it and run your whole script again.

    -
    -
    -
    -
    -

    🥳 Finished

    +

    🥳 Finished

    Well Done!

    -
    -
    -

    Independent study following the workshop

    +

    Independent study following the workshop

    Consolidate

    -
    -
    -

    The Code file

    +

    The Code file

    These contain all the code needed in the workshop even where it is not visible on the webpage.

    The workshop.qmd file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interleaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Qmd file in RStudio. Alternatively, View in Browser. Coding and thinking answers are marked with #---CODING ANSWER--- and #---THINKING ANSWER---

    Pages made with R (R Core Team 2023), Quarto (Allaire et al. 2022), knitr (Xie 2022), kableExtra (Zhu 2021)

    -
    -
    - - - +
    -
    -

    References

    +

    References

    Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2022. Quarto. https://doi.org/10.5281/zenodo.5960048.
    @@ -2180,13 +2118,10 @@

    The Code file

    Zhu, Hao. 2021. “kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.” https://CRAN.R-project.org/package=kableExtra.

    Footnotes

    -

    1. This a result of the Central limit theorem,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.↩︎

    2. This a result of the Central limit theorem,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.↩︎

      3. -
    -
    - - - \ No newline at end of file diff --git a/search.json b/search.json index 1cd4c92..e2263f7 100644 --- a/search.json +++ b/search.json @@ -1,10 +1,220 @@ [ { - "objectID": "omics/week-3/overview.html", - "href": "omics/week-3/overview.html", - "title": "Overview", + "objectID": "about.html", + "href": "about.html", + "title": "About", "section": "", - "text": "xxxxx\n\nLearning objectives\n\ndd\ndd.\ndd\nd\n\n\n\nInstructions\n\nPrepare\n\n📖 Read how the data were generated and how they have been processed so far.\n\nWorkshop\n\n💻 Set up a Project\n💻 Import data\n💻 Explore the distribution of values across samples/cells and across genes/species\n💻 Perform quality control\n💻 Look after future you!\n\nConsolidate\n\n💻 Use the work you completed in the workshop as a template to apply to a new case." + "text": "About this site" + }, + { + "objectID": "core/week-1/study_after_workshop.html#msc-bioinformatics-students-doing-bio00070m", + "href": "core/week-1/study_after_workshop.html#msc-bioinformatics-students-doing-bio00070m", + "title": "Independent Study to consolidate this week", + "section": "MSc Bioinformatics students doing BIO00070M", + "text": "MSc Bioinformatics students doing BIO00070M" + }, + { + "objectID": "core/week-1/workshop.html", + "href": "core/week-1/workshop.html", + "title": "Workshop", + "section": "", + "text": "In this workshop we will discuss why reproducibility matters and how to organise your work to make it reproducible. We will cover:" + }, + { + "objectID": "core/week-1/workshop.html#session-overview", + "href": "core/week-1/workshop.html#session-overview", + "title": "Workshop", + "section": "", + "text": "In this workshop we will discuss why reproducibility matters and how to organise your work to make it reproducible. We will cover:" + }, + { + "objectID": "core/week-1/workshop.html#what-is-reproducibility", + "href": "core/week-1/workshop.html#what-is-reproducibility", + "title": "Workshop", + "section": "What is reproducibility?", + "text": "What is reproducibility?\n\nReproducible: Same data + same analysis = identical results. “… obtaining consistent results using the same input data; computational steps, methods, and code; and conditions of analysis. This definition is synonymous with”computational reproducibility” (National Academies of Sciences et al. 2019)\nReplicable: Different data + same analysis = qualitatively similar results. The work is not dependent on the specificities of the data.\nRobust: Same data + different analysis = qualitatively similar or identical results. The work is not dependent on the specificities of the analysis.\nGeneralisable: Different data + different analysis = qualitatively similar results and same conclusions. The findings can be generalised\n\n\n\n\nThe Turing Way's definitions of reproducible research" + }, + { + "objectID": "core/week-1/workshop.html#why-does-it-matter", + "href": "core/week-1/workshop.html#why-does-it-matter", + "title": "Workshop", + "section": "Why does it matter?", + "text": "Why does it matter?\n\n\n\nfutureself, CC-BY-NC, by Julen Colomb\n\n\n\nFive selfish reasons to work reproducibly. Alternatively, see the very entertaining talk\nMany high profile cases of work which did not reproduce e.g. Anil Potti unravelled by Baggerly and Coombes (2009)\nWill become standard in Science and publishing e.g OECD Global Science Forum Building digital workforce capacity and skills for data-intensive science OECD Global Science Forum (2020)" + }, + { + "objectID": "core/week-1/workshop.html#how-to-achieve-reproducibility", + "href": "core/week-1/workshop.html#how-to-achieve-reproducibility", + "title": "Workshop", + "section": "How to achieve reproducibility", + "text": "How to achieve reproducibility\n\nScripting\nOrganisation: Project-oriented workflows with file and folder structure, naming things\nDocumentation: Readme files, code comments, metadata, version control" + }, + { + "objectID": "core/week-1/workshop.html#rationale-for-scripting", + "href": "core/week-1/workshop.html#rationale-for-scripting", + "title": "Workshop", + "section": "Rationale for scripting?", + "text": "Rationale for scripting?\n\nScience is the generation of ideas, designing work to test them and reporting the results.\nWe ensure laboratory and field work is replicable, robust and generalisable by planning and recording in lab books and using standard protocols. Repeating results is still hard.\nWorkflows for computational projects, and the data analysis and reporting of other work can, and should, be 100% reproducible!\nScripting is the way to achieve this." + }, + { + "objectID": "core/week-1/workshop.html#project-oriented-workflow", + "href": "core/week-1/workshop.html#project-oriented-workflow", + "title": "Workshop", + "section": "Project-oriented workflow", + "text": "Project-oriented workflow\n\nuse folders to organise your work\nyou are aiming for structured, systematic and repeatable.\n\nExample\n-- liver_transcriptome/\n |__data\n |__raw/\n |__processed/\n |__images/\n |__R/\n |__reports/\n |__figures/" + }, + { + "objectID": "core/week-1/workshop.html#naming-things", + "href": "core/week-1/workshop.html#naming-things", + "title": "Workshop", + "section": "Naming things", + "text": "Naming things\n\n\n\ndocuments, CC-BY-NC, https://xkcd.com/1459/\n\n\nGuiding principle - names of files and directories should be systematic and readable by humans and machines. Have a convention!\nI suggest\n\nno spaces in names\nuse snake_case or kebab-case rather than CamelCase or dot.case\nuse all lower case except very occasionally where convention is otherwise, e.g., README, LICENSE\nordering: use left-padded numbers e.g., 01, 02….99 or 001, 002….999\ndates ISO 8601 format: 2020-10-16\nwrite down your conventions\n\n-- liver_transcriptome/\n |__data\n |__raw/\n |__2022-03-21_donor_1.csv\n |__2022-03-21_donor_2.csv\n |__2022-03-21_donor_3.csv\n |__2022-05-14_donor_1.csv\n |__2022-05-14_donor_2.csv\n |__2022-05-14_donor_3.csv\n |__processed/\n |__images/\n |__code/\n |__functions/\n |__summarise.R\n |__normalise.R\n |__theme_volcano.R\n |__01_data_processing.py\n |__02_exploratory.R\n |__03_modelling.R\n |__04_figures.R\n |__reports/\n |__01_report.qmd\n |__02_supplementary.qmd\n |__figures/\n |__01_volcano_donor_1_vs_donor_2.eps\n |__02_volcano_donor_1_vs_donor_3.eps" + }, + { + "objectID": "core/week-1/workshop.html#readme-files", + "href": "core/week-1/workshop.html#readme-files", + "title": "Workshop", + "section": "Readme files", + "text": "Readme files\nREADMEs are a form of documentation which have been widely used for a long time. They contain all the information about the other files in a directory. They can be extensive but need not be. Concise is good. Bullet points are good\n\nGive a project description, brief\nOutline the folder structure\nGive software requirements: programs and versions used or required. There are packages that give session information in R Wickham et al. (2021) and Python Ostblom, Joel (2019)\n\nR:\n```\n#| eval: false\nsessioninfo::session_info()\n```\nPython:\n```\n#| eval: false\nimport session_info\nsession_info.show()\n\n```\n\nInstructions run the code, build reports, and reproduce the figures etc\nWhere to find the data, outputs\nAny other information that needed to understand and recreate the work\n\n-- liver_transcriptome/\n |__data\n |__raw/\n |__2022-03-21_donor_1.csv\n |__2022-03-21_donor_2.csv\n |__2022-03-21_donor_3.csv\n |__2022-05-14_donor_1.csv\n |__2022-05-14_donor_2.csv\n |__2022-05-14_donor_3.csv\n |__processed/\n |__images/\n |__code/\n |__functions/\n |__summarise.R\n |__normalise.R\n |__theme_volcano.R\n |__01_data_processing.py\n |__02_exploratory.R\n |__03_modelling.R\n |__04_figures.R\n |__README.md\n |__reports/\n |__01_report.qmd\n |__02_supplementary.qmd\n |__figures/\n |__01_volcano_donor_1_vs_donor_2.eps\n |__02_volcano_donor_1_vs_donor_3.eps" + }, + { + "objectID": "core/week-1/workshop.html#code-comments", + "href": "core/week-1/workshop.html#code-comments", + "title": "Workshop", + "section": "Code comments", + "text": "Code comments\n\nComments are notes in the code which are not executed. They are ignored by the computer but are read by humans. They are used to explain what the code is doing and why. They are also used to temporarily remove code from execution." + }, + { + "objectID": "core/week-2/study_after_workshop.html", + "href": "core/week-2/study_after_workshop.html", + "title": "Independent Study to consolidate this week", + "section": "", + "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\n💻 xx." + }, + { + "objectID": "core/week-2/workshop.html", + "href": "core/week-2/workshop.html", + "title": "Workshop", + "section": "", + "text": "In this workshop you will\n\nData files. - Sequences data - Image data - Structure data\nSimilarities and differences\n🎬\nYou’re finished!" + }, + { + "objectID": "core/week-2/workshop.html#session-overview", + "href": "core/week-2/workshop.html#session-overview", + "title": "Workshop", + "section": "", + "text": "In this workshop you will" + }, + { + "objectID": "core/week-2/workshop.html#file-formats", + "href": "core/week-2/workshop.html#file-formats", + "title": "Workshop", + "section": "", + "text": "Data files. - Sequences data - Image data - Structure data\nSimilarities and differences\n🎬\nYou’re finished!" + }, + { + "objectID": "core/week-6/study_after_workshop.html", + "href": "core/week-6/study_after_workshop.html", + "title": "Independent Study to consolidate this week", + "section": "", + "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\n💻 xx." + }, + { + "objectID": "core/week-6/workshop.html", + "href": "core/week-6/workshop.html", + "title": "Workshop", + "section": "", + "text": "In this workshop you will\n\n\n\n\n\n\nKey" + }, + { + "objectID": "core/week-6/workshop.html#session-overview", + "href": "core/week-6/workshop.html#session-overview", + "title": "Workshop", + "section": "", + "text": "In this workshop you will\n\n\n\n\n\n\nKey" + }, + { + "objectID": "core/core.html", + "href": "core/core.html", + "title": "Core Data Analysis", + "section": "", + "text": "There are three workshops taken by everyone on BIO00088H and BIO00070M. These are in weeks 1, 2 and 6. The first two cover some useful workflow tips and how to organise your analyses effectively so they are reproducible but you will also have the chance to revise material from stage 1 and 2.\nGood organisation is important because you will want to be able to set work aside for holidays and assessment periods and then restart easily. You will also be assessed on the organisation, reproducibility and transparency of your work.\n\n\nThis week you will revise some essential concepts for scientific computing: file system organisation, file types, working directories and paths. The workshop will cover a rationale for working reproducibly, project oriented workflow, naming things and documenting your work. We will also examine some file types and the concept of tidy data." + }, + { + "objectID": "core/core.html#week-1-core-1-organising-reproducible-data-analyses", + "href": "core/core.html#week-1-core-1-organising-reproducible-data-analyses", + "title": "Core Data Analysis", + "section": "", + "text": "This week you will revise some essential concepts for scientific computing: file system organisation, file types, working directories and paths. The workshop will cover a rationale for working reproducibly, project oriented workflow, naming things and documenting your work. We will also examine some file types and the concept of tidy data." + }, + { + "objectID": "omics/week-3/study_after_workshop.html", + "href": "omics/week-3/study_after_workshop.html", + "title": "Independent Study to consolidate this week", + "section": "", + "text": "You need only do the section for your own project data\n🐸 Frogs\n🎬 Open your frogs-88H Project. Make a new script and, using cont-fgf-s30.R as a template, repeat the analysis on one of the other comparisons.\n🐭 Mice\n🎬 Open your mice-88H Project. Open your hspc-prog.R script and, using you code working with the hspc cells as a template, repeat the analysis on the prog cells.\n🍂 xxxx\n🎬 Open your xxxx-88H Project. Make a new script and, using xxxxxxx.R as a template, repeat the analysis on xxxxxxxx" + }, + { + "objectID": "omics/week-3/workshop.html", + "href": "omics/week-3/workshop.html", + "title": "Workshop", + "section": "", + "text": "In this workshop you will learn what steps to take to get a good understanding of your ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.\nYou should examine all three data sets because the comparisons will give you a stronger understanding of your own project data." + }, + { + "objectID": "omics/week-3/workshop.html#session-overview", + "href": "omics/week-3/workshop.html#session-overview", + "title": "Workshop", + "section": "", + "text": "In this workshop you will learn what steps to take to get a good understanding of your ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.\nYou should examine all three data sets because the comparisons will give you a stronger understanding of your own project data." + }, + { + "objectID": "omics/week-3/workshop.html#set-up-a-project", + "href": "omics/week-3/workshop.html#set-up-a-project", + "title": "Workshop", + "section": "Set up a Project", + "text": "Set up a Project\n🎬 Start RStudio from the Start menu\n🎬 Make an RStudio project. Be deliberate about where you create it so that it is a good place for you\n🎬 Use the Files pane to make new folders for the data. I suggest data-raw and data-processed\n🎬 Make a new script called workshop-1.R to carry out the rest of the work.\n🎬 Record what you do and what you find out. All of it!\n🎬 Load tidyverse (Wickham et al. 2019) for importing, summarising, plotting and filtering.\n\nlibrary(tidyverse)" + }, + { + "objectID": "omics/week-3/workshop.html#examine-the-data-in-a-spreadsheet", + "href": "omics/week-3/workshop.html#examine-the-data-in-a-spreadsheet", + "title": "Workshop", + "section": "Examine the data in a spreadsheet", + "text": "Examine the data in a spreadsheet\nThese are the three datasets. Each set compromises several files.\n🐸 Frog development data:\n\nxlaevis_counts_S14.csv\nxlaevis_counts_S20.csv\nxlaevis_counts_S30.csv\nmeta_data.txt\n\n🐭 Stem cell data:\n\nsurfaceome_hspc.csv\nsurfaceome_prog.csv\nsurfaceome_lthsc.csv\n\n🍂 xxxx data:\n\nxxx\nxxx\n\n🎬 Save the files to data-raw and open them in Excel\n🎬 Answer the following questions:\n\nDescribe how the sets of data are similar and how they are different.\nWhat is in the rows and columns of each file?\nHow many rows and columns are there in each file? Are these the same? In all cases or some cases? Why?\nGoogle an id. Where does your search take you? How much information is available?\n\n🎬 Did you record all that??" + }, + { + "objectID": "omics/week-3/workshop.html#import", + "href": "omics/week-3/workshop.html#import", + "title": "Workshop", + "section": "Import", + "text": "Import\nNow let’s get the data into R and visualise it.\n🎬 Import xlaevis_counts_S30.csv, surfaceome_hspc.csv and xxxxxxxx\n\n# 🐸 import the s30 data\ns30 <- read_csv(\"data-raw/xlaevis_counts_S30.csv\")\n\n\n# 🐭 import the hspc data\nhspc <- read_csv(\"data-raw/surfaceome_hspc.csv\")\n\n\n# 🍂 xxxx import the xxxx data\n# prog <- read_csv(\"\")\n\n🎬 Check these have the number of rows and column you were expecting and that column types and names are as expected." + }, + { + "objectID": "omics/week-3/workshop.html#explore", + "href": "omics/week-3/workshop.html#explore", + "title": "Workshop", + "section": "Explore", + "text": "Explore\nThe first task is to get an overview. We want to know\n\nare there any missing values? If so, how many and how are they distributed?\nhow may zeros are there and how are they distributed\ndoes it look as tough all the samples/cells were equally “successful”? Can we spot any problematic anomalies?\nwhat is the distribution of values?\n\nIf 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. That is replicates should be similar. We would also expect to see that the average expression of genes varies. We might have genes which are zero in every cell/sample. We will want to to filter those out.\nWe get this overview by looking at:\n\nThe distribution of values across the whole dataset\nThe distribution of values across the sample/cells (i.e., averaged across genes). This allows us to see variation between samples/cells:\nThe distribution of values across the genes (i.e., averaged across samples/cells). This allows us to see variation between genes.\n\nDistribution of values across the whole dataset\nIn all data sets, the values are spread over multiple columns so in order to plot the distribution as a whole, we will need to first use pivot_longer() to put the data in ‘tidy’ format (Wickham 2014) by stacking the columns. We could save a copy of the stacked data and then plot it, but here, I have just piped the stacked data straight into ggplot().\n🐸 Frogs\n🎬 Pivot the counts (stack the columns) so all the counts are in a single column (count) and pipe into ggplot() to create a histogram:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n ggplot(aes(x = count)) +\n geom_histogram()\n\n\n\n\nThis data is very skewed - there are so many low values that we can’t see the the tiny bars for the higher values. Logging the counts is a way to make the distribution more visible.\n🎬 Repeat the plot on log of the counts.\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n ggplot(aes(x = log10(count))) +\n geom_histogram()\n\n\n\n\nI’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 peak at zero suggests quite a few counts of 1. We would expect we would expect the distribution of counts to be roughly log normal because this is expression of all the genes in the genome1. That small peak near the low end suggests that these lower counts might be anomalies.\nThe 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 samples and genes.\n🐭 Mice\n🎬 Pivot the expression values (stack the columns) so all the counts are in a single column (expr) and pipe into ggplot() to create a histogram:\n\nhspc |>\n pivot_longer(cols = -ensembl_gene_id,\n names_to = \"cell\",\n values_to = \"expr\") |> \n ggplot(aes(x = expr)) +\n geom_histogram()\n\n\n\n\nThis is a very striking distribution. Is it what we are expecting? Again,the excess number of low values is almost certainly anomalous. They will be inaccurate measure and we will want to exclude expression values below (about) 1. We will revisit this after we have considered the distribution of expression across cells and genes.\nWhat about the bimodal appearance of the the ‘real’ values? If we had the whole genome we would not expect to see such a pattern - we’d expect to see a roughly normal distribution2. However, this is a subset of the genome and the nature of the subsetting has had an influence here. These are a subset of cell surface proteins that show a significant difference between at least two of twelve cell subtypes. That is, all of these genes are either high or low.\nDistribution of values across the sample/cells\n🐸 Frog samples\nSummary 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.\n🎬 Get a quick overview of the columns:\n\n# examine all the columns quickly\n# works well with smaller numbers of column\nsummary(s30)\n\n xenbase_gene_id S30_C_5 S30_C_6 S30_C_A \n Length:11893 Min. : 0.0 Min. : 0.0 Min. : 0.0 \n Class :character 1st Qu.: 14.0 1st Qu.: 14.0 1st Qu.: 23.0 \n Mode :character Median : 70.0 Median : 75.0 Median : 107.0 \n Mean : 317.1 Mean : 335.8 Mean : 426.3 \n 3rd Qu.: 205.0 3rd Qu.: 220.0 3rd Qu.: 301.0 \n Max. :101746.0 Max. :118708.0 Max. :117945.0 \n S30_F_5 S30_F_6 S30_F_A \n Min. : 0.0 Min. : 0.0 Min. : 0.0 \n 1st Qu.: 19.0 1st Qu.: 17.0 1st Qu.: 16.0 \n Median : 88.0 Median : 84.0 Median : 69.0 \n Mean : 376.2 Mean : 376.5 Mean : 260.4 \n 3rd Qu.: 251.0 3rd Qu.: 246.0 3rd Qu.: 187.0 \n Max. :117573.0 Max. :130672.0 Max. :61531.0 \n\n\nNotice 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.\nTo 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 0 in the S30_C_5 column\n🎬 Find the number of zeros in all six columns:\n\ns30 |>\n summarise(sum(S30_C_5 == 0),\n sum(S30_C_6 == 0),\n sum(S30_C_A == 0),\n sum(S30_F_5 == 0),\n sum(S30_F_6 == 0),\n sum(S30_F_A == 0))\n\n# A tibble: 1 × 6\n `sum(S30_C_5 == 0)` `sum(S30_C_6 == 0)` `sum(S30_C_A == 0)`\n <int> <int> <int>\n1 1340 1361 998\n# ℹ 3 more variables: `sum(S30_F_5 == 0)` <int>, `sum(S30_F_6 == 0)` <int>,\n# `sum(S30_F_A == 0)` <int>\n\n\nThere 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.\n🎬 Find the number of zeros in all columns:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n group_by(sample) |>\n summarise(n_zero = sum(count == 0))\n\n# A tibble: 6 × 2\n sample n_zero\n <chr> <int>\n1 S30_C_5 1340\n2 S30_C_6 1361\n3 S30_C_A 998\n4 S30_F_5 1210\n5 S30_F_6 1199\n6 S30_F_A 963\n\n\nYou could expand to get all the summary information\n🎬 Summarise all the samples:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n group_by(sample) |>\n summarise(min = min(count),\n lowerq = quantile(count, 0.25),\n mean = mean(count),\n median = median(count),\n upperq = quantile(count, 0.75),\n max = max(count),\n n_zero = sum(count == 0))\n\n# A tibble: 6 × 8\n sample min lowerq mean median upperq max n_zero\n <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>\n1 S30_C_5 0 14 317. 70 205 101746 1340\n2 S30_C_6 0 14 336. 75 220 118708 1361\n3 S30_C_A 0 23 426. 107 301 117945 998\n4 S30_F_5 0 19 376. 88 251 117573 1210\n5 S30_F_6 0 17 376. 84 246 130672 1199\n6 S30_F_A 0 16 260. 69 187 61531 963\n\n\nThe mean count ranges from 260 to 426.\nOne advantage this has over using summary() is that the output is a dataframe. For results, this is useful, and makes it easier to:\n\nwrite to file\nuse in ggplot()\n\nformat in a Quarto report\n\n🎬 Save the summary as a dataframe, s30_summary_samp.\nWe can write to file using write_csv()\n🎬 Write s30_summary_samp_by to a file called “s30_summary_samp_by.csv”:\n\nwrite_csv(s30_summary_samp_by, file = \"data-processed/s30_summary_samp_by.csv\")\n\nError in eval(expr, envir, enclos): object 's30_summary_samp_by' not found\n\n\nPlotting 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()\n🎬 Pivot the data and pipe into ggplot:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n ggplot(aes(count)) +\n geom_density() +\n facet_wrap(. ~ sample, nrow = 3)\n\n\n\n\nWe have many values (11893) so we are not limited to using geom_histogram(). geom_density() gives us a smooth distribution.\nWe 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.\n🎬 Repeat the graph but taking the base 10 log of the counts:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n ggplot(aes(log10(count))) +\n geom_density() +\n facet_wrap(. ~ sample, nrow = 3)\n\n\n\n\nThe key information to take from these plots is:\n\nthe distributions are roughly similar in width, height, location and overall shape so it doesn’t look as though we have any suspect samples\nthe peak at zero suggests quite a few counts of 1.\nsince we would expect the distribution of counts in each sample to be roughly log normal so that the small rise near the low end, even before the peak at zero, suggests that these lower counts might be anomalies.\n\nThe 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).\n🐭 Mouse cells\nWe used the summary() function to get an overview of the columns in the frog data. Let’s try that here.\n🎬 Get a quick overview of the columns:\n\nsummary(hspc)\n\n ensembl_gene_id HSPC_001 HSPC_002 HSPC_003 \n Length:280 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Mode :character Median : 0.000 Median : 0.000 Median : 0.9929 \n Mean : 2.143 Mean : 1.673 Mean : 2.5964 \n 3rd Qu.: 2.120 3rd Qu.: 2.239 3rd Qu.: 6.1559 \n Max. :12.567 Max. :11.976 Max. :11.1138 \n HSPC_004 HSPC_006 HSPC_008 HSPC_009 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 \n Median : 0.000 Median : 1.276 Median : 0.000 Median :0.000 \n Mean : 1.851 Mean : 2.338 Mean : 2.375 Mean :2.220 \n 3rd Qu.: 2.466 3rd Qu.: 3.536 3rd Qu.: 3.851 3rd Qu.:3.594 \n Max. :11.133 Max. :10.014 Max. :11.574 Max. :9.997 \n HSPC_011 HSPC_012 HSPC_014 HSPC_015 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.750 Median : 0.000 Median : 0.000 \n Mean : 2.285 Mean : 2.431 Mean : 2.295 Mean : 2.515 \n 3rd Qu.: 3.193 3rd Qu.: 3.741 3rd Qu.: 3.150 3rd Qu.: 3.789 \n Max. :11.260 Max. :10.905 Max. :11.051 Max. :10.751 \n HSPC_016 HSPC_017 HSPC_018 HSPC_020 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.9488 Median : 0.000 Median : 1.248 Median : 0.000 \n Mean : 2.6115 Mean : 2.146 Mean : 2.710 Mean : 2.509 \n 3rd Qu.: 5.9412 3rd Qu.: 2.357 3rd Qu.: 6.006 3rd Qu.: 4.470 \n Max. :11.3082 Max. :12.058 Max. :11.894 Max. :11.281 \n HSPC_021 HSPC_022 HSPC_023 HSPC_024 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.170 Mean : 2.287 Mean : 2.314 Mean : 2.195 \n 3rd Qu.: 2.996 3rd Qu.: 3.351 3rd Qu.: 2.749 3rd Qu.: 2.944 \n Max. :10.709 Max. :11.814 Max. :12.113 Max. :11.279 \n HSPC_025 HSPC_026 HSPC_027 HSPC_028 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.572 Median : 1.385 Median : 0.000 Median : 0.000 \n Mean : 2.710 Mean : 2.721 Mean : 2.458 Mean : 1.906 \n 3rd Qu.: 5.735 3rd Qu.: 6.392 3rd Qu.: 5.496 3rd Qu.: 2.037 \n Max. :11.309 Max. :10.865 Max. :11.266 Max. :10.777 \n HSPC_030 HSPC_031 HSPC_033 HSPC_034 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.119 Median : 0.9026 Median : 0.000 Median : 0.7984 \n Mean : 2.338 Mean : 2.3049 Mean : 1.938 Mean : 2.3220 \n 3rd Qu.: 3.005 3rd Qu.: 2.9919 3rd Qu.: 2.434 3rd Qu.: 4.8324 \n Max. :11.391 Max. :11.1748 Max. :10.808 Max. :10.6707 \n HSPC_035 HSPC_036 HSPC_037 HSPC_038 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8879 Median : 1.517 Median : 0.000 \n Mean : 1.810 Mean : 2.6918 Mean : 2.327 Mean : 2.212 \n 3rd Qu.: 2.175 3rd Qu.: 5.9822 3rd Qu.: 3.079 3rd Qu.: 2.867 \n Max. :11.221 Max. :11.3018 Max. :11.399 Max. :12.275 \n HSPC_040 HSPC_041 HSPC_042 HSPC_043 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.8673 Median : 1.342 \n Mean : 2.509 Mean : 2.492 Mean : 2.3673 Mean : 2.420 \n 3rd Qu.: 3.995 3rd Qu.: 3.943 3rd Qu.: 3.8371 3rd Qu.: 3.731 \n Max. :11.863 Max. :11.016 Max. :11.4852 Max. :11.123 \n HSPC_044 HSPC_045 HSPC_046 HSPC_047 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.8452 Median : 2.195 \n Mean : 2.382 Mean : 2.277 Mean : 1.9707 Mean : 2.498 \n 3rd Qu.: 3.998 3rd Qu.: 2.843 3rd Qu.: 2.0656 3rd Qu.: 3.937 \n Max. :10.782 Max. :10.629 Max. :11.0311 Max. :10.180 \n HSPC_048 HSPC_049 HSPC_050 HSPC_051 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.108 Median : 1.275 Median : 0.000 Median : 0.9757 \n Mean : 2.289 Mean : 2.453 Mean : 2.673 Mean : 2.2693 \n 3rd Qu.: 2.988 3rd Qu.: 3.819 3rd Qu.: 5.772 3rd Qu.: 3.1644 \n Max. :10.335 Max. :11.844 Max. :11.301 Max. :10.8692 \n HSPC_052 HSPC_053 HSPC_054 HSPC_055 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.509 Median : 0.818 Median : 0.000 Median : 0.000 \n Mean : 2.561 Mean : 2.684 Mean : 2.107 Mean : 1.959 \n 3rd Qu.: 4.644 3rd Qu.: 5.937 3rd Qu.: 2.568 3rd Qu.: 2.573 \n Max. :11.674 Max. :11.624 Max. :10.770 Max. :11.105 \n HSPC_056 HSPC_057 HSPC_058 HSPC_060 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.399 Median : 0.000 \n Mean : 2.295 Mean : 2.430 Mean : 2.296 Mean : 2.112 \n 3rd Qu.: 3.721 3rd Qu.: 3.806 3rd Qu.: 3.199 3rd Qu.: 2.201 \n Max. :11.627 Max. :10.575 Max. :11.134 Max. :10.631 \n HSPC_061 HSPC_062 HSPC_063 HSPC_064 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.515 Median : 1.101 \n Mean : 1.934 Mean : 2.129 Mean : 2.508 Mean : 2.696 \n 3rd Qu.: 2.489 3rd Qu.: 2.875 3rd Qu.: 4.895 3rd Qu.: 6.412 \n Max. :11.190 Max. :10.433 Max. :10.994 Max. :10.873 \n HSPC_065 HSPC_066 HSPC_067 HSPC_068 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.4852 Median : 0.000 Median : 1.441 Median : 0.000 \n Mean : 2.2676 Mean : 2.136 Mean : 2.480 Mean : 2.449 \n 3rd Qu.: 3.8217 3rd Qu.: 2.632 3rd Qu.: 3.548 3rd Qu.: 4.517 \n Max. :10.9023 Max. :11.608 Max. :11.147 Max. :10.901 \n HSPC_069 HSPC_070 HSPC_071 HSPC_072 \n Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8949 Median : 0.9272 Median : 1.121 \n Mean : 2.406 Mean : 2.5826 Mean : 2.2844 Mean : 2.545 \n 3rd Qu.: 4.705 3rd Qu.: 5.4749 3rd Qu.: 3.2531 3rd Qu.: 4.939 \n Max. :11.258 Max. :11.6715 Max. :10.7886 Max. :11.397 \n HSPC_073 HSPC_074 HSPC_075 HSPC_076 \n Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.00 Median : 1.674 Median : 0.000 \n Mean : 2.491 Mean : 2.46 Mean : 2.413 Mean : 2.289 \n 3rd Qu.: 4.134 3rd Qu.: 3.40 3rd Qu.: 3.013 3rd Qu.: 2.550 \n Max. :11.844 Max. :11.66 Max. :11.976 Max. :12.136 \n HSPC_077 HSPC_078 HSPC_079 HSPC_080 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.6624 Median : 1.492 Median : 1.384 Median : 1.013 \n Mean : 2.4336 Mean : 2.637 Mean : 2.432 Mean : 2.881 \n 3rd Qu.: 5.4937 3rd Qu.: 5.472 3rd Qu.: 3.617 3rd Qu.: 7.220 \n Max. :11.6020 Max. :10.673 Max. :11.199 Max. :11.836 \n HSPC_081 HSPC_082 HSPC_083 HSPC_084 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.7671 Median : 0.000 Median : 1.896 Median : 1.128 \n Mean : 1.9227 Mean : 2.474 Mean : 2.864 Mean : 2.289 \n 3rd Qu.: 1.6349 3rd Qu.: 3.488 3rd Qu.: 5.101 3rd Qu.: 2.792 \n Max. :11.4681 Max. :11.962 Max. :10.865 Max. :11.834 \n HSPC_085 HSPC_087 HSPC_088 HSPC_089 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.157 Mean : 2.314 Mean : 2.202 Mean : 2.329 \n 3rd Qu.: 3.010 3rd Qu.: 3.245 3rd Qu.: 2.092 3rd Qu.: 3.246 \n Max. :10.809 Max. :10.976 Max. :11.362 Max. :11.301 \n HSPC_090 HSPC_094 HSPC_095 HSPC_096 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 \n Median : 0.000 Median : 0.000 Median : 2.055 Median :0.000 \n Mean : 2.286 Mean : 2.186 Mean : 2.756 Mean :2.348 \n 3rd Qu.: 4.174 3rd Qu.: 2.002 3rd Qu.: 4.370 3rd Qu.:4.482 \n Max. :11.124 Max. :11.694 Max. :11.385 Max. :9.601 \n HSPC_098 HSPC_099 HSPC_100 HSPC_101 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.007 \n Mean : 2.209 Mean : 2.082 Mean : 2.313 Mean : 2.587 \n 3rd Qu.: 3.354 3rd Qu.: 2.505 3rd Qu.: 2.775 3rd Qu.: 5.334 \n Max. :11.070 Max. :10.200 Max. :11.452 Max. :11.456 \n HSPC_102 HSPC_103 HSPC_104 HSPC_105 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.111 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.210 Mean : 2.853 Mean : 2.099 Mean : 1.893 \n 3rd Qu.: 2.993 3rd Qu.: 6.123 3rd Qu.: 2.720 3rd Qu.: 2.129 \n Max. :11.153 Max. :11.328 Max. :10.746 Max. :10.721 \n HSPC_106 HSPC_107 HSPC_108 HSPC_109 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.595 \n Mean : 1.980 Mean : 2.279 Mean : 2.296 Mean : 2.420 \n 3rd Qu.: 2.425 3rd Qu.: 3.396 3rd Qu.: 3.361 3rd Qu.: 4.006 \n Max. :10.919 Max. :10.982 Max. :11.744 Max. :10.463 \n HSPC_110 HSPC_111 HSPC_114 HSPC_115 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.9173 Median : 2.349 \n Mean : 2.159 Mean : 1.800 Mean : 1.8376 Mean : 2.943 \n 3rd Qu.: 2.667 3rd Qu.: 2.214 3rd Qu.: 1.8741 3rd Qu.: 6.223 \n Max. :11.121 Max. :11.109 Max. :10.4645 Max. :11.124 \n HSPC_117 HSPC_118 HSPC_119 HSPC_120 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.187 \n Mean : 1.919 Mean : 1.855 Mean : 2.289 Mean : 2.041 \n 3rd Qu.: 2.306 3rd Qu.: 2.387 3rd Qu.: 3.292 3rd Qu.: 2.610 \n Max. :14.579 Max. :11.119 Max. :12.534 Max. :11.438 \n HSPC_121 HSPC_122 HSPC_123 HSPC_125 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.803 Mean : 2.072 Mean : 2.200 Mean : 2.116 \n 3rd Qu.: 5.798 3rd Qu.: 2.140 3rd Qu.: 3.215 3rd Qu.: 2.409 \n Max. :11.320 Max. :11.013 Max. :11.163 Max. :11.368 \n HSPC_126 HSPC_127 HSPC_130 HSPC_131 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.9381 Median : 1.147 Median : 0.000 Median : 0.000 \n Mean : 2.0014 Mean : 2.287 Mean : 2.551 Mean : 2.240 \n 3rd Qu.: 2.2215 3rd Qu.: 3.051 3rd Qu.: 3.968 3rd Qu.: 3.773 \n Max. :10.9622 Max. :11.028 Max. :10.585 Max. :11.216 \n HSPC_132 HSPC_133 HSPC_134 HSPC_135 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.4438 Median : 2.234 Median : 0.000 Median : 0.000 \n Mean : 2.1659 Mean : 2.582 Mean : 2.335 Mean : 2.402 \n 3rd Qu.: 1.8512 3rd Qu.: 4.591 3rd Qu.: 3.659 3rd Qu.: 4.134 \n Max. :10.6431 Max. :10.730 Max. :11.995 Max. :11.573 \n HSPC_136 HSPC_138 HSPC_139 HSPC_140 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.7062 Median : 2.078 Median : 0.000 \n Mean : 2.546 Mean : 2.1054 Mean : 2.876 Mean : 2.220 \n 3rd Qu.: 5.219 3rd Qu.: 1.8181 3rd Qu.: 4.604 3rd Qu.: 3.716 \n Max. :11.281 Max. :11.1177 Max. :11.013 Max. :10.893 \n HSPC_141 HSPC_142 HSPC_143 HSPC_144 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.075 \n Mean : 2.385 Mean : 2.232 Mean : 2.592 Mean : 2.004 \n 3rd Qu.: 4.149 3rd Qu.: 2.523 3rd Qu.: 4.248 3rd Qu.: 2.441 \n Max. :11.099 Max. :11.902 Max. :12.932 Max. :11.121 \n HSPC_146 HSPC_148 HSPC_149 HSPC_151 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.9711 \n Mean : 2.418 Mean : 2.385 Mean : 2.314 Mean : 2.4375 \n 3rd Qu.: 4.430 3rd Qu.: 3.288 3rd Qu.: 3.139 3rd Qu.: 3.2523 \n Max. :10.385 Max. :12.823 Max. :10.910 Max. :11.7148 \n HSPC_152 HSPC_153 HSPC_154 HSPC_155 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.247 Mean : 2.415 Mean : 2.476 Mean : 2.468 \n 3rd Qu.: 3.293 3rd Qu.: 3.524 3rd Qu.: 4.653 3rd Qu.: 3.621 \n Max. :12.463 Max. :12.205 Max. :11.437 Max. :11.207 \n HSPC_156 HSPC_157 HSPC_158 HSPC_159 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.5545 Median : 1.993 Median : 0.000 Median : 0.000 \n Mean : 2.2297 Mean : 2.493 Mean : 2.119 Mean : 2.461 \n 3rd Qu.: 2.0977 3rd Qu.: 3.692 3rd Qu.: 2.930 3rd Qu.: 3.340 \n Max. :11.2431 Max. :10.539 Max. :11.336 Max. :11.123 \n HSPC_161 HSPC_162 HSPC_164 HSPC_165 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.701 Median : 0.7152 Median : 0.000 Median : 0.000 \n Mean : 2.533 Mean : 2.3473 Mean : 2.161 Mean : 2.084 \n 3rd Qu.: 3.616 3rd Qu.: 2.4973 3rd Qu.: 2.553 3rd Qu.: 3.020 \n Max. :11.429 Max. :11.0065 Max. :11.865 Max. :10.282 \n HSPC_166 HSPC_168 HSPC_169 HSPC_170 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.002 Median : 1.158 Median : 0.000 \n Mean : 2.177 Mean : 2.390 Mean : 2.038 Mean : 2.401 \n 3rd Qu.: 3.296 3rd Qu.: 4.701 3rd Qu.: 2.232 3rd Qu.: 3.703 \n Max. :11.427 Max. :10.393 Max. :10.447 Max. :11.288 \n HSPC_171 HSPC_172 HSPC_173 HSPC_174 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.525 Median : 0.7679 Median : 0.000 Median : 1.257 \n Mean : 2.312 Mean : 2.3115 Mean : 2.288 Mean : 2.444 \n 3rd Qu.: 2.729 3rd Qu.: 3.7889 3rd Qu.: 3.037 3rd Qu.: 4.996 \n Max. :10.468 Max. :11.1442 Max. :11.074 Max. :11.095 \n HSPC_175 HSPC_176 HSPC_177 HSPC_178 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.496 Median : 2.024 Median : 1.971 Median : 1.003 \n Mean : 2.613 Mean : 2.593 Mean : 2.421 Mean : 2.277 \n 3rd Qu.: 4.845 3rd Qu.: 4.092 3rd Qu.: 3.665 3rd Qu.: 2.812 \n Max. :11.235 Max. :10.379 Max. :10.864 Max. :10.979 \n HSPC_179 HSPC_180 HSPC_181 HSPC_182 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.836 Median : 1.544 Median : 2.030 Median : 0.000 \n Mean : 2.205 Mean : 2.556 Mean : 2.890 Mean : 2.363 \n 3rd Qu.: 2.300 3rd Qu.: 4.798 3rd Qu.: 4.846 3rd Qu.: 3.779 \n Max. :11.244 Max. :10.802 Max. :10.945 Max. :10.399 \n HSPC_183 HSPC_185 HSPC_186 HSPC_187 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.020 Median : 0.000 Median : 1.606 Median : 0.000 \n Mean : 2.242 Mean : 2.708 Mean : 2.053 Mean : 2.360 \n 3rd Qu.: 2.842 3rd Qu.: 4.855 3rd Qu.: 2.834 3rd Qu.: 3.541 \n Max. :10.530 Max. :11.079 Max. :11.016 Max. :10.923 \n HSPC_189 HSPC_190 HSPC_191 HSPC_192 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.412 \n Mean : 2.120 Mean : 2.417 Mean : 2.175 Mean : 2.192 \n 3rd Qu.: 2.652 3rd Qu.: 5.226 3rd Qu.: 2.574 3rd Qu.: 2.669 \n Max. :11.300 Max. :11.023 Max. :11.454 Max. :10.225 \n HSPC_193 HSPC_195 HSPC_196 HSPC_198 \n Min. :0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.:0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median :0.9691 Median : 0.9175 Median : 1.379 Median : 1.105 \n Mean :2.5448 Mean : 2.7307 Mean : 2.327 Mean : 2.155 \n 3rd Qu.:5.1191 3rd Qu.: 5.8899 3rd Qu.: 2.625 3rd Qu.: 2.756 \n Max. :9.8728 Max. :10.4757 Max. :11.319 Max. :11.405 \n HSPC_199 HSPC_200 HSPC_202 HSPC_203 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 1.069 Median : 1.572 Median : 0.8045 Median : 1.311 \n Mean : 1.909 Mean : 2.346 Mean : 2.1384 Mean : 2.058 \n 3rd Qu.: 2.431 3rd Qu.: 2.791 3rd Qu.: 2.0569 3rd Qu.: 2.792 \n Max. :11.377 Max. :11.334 Max. :11.0516 Max. :10.852 \n HSPC_204 HSPC_205 HSPC_206 HSPC_207 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.342 Median : 1.997 Median : 1.076 Median : 0.9235 \n Mean : 2.716 Mean : 2.520 Mean : 2.426 Mean : 2.2974 \n 3rd Qu.: 5.611 3rd Qu.: 4.244 3rd Qu.: 4.057 3rd Qu.: 2.6736 \n Max. :10.269 Max. :10.817 Max. :11.866 Max. :11.4287 \n HSPC_208 HSPC_210 HSPC_211 HSPC_212 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 2.263 Median : 1.021 Median : 1.351 Median : 0.000 \n Mean : 2.893 Mean : 2.315 Mean : 2.425 Mean : 2.336 \n 3rd Qu.: 5.014 3rd Qu.: 2.676 3rd Qu.: 3.820 3rd Qu.: 3.443 \n Max. :11.375 Max. :12.208 Max. :11.360 Max. :11.808 \n HSPC_213 HSPC_214 HSPC_215 HSPC_216 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.270 Median : 0.9195 Median : 1.653 Median : 0.8022 \n Mean : 2.483 Mean : 2.1976 Mean : 2.563 Mean : 2.6010 \n 3rd Qu.: 4.903 3rd Qu.: 2.7139 3rd Qu.: 4.344 3rd Qu.: 6.0076 \n Max. :11.548 Max. :10.6947 Max. :10.933 Max. :11.2119 \n HSPC_218 HSPC_219 HSPC_220 HSPC_221 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.027 Median : 0.000 Median : 1.269 \n Mean : 2.467 Mean : 2.291 Mean : 2.449 Mean : 2.641 \n 3rd Qu.: 3.980 3rd Qu.: 2.853 3rd Qu.: 4.486 3rd Qu.: 3.617 \n Max. :11.654 Max. :10.801 Max. :10.410 Max. :11.651 \n HSPC_222 HSPC_223 HSPC_224 HSPC_225 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.449 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.262 Mean : 2.271 Mean : 2.492 Mean : 2.585 \n 3rd Qu.: 3.271 3rd Qu.: 3.727 3rd Qu.: 3.769 3rd Qu.: 5.253 \n Max. :11.133 Max. :12.000 Max. :11.114 Max. :11.671 \n HSPC_227 HSPC_228 HSPC_229 HSPC_230 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 2.484 Median : 0.000 \n Mean : 2.492 Mean : 2.370 Mean : 2.742 Mean : 2.586 \n 3rd Qu.: 3.692 3rd Qu.: 4.488 3rd Qu.: 4.836 3rd Qu.: 5.188 \n Max. :10.815 Max. :10.165 Max. :11.143 Max. :10.734 \n HSPC_231 HSPC_232 HSPC_233 HSPC_235 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.869 Median : 1.254 Median : 0.000 \n Mean : 2.379 Mean : 2.264 Mean : 2.531 Mean : 2.552 \n 3rd Qu.: 4.787 3rd Qu.: 3.163 3rd Qu.: 3.925 3rd Qu.: 4.389 \n Max. :10.790 Max. :12.098 Max. :11.533 Max. :11.765 \n HSPC_236 HSPC_237 HSPC_239 HSPC_240 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 2.207 Median : 0.892 \n Mean : 2.205 Mean : 2.457 Mean : 2.656 Mean : 2.049 \n 3rd Qu.: 3.748 3rd Qu.: 3.488 3rd Qu.: 4.904 3rd Qu.: 2.617 \n Max. :10.234 Max. :10.630 Max. :10.858 Max. :10.528 \n HSPC_243 HSPC_244 HSPC_245 HSPC_246 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.118 Median : 0.7872 Median : 1.459 Median : 1.629 \n Mean : 2.311 Mean : 2.6638 Mean : 2.360 Mean : 2.321 \n 3rd Qu.: 2.574 3rd Qu.: 6.2395 3rd Qu.: 3.000 3rd Qu.: 3.229 \n Max. :11.069 Max. :10.0730 Max. :11.297 Max. :11.237 \n HSPC_247 HSPC_248 HSPC_249 HSPC_250 \n Min. :0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.:0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median :0.000 Median : 0.8453 Median : 0.000 Median : 1.278 \n Mean :2.537 Mean : 2.3719 Mean : 1.803 Mean : 2.751 \n 3rd Qu.:4.687 3rd Qu.: 3.3090 3rd Qu.: 2.335 3rd Qu.: 6.330 \n Max. :9.821 Max. :10.8128 Max. :10.568 Max. :11.256 \n HSPC_251 HSPC_253 HSPC_254 HSPC_255 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.9714 Median : 1.265 Median : 0.000 Median : 0.9098 \n Mean : 2.5626 Mean : 2.492 Mean : 2.177 Mean : 2.1878 \n 3rd Qu.: 4.9167 3rd Qu.: 4.185 3rd Qu.: 3.437 3rd Qu.: 2.4313 \n Max. :11.1252 Max. :10.435 Max. :10.422 Max. :10.7952 \n HSPC_256 HSPC_257 HSPC_258 HSPC_261 \n Min. : 0.0000 Min. : 0.000 Min. :0.0000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 0.0000 \n Median : 0.8248 Median : 1.241 Median :0.8526 Median : 0.5387 \n Mean : 2.1051 Mean : 2.630 Mean :2.0295 Mean : 2.1419 \n 3rd Qu.: 2.3331 3rd Qu.: 5.646 3rd Qu.:3.0784 3rd Qu.: 1.9352 \n Max. :13.0375 Max. :11.499 Max. :9.9116 Max. :11.3247 \n HSPC_263 HSPC_264 HSPC_265 HSPC_266 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.538 Median : 1.426 Median : 1.883 Median : 1.839 \n Mean : 2.613 Mean : 2.374 Mean : 3.177 Mean : 2.833 \n 3rd Qu.: 4.485 3rd Qu.: 3.238 3rd Qu.: 5.702 3rd Qu.: 5.801 \n Max. :10.571 Max. :11.136 Max. :12.436 Max. :10.338 \n HSPC_267 HSPC_268 HSPC_269 HSPC_270 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.9675 Median : 0.7787 Median : 0.8632 Median : 0.9637 \n Mean : 2.4910 Mean : 2.5342 Mean : 2.4029 Mean : 2.6899 \n 3rd Qu.: 3.5345 3rd Qu.: 4.9871 3rd Qu.: 4.3176 3rd Qu.: 5.7266 \n Max. :10.0139 Max. :10.7848 Max. :11.2689 Max. :11.1648 \n HSPC_271 HSPC_274 HSPC_275 HSPC_276 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 1.352 Median : 1.730 Median : 0.5252 Median : 1.156 \n Mean : 2.493 Mean : 2.382 Mean : 2.5375 Mean : 2.485 \n 3rd Qu.: 4.430 3rd Qu.: 3.360 3rd Qu.: 5.7329 3rd Qu.: 4.623 \n Max. :11.636 Max. :11.165 Max. :11.6234 Max. :11.562 \n HSPC_278 HSPC_279 HSPC_280 HSPC_281 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.487 Median : 1.608 Median : 2.611 \n Mean : 2.161 Mean : 2.497 Mean : 2.580 Mean : 2.737 \n 3rd Qu.: 2.270 3rd Qu.: 3.813 3rd Qu.: 3.985 3rd Qu.: 4.731 \n Max. :11.734 Max. :10.900 Max. :11.673 Max. :10.076 \n HSPC_282 HSPC_283 HSPC_285 HSPC_286 \n Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.7021 Median : 1.911 Median : 0.8658 Median : 1.178 \n Mean : 2.4272 Mean : 2.534 Mean : 2.4868 Mean : 2.293 \n 3rd Qu.: 4.1254 3rd Qu.: 3.888 3rd Qu.: 5.3804 3rd Qu.: 2.597 \n Max. :11.1094 Max. :10.258 Max. :10.5533 Max. :11.112 \n HSPC_287 HSPC_288 HSPC_289 HSPC_290 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.049 Median : 0.8548 Median : 1.953 Median : 1.176 \n Mean : 2.775 Mean : 2.6412 Mean : 2.925 Mean : 2.304 \n 3rd Qu.: 5.476 3rd Qu.: 5.4204 3rd Qu.: 5.613 3rd Qu.: 3.445 \n Max. :10.925 Max. :11.0814 Max. :10.199 Max. :11.094 \n HSPC_291 HSPC_292 HSPC_293 HSPC_294 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.176 Median : 1.320 Median : 1.077 Median : 0.9161 \n Mean : 2.662 Mean : 2.534 Mean : 2.538 Mean : 2.4365 \n 3rd Qu.: 5.690 3rd Qu.: 4.297 3rd Qu.: 3.458 3rd Qu.: 4.8204 \n Max. :12.255 Max. :11.090 Max. :10.987 Max. :10.6135 \n HSPC_295 HSPC_296 HSPC_297 HSPC_298 \n Min. :0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median :1.479 Median : 2.157 Median : 2.444 Median : 1.281 \n Mean :2.849 Mean : 2.977 Mean : 3.062 Mean : 2.277 \n 3rd Qu.:5.282 3rd Qu.: 5.006 3rd Qu.: 5.005 3rd Qu.: 2.749 \n Max. :9.986 Max. :10.830 Max. :11.009 Max. :10.636 \n HSPC_299 HSPC_300 HSPC_301 HSPC_302 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.716 Median : 1.163 Median : 2.235 Median : 2.240 \n Mean : 2.597 Mean : 2.346 Mean : 2.739 Mean : 2.890 \n 3rd Qu.: 3.762 3rd Qu.: 2.876 3rd Qu.: 4.593 3rd Qu.: 4.945 \n Max. :11.663 Max. :11.690 Max. :10.364 Max. :10.498 \n HSPC_303 HSPC_304 HSPC_305 HSPC_306 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.8348 Median : 0.9727 Median : 1.152 Median : 1.303 \n Mean : 2.3400 Mean : 2.3710 Mean : 2.469 Mean : 2.496 \n 3rd Qu.: 3.2942 3rd Qu.: 2.9942 3rd Qu.: 3.300 3rd Qu.: 3.015 \n Max. :10.3022 Max. :11.7185 Max. :11.051 Max. :11.211 \n HSPC_307 HSPC_308 HSPC_309 HSPC_310 \n Min. :0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median :1.976 Median : 1.634 Median : 1.804 Median : 1.743 \n Mean :2.873 Mean : 2.812 Mean : 2.892 Mean : 2.874 \n 3rd Qu.:5.396 3rd Qu.: 5.089 3rd Qu.: 5.165 3rd Qu.: 5.004 \n Max. :9.921 Max. :10.527 Max. :10.476 Max. :11.107 \n HSPC_312 HSPC_313 HSPC_314 HSPC_315 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.420 Median : 1.592 Median : 1.635 Median : 2.262 \n Mean : 2.645 Mean : 2.637 Mean : 2.564 Mean : 2.628 \n 3rd Qu.: 4.925 3rd Qu.: 4.257 3rd Qu.: 4.297 3rd Qu.: 4.092 \n Max. :11.367 Max. :10.644 Max. :10.882 Max. :12.140 \n HSPC_317 HSPC_318 HSPC_320 HSPC_321 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 2.335 Median : 1.728 Median : 2.340 Median : 1.835 \n Mean : 2.648 Mean : 2.637 Mean : 3.064 Mean : 2.742 \n 3rd Qu.: 4.103 3rd Qu.: 4.483 3rd Qu.: 5.325 3rd Qu.: 4.340 \n Max. :10.933 Max. :11.712 Max. :11.589 Max. :11.695 \n HSPC_322 HSPC_323 HSPC_324 HSPC_325 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.9842 Median : 0.989 Median : 1.088 Median : 2.132 \n Mean : 2.5948 Mean : 2.905 Mean : 2.655 Mean : 3.091 \n 3rd Qu.: 3.4619 3rd Qu.: 5.629 3rd Qu.: 3.772 3rd Qu.: 5.191 \n Max. :11.9594 Max. :12.267 Max. :11.310 Max. :11.134 \n HSPC_326 HSPC_327 HSPC_328 HSPC_329 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.781 Median : 1.085 Median : 1.936 Median : 1.954 \n Mean : 3.021 Mean : 2.838 Mean : 2.582 Mean : 3.034 \n 3rd Qu.: 5.582 3rd Qu.: 6.388 3rd Qu.: 4.048 3rd Qu.: 5.497 \n Max. :11.268 Max. :11.433 Max. :11.908 Max. :10.927 \n HSPC_330 HSPC_331 HSPC_332 HSPC_333 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.870 Median : 2.953 Median : 1.644 Median : 1.320 \n Mean : 2.791 Mean : 3.058 Mean : 2.768 Mean : 2.428 \n 3rd Qu.: 4.409 3rd Qu.: 5.118 3rd Qu.: 5.141 3rd Qu.: 2.985 \n Max. :11.561 Max. :10.855 Max. :10.420 Max. :11.946 \n HSPC_334 HSPC_335 HSPC_336 HSPC_337 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.931 Median : 1.541 Median : 2.761 Median : 0.000 \n Mean : 2.894 Mean : 2.746 Mean : 3.051 Mean : 2.415 \n 3rd Qu.: 4.160 3rd Qu.: 4.461 3rd Qu.: 4.408 3rd Qu.: 4.188 \n Max. :11.592 Max. :11.076 Max. :11.246 Max. :10.205 \n HSPC_338 HSPC_339 HSPC_341 HSPC_342 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.9553 Median : 0.4452 \n Mean : 2.205 Mean : 2.325 Mean : 2.0823 Mean : 2.4572 \n 3rd Qu.: 2.449 3rd Qu.: 3.136 3rd Qu.: 2.0118 3rd Qu.: 4.9582 \n Max. :12.052 Max. :11.858 Max. :11.3855 Max. :11.8066 \n HSPC_343 HSPC_344 HSPC_345 HSPC_346 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.5197 \n Mean : 2.363 Mean : 2.290 Mean : 1.984 Mean : 2.5126 \n 3rd Qu.: 4.285 3rd Qu.: 3.238 3rd Qu.: 2.561 3rd Qu.: 5.2033 \n Max. :11.422 Max. :11.877 Max. :10.939 Max. :11.1527 \n HSPC_348 HSPC_349 HSPC_350 HSPC_351 \n Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 \n Median : 1.113 Median : 0.000 Median : 0.00 Median : 0.000 \n Mean : 2.232 Mean : 1.949 Mean : 2.11 Mean : 2.259 \n 3rd Qu.: 2.875 3rd Qu.: 2.784 3rd Qu.: 3.07 3rd Qu.: 3.214 \n Max. :11.161 Max. :10.720 Max. :11.15 Max. :10.912 \n HSPC_352 HSPC_353 HSPC_354 HSPC_356 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.333 Mean : 2.162 Mean : 2.427 Mean : 2.135 \n 3rd Qu.: 3.197 3rd Qu.: 2.819 3rd Qu.: 3.808 3rd Qu.: 2.709 \n Max. :12.275 Max. :11.351 Max. :11.190 Max. :10.662 \n HSPC_358 HSPC_359 HSPC_360 HSPC_361 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.278 Mean : 2.012 Mean : 2.381 Mean : 2.137 \n 3rd Qu.: 3.608 3rd Qu.: 1.460 3rd Qu.: 3.044 3rd Qu.: 2.875 \n Max. :10.924 Max. :11.678 Max. :11.203 Max. :10.847 \n HSPC_362 HSPC_363 HSPC_365 HSPC_367 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 1.783 Mean : 1.987 Mean : 2.937 Mean : 2.449 \n 3rd Qu.: 1.594 3rd Qu.: 2.750 3rd Qu.: 5.572 3rd Qu.: 3.936 \n Max. :11.889 Max. :10.389 Max. :12.427 Max. :11.081 \n HSPC_368 HSPC_370 HSPC_371 HSPC_372 \n Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.7971 Median : 0.7613 Median : 0.000 \n Mean : 1.877 Mean : 2.7681 Mean : 2.4278 Mean : 2.487 \n 3rd Qu.: 2.018 3rd Qu.: 6.5358 3rd Qu.: 4.9578 3rd Qu.: 4.226 \n Max. :11.523 Max. :11.9636 Max. :11.4223 Max. :11.700 \n HSPC_373 HSPC_374 HSPC_376 HSPC_377 \n Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.00 Median : 0.000 Median : 0.000 \n Mean : 2.330 Mean : 2.21 Mean : 2.625 Mean : 2.456 \n 3rd Qu.: 3.784 3rd Qu.: 2.44 3rd Qu.: 4.365 3rd Qu.: 4.875 \n Max. :11.672 Max. :12.04 Max. :12.011 Max. :11.282 \n HSPC_380 HSPC_382 HSPC_383 HSPC_386 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.9728 Median : 1.753 Median : 0.000 \n Mean : 2.291 Mean : 2.3318 Mean : 2.307 Mean : 2.351 \n 3rd Qu.: 2.403 3rd Qu.: 2.7605 3rd Qu.: 3.113 3rd Qu.: 3.704 \n Max. :11.415 Max. :11.3370 Max. :11.592 Max. :11.079 \n HSPC_387 HSPC_388 HSPC_389 HSPC_390 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.9037 Median : 0.000 Median : 0.000 \n Mean : 2.255 Mean : 2.4969 Mean : 2.081 Mean : 2.131 \n 3rd Qu.: 3.151 3rd Qu.: 5.3587 3rd Qu.: 2.723 3rd Qu.: 2.738 \n Max. :11.700 Max. :10.9923 Max. :11.868 Max. :10.913 \n HSPC_391 HSPC_392 HSPC_393 HSPC_395 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.026 Mean : 2.356 Mean : 2.063 Mean : 1.779 \n 3rd Qu.: 2.126 3rd Qu.: 3.781 3rd Qu.: 2.163 3rd Qu.: 1.924 \n Max. :12.021 Max. :11.370 Max. :10.530 Max. :12.219 \n HSPC_396 HSPC_398 HSPC_399 HSPC_400 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.164 Mean : 2.309 Mean : 1.831 Mean : 2.091 \n 3rd Qu.: 2.681 3rd Qu.: 3.994 3rd Qu.: 1.844 3rd Qu.: 2.781 \n Max. :11.292 Max. :11.431 Max. :11.343 Max. :10.863 \n HSPC_402 HSPC_403 HSPC_404 HSPC_405 \n Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.00 Median : 0.000 Median : 0.5496 \n Mean : 2.343 Mean : 2.06 Mean : 1.878 Mean : 2.3660 \n 3rd Qu.: 4.552 3rd Qu.: 2.45 3rd Qu.: 1.644 3rd Qu.: 2.5449 \n Max. :11.444 Max. :12.00 Max. :11.188 Max. :12.2605 \n HSPC_406 HSPC_407 HSPC_408 HSPC_409 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.565 Median : 0.5775 Median : 0.000 \n Mean : 2.169 Mean : 2.611 Mean : 1.9174 Mean : 2.234 \n 3rd Qu.: 2.606 3rd Qu.: 6.000 3rd Qu.: 1.3086 3rd Qu.: 3.044 \n Max. :10.866 Max. :11.296 Max. :12.8185 Max. :11.595 \n HSPC_410 HSPC_411 HSPC_412 HSPC_413 \n Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.9059 Median : 0.6614 Median : 0.000 \n Mean : 2.308 Mean : 3.1194 Mean : 3.0437 Mean : 2.433 \n 3rd Qu.: 4.022 3rd Qu.: 7.7574 3rd Qu.: 7.4695 3rd Qu.: 3.329 \n Max. :11.620 Max. :12.0858 Max. :11.5582 Max. :12.549 \n HSPC_415 HSPC_416 HSPC_417 HSPC_418 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.7222 Median : 0.000 \n Mean : 2.904 Mean : 2.228 Mean : 2.4242 Mean : 2.508 \n 3rd Qu.: 5.531 3rd Qu.: 3.111 3rd Qu.: 3.0795 3rd Qu.: 3.249 \n Max. :12.359 Max. :11.338 Max. :12.0314 Max. :11.857 \n HSPC_419 HSPC_420 HSPC_421 HSPC_422 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.6924 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.6246 Mean : 2.514 Mean : 2.075 Mean : 2.552 \n 3rd Qu.: 4.8156 3rd Qu.: 5.709 3rd Qu.: 3.682 3rd Qu.: 5.382 \n Max. :12.0526 Max. :11.270 Max. :10.250 Max. :11.691 \n HSPC_423 HSPC_424 HSPC_425 HSPC_426 \n Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.00 Median : 0.000 Median : 1.016 Median : 0.000 \n Mean : 2.12 Mean : 2.225 Mean : 2.658 Mean : 2.235 \n 3rd Qu.: 1.55 3rd Qu.: 2.471 3rd Qu.: 6.474 3rd Qu.: 3.134 \n Max. :11.56 Max. :11.734 Max. :11.303 Max. :10.888 \n HSPC_427 HSPC_431 HSPC_432 HSPC_435 \n Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.102 Median :0.000 Median : 1.098 \n Mean : 1.829 Mean : 2.360 Mean :2.169 Mean : 2.060 \n 3rd Qu.: 2.980 3rd Qu.: 3.640 3rd Qu.:3.261 3rd Qu.: 2.744 \n Max. :10.517 Max. :10.533 Max. :9.911 Max. :10.677 \n HSPC_436 HSPC_440 HSPC_441 HSPC_442 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.4719 Median : 1.385 Median : 1.084 Median : 0.595 \n Mean : 2.3880 Mean : 1.712 Mean : 2.265 Mean : 2.109 \n 3rd Qu.: 4.3738 3rd Qu.: 2.079 3rd Qu.: 2.828 3rd Qu.: 2.193 \n Max. :11.2839 Max. :11.065 Max. :11.152 Max. :11.560 \n HSPC_443 HSPC_444 HSPC_446 HSPC_447 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.7734 Median : 1.374 Median : 0.000 Median : 1.113 \n Mean : 2.5663 Mean : 2.262 Mean : 1.475 Mean : 2.446 \n 3rd Qu.: 4.9423 3rd Qu.: 2.952 3rd Qu.: 1.683 3rd Qu.: 4.733 \n Max. :10.9262 Max. :10.705 Max. :10.545 Max. :10.303 \n HSPC_448 HSPC_449 HSPC_450 HSPC_451 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.139 Median : 1.344 Median : 0.000 Median : 1.759 \n Mean : 2.396 Mean : 2.164 Mean : 1.946 Mean : 1.806 \n 3rd Qu.: 3.660 3rd Qu.: 2.490 3rd Qu.: 2.483 3rd Qu.: 2.528 \n Max. :11.091 Max. :11.324 Max. :10.397 Max. :10.395 \n HSPC_453 HSPC_454 HSPC_455 HSPC_456 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.9321 Median : 0.5303 Median : 0.000 Median : 0.6497 \n Mean : 2.4906 Mean : 2.4477 Mean : 2.379 Mean : 2.4263 \n 3rd Qu.: 4.9604 3rd Qu.: 4.8773 3rd Qu.: 3.016 3rd Qu.: 5.4740 \n Max. :10.5263 Max. :11.1628 Max. :11.437 Max. :10.9787 \n HSPC_457 HSPC_459 HSPC_460 HSPC_461 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.313 \n Mean : 2.060 Mean : 2.403 Mean : 1.712 Mean : 1.875 \n 3rd Qu.: 2.937 3rd Qu.: 3.029 3rd Qu.: 1.598 3rd Qu.: 2.104 \n Max. :11.746 Max. :12.135 Max. :12.526 Max. :10.210 \n HSPC_462 HSPC_463 HSPC_465 HSPC_466 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.7257 Median : 0.000 Median : 0.5816 \n Mean : 2.095 Mean : 2.2325 Mean : 2.000 Mean : 1.9972 \n 3rd Qu.: 2.578 3rd Qu.: 2.3442 3rd Qu.: 2.633 3rd Qu.: 2.2384 \n Max. :11.429 Max. :11.1776 Max. :11.064 Max. :11.5475 \n HSPC_467 HSPC_468 HSPC_470 HSPC_471 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.177 Median : 0.649 Median : 0.000 Median : 0.000 \n Mean : 1.866 Mean : 2.130 Mean : 1.774 Mean : 2.279 \n 3rd Qu.: 2.258 3rd Qu.: 2.513 3rd Qu.: 1.931 3rd Qu.: 2.744 \n Max. :10.632 Max. :10.527 Max. :10.781 Max. :11.533 \n HSPC_472 HSPC_473 HSPC_474 HSPC_475 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.265 Mean : 2.168 Mean : 2.016 Mean : 2.339 \n 3rd Qu.: 2.982 3rd Qu.: 2.677 3rd Qu.: 2.061 3rd Qu.: 3.319 \n Max. :11.795 Max. :12.071 Max. :11.732 Max. :10.672 \n HSPC_477 HSPC_478 HSPC_479 HSPC_480 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.6278 Median : 0.000 Median : 1.281 Median : 1.034 \n Mean : 1.9910 Mean : 2.068 Mean : 2.175 Mean : 2.239 \n 3rd Qu.: 1.6695 3rd Qu.: 3.402 3rd Qu.: 3.028 3rd Qu.: 2.642 \n Max. :11.1171 Max. :12.113 Max. :11.277 Max. :10.641 \n HSPC_482 HSPC_483 HSPC_485 HSPC_486 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.088 Median : 0.6036 Median : 1.411 \n Mean : 1.998 Mean : 2.454 Mean : 2.3824 Mean : 2.078 \n 3rd Qu.: 2.648 3rd Qu.: 3.006 3rd Qu.: 4.8213 3rd Qu.: 2.579 \n Max. :13.948 Max. :10.722 Max. :11.8691 Max. :10.155 \n HSPC_488 HSPC_489 HSPC_490 HSPC_491 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.310 Median : 0.000 \n Mean : 1.809 Mean : 1.947 Mean : 2.518 Mean : 2.268 \n 3rd Qu.: 2.120 3rd Qu.: 2.330 3rd Qu.: 4.140 3rd Qu.: 3.300 \n Max. :11.271 Max. :11.518 Max. :11.646 Max. :10.366 \n HSPC_492 HSPC_493 HSPC_494 HSPC_495 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.127 Mean : 2.054 Mean : 2.255 Mean : 2.326 \n 3rd Qu.: 2.322 3rd Qu.: 3.060 3rd Qu.: 3.386 3rd Qu.: 3.812 \n Max. :11.674 Max. :10.404 Max. :10.461 Max. :10.304 \n HSPC_496 HSPC_497 HSPC_498 HSPC_499 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.945 Median : 0.5839 Median : 0.000 \n Mean : 1.938 Mean : 2.287 Mean : 2.3731 Mean : 2.045 \n 3rd Qu.: 2.227 3rd Qu.: 2.872 3rd Qu.: 3.6112 3rd Qu.: 2.358 \n Max. :11.323 Max. :11.873 Max. :11.3264 Max. :10.632 \n HSPC_500 HSPC_501 HSPC_502 HSPC_503 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.9146 Median : 0.7789 \n Mean : 2.199 Mean : 2.209 Mean : 2.2727 Mean : 2.4495 \n 3rd Qu.: 2.678 3rd Qu.: 3.150 3rd Qu.: 2.8888 3rd Qu.: 5.4034 \n Max. :11.665 Max. :10.727 Max. :11.4591 Max. :11.5376 \n HSPC_504 HSPC_505 HSPC_506 HSPC_507 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.137 Mean : 2.132 Mean : 2.017 Mean : 2.314 \n 3rd Qu.: 3.035 3rd Qu.: 2.744 3rd Qu.: 2.794 3rd Qu.: 3.175 \n Max. :11.625 Max. :11.385 Max. :11.467 Max. :11.232 \n HSPC_508 HSPC_509 HSPC_510 HSPC_512 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.2297 Median : 1.691 Median : 1.166 Median : 0.000 \n Mean : 1.9265 Mean : 2.548 Mean : 2.319 Mean : 2.482 \n 3rd Qu.: 0.8975 3rd Qu.: 4.397 3rd Qu.: 3.492 3rd Qu.: 3.753 \n Max. :12.0747 Max. :10.603 Max. :10.885 Max. :12.492 \n HSPC_514 HSPC_515 HSPC_516 HSPC_518 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.109 Median : 0.8853 Median : 0.000 \n Mean : 2.295 Mean : 2.298 Mean : 2.5439 Mean : 2.649 \n 3rd Qu.: 2.429 3rd Qu.: 2.560 3rd Qu.: 4.6629 3rd Qu.: 5.581 \n Max. :11.783 Max. :12.193 Max. :12.1718 Max. :11.838 \n HSPC_520 HSPC_521 HSPC_522 HSPC_523 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.3648 \n Mean : 2.295 Mean : 2.348 Mean : 2.529 Mean : 1.9471 \n 3rd Qu.: 2.975 3rd Qu.: 3.375 3rd Qu.: 5.350 3rd Qu.: 1.5726 \n Max. :12.289 Max. :11.712 Max. :10.364 Max. :12.5906 \n HSPC_524 HSPC_526 HSPC_527 HSPC_528 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.777 Median : 0.532 \n Mean : 1.989 Mean : 2.218 Mean : 2.133 Mean : 2.238 \n 3rd Qu.: 3.267 3rd Qu.: 2.431 3rd Qu.: 1.651 3rd Qu.: 2.095 \n Max. :12.105 Max. :10.870 Max. :12.017 Max. :12.183 \n HSPC_530 HSPC_532 HSPC_533 HSPC_534 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.7537 Median : 0.000 \n Mean : 2.017 Mean : 1.856 Mean : 1.7546 Mean : 2.183 \n 3rd Qu.: 2.514 3rd Qu.: 1.816 3rd Qu.: 1.3378 3rd Qu.: 2.311 \n Max. :11.549 Max. :11.255 Max. :11.5862 Max. :11.696 \n HSPC_535 HSPC_537 HSPC_538 HSPC_539 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.122 Mean : 2.010 Mean : 2.501 Mean : 2.463 \n 3rd Qu.: 2.733 3rd Qu.: 2.541 3rd Qu.: 4.886 3rd Qu.: 4.100 \n Max. :10.793 Max. :10.305 Max. :11.359 Max. :11.755 \n HSPC_540 HSPC_541 HSPC_543 HSPC_544 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.9898 Median : 2.362 Median : 0.000 Median : 0.8222 \n Mean : 2.1775 Mean : 2.613 Mean : 2.275 Mean : 2.8070 \n 3rd Qu.: 1.9846 3rd Qu.: 4.440 3rd Qu.: 2.690 3rd Qu.: 6.4209 \n Max. :12.2963 Max. :11.844 Max. :10.983 Max. :10.7976 \n HSPC_545 HSPC_546 HSPC_547 HSPC_548 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 1.485 Median : 0.000 Median : 0.6548 Median : 1.456 \n Mean : 2.215 Mean : 2.424 Mean : 2.5255 Mean : 2.415 \n 3rd Qu.: 2.677 3rd Qu.: 3.573 3rd Qu.: 2.8714 3rd Qu.: 2.639 \n Max. :11.815 Max. :11.235 Max. :11.8801 Max. :11.955 \n HSPC_549 HSPC_550 HSPC_551 HSPC_552 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.750 Median : 1.287 Median : 1.226 \n Mean : 2.149 Mean : 2.592 Mean : 2.680 Mean : 2.236 \n 3rd Qu.: 2.289 3rd Qu.: 4.686 3rd Qu.: 4.007 3rd Qu.: 2.669 \n Max. :11.827 Max. :12.064 Max. :11.874 Max. :11.581 \n HSPC_553 HSPC_554 HSPC_555 HSPC_556 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.4709 Median : 0.000 Median : 0.000 Median : 0.9369 \n Mean : 2.6931 Mean : 2.090 Mean : 1.903 Mean : 2.4784 \n 3rd Qu.: 6.4420 3rd Qu.: 2.158 3rd Qu.: 2.579 3rd Qu.: 3.4024 \n Max. :11.0566 Max. :11.755 Max. :11.245 Max. :11.9838 \n HSPC_557 HSPC_559 HSPC_560 HSPC_562 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.681 Median : 0.000 \n Mean : 1.972 Mean : 1.937 Mean : 2.082 Mean : 2.470 \n 3rd Qu.: 1.880 3rd Qu.: 2.411 3rd Qu.: 2.436 3rd Qu.: 4.148 \n Max. :11.792 Max. :11.871 Max. :11.761 Max. :11.958 \n HSPC_563 HSPC_566 HSPC_567 HSPC_568 \n Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.00 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 1.83 Mean : 2.486 Mean : 2.186 Mean : 2.267 \n 3rd Qu.: 2.29 3rd Qu.: 3.577 3rd Qu.: 2.254 3rd Qu.: 2.957 \n Max. :10.59 Max. :12.452 Max. :11.302 Max. :10.851 \n HSPC_569 HSPC_571 HSPC_573 HSPC_574 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.771 Median : 1.042 Median : 0.000 Median : 0.7547 \n Mean : 2.283 Mean : 2.213 Mean : 2.089 Mean : 2.3196 \n 3rd Qu.: 3.021 3rd Qu.: 2.879 3rd Qu.: 2.291 3rd Qu.: 5.6078 \n Max. :10.720 Max. :10.939 Max. :11.397 Max. :10.4741 \n HSPC_575 HSPC_576 HSPC_577 HSPC_578 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.606 Median : 0.000 \n Mean : 2.016 Mean : 2.206 Mean : 2.358 Mean : 2.257 \n 3rd Qu.: 2.267 3rd Qu.: 2.741 3rd Qu.: 3.198 3rd Qu.: 2.923 \n Max. :10.687 Max. :11.201 Max. :11.613 Max. :12.323 \n HSPC_579 HSPC_580 HSPC_582 HSPC_584 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.182 Median : 0.9442 Median : 0.000 Median : 0.000 \n Mean : 2.472 Mean : 2.4264 Mean : 2.218 Mean : 2.276 \n 3rd Qu.: 5.009 3rd Qu.: 3.5841 3rd Qu.: 3.332 3rd Qu.: 3.067 \n Max. :11.096 Max. :10.6790 Max. :10.882 Max. :10.954 \n HSPC_585 HSPC_586 HSPC_589 HSPC_590 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8915 Median : 0.000 Median : 1.192 \n Mean : 2.034 Mean : 2.0490 Mean : 2.274 Mean : 2.252 \n 3rd Qu.: 2.157 3rd Qu.: 1.8340 3rd Qu.: 3.655 3rd Qu.: 2.364 \n Max. :11.956 Max. :11.4729 Max. :11.198 Max. :10.673 \n HSPC_592 HSPC_593 HSPC_594 HSPC_595 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.228 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.317 Mean : 2.329 Mean : 2.474 Mean : 1.463 \n 3rd Qu.: 2.671 3rd Qu.: 3.263 3rd Qu.: 4.396 3rd Qu.: 1.757 \n Max. :12.036 Max. :10.626 Max. :11.347 Max. :11.286 \n HSPC_596 HSPC_597 HSPC_598 HSPC_599 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.392 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.283 Mean : 1.858 Mean : 1.954 Mean : 1.905 \n 3rd Qu.: 3.425 3rd Qu.: 2.296 3rd Qu.: 2.320 3rd Qu.: 2.497 \n Max. :10.899 Max. :11.002 Max. :11.117 Max. :11.248 \n HSPC_600 HSPC_601 HSPC_602 HSPC_603 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.662 Median : 0.000 \n Mean : 2.335 Mean : 1.905 Mean : 2.343 Mean : 2.281 \n 3rd Qu.: 3.827 3rd Qu.: 2.376 3rd Qu.: 3.272 3rd Qu.: 3.048 \n Max. :11.208 Max. :11.022 Max. :10.908 Max. :11.464 \n HSPC_604 HSPC_606 HSPC_607 HSPC_608 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.136 Mean : 2.392 Mean : 2.142 Mean : 2.139 \n 3rd Qu.: 2.516 3rd Qu.: 4.726 3rd Qu.: 3.187 3rd Qu.: 2.885 \n Max. :11.743 Max. :11.210 Max. :10.319 Max. :10.802 \n HSPC_610 HSPC_612 HSPC_613 HSPC_614 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.315 \n Mean : 2.327 Mean : 2.298 Mean : 2.228 Mean : 2.364 \n 3rd Qu.: 3.718 3rd Qu.: 3.138 3rd Qu.: 2.705 3rd Qu.: 3.136 \n Max. :10.860 Max. :11.564 Max. :10.560 Max. :11.824 \n HSPC_615 HSPC_617 HSPC_618 HSPC_620 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8525 Median : 0.000 Median : 0.000 \n Mean : 1.964 Mean : 2.2100 Mean : 2.229 Mean : 1.881 \n 3rd Qu.: 2.451 3rd Qu.: 2.3301 3rd Qu.: 2.885 3rd Qu.: 2.518 \n Max. :11.058 Max. :10.9434 Max. :11.210 Max. :11.388 \n HSPC_623 HSPC_624 HSPC_625 HSPC_626 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.7201 Median : 0.000 Median : 0.000 \n Mean : 2.563 Mean : 2.0968 Mean : 2.042 Mean : 2.262 \n 3rd Qu.: 4.626 3rd Qu.: 1.8437 3rd Qu.: 2.938 3rd Qu.: 3.424 \n Max. :10.954 Max. :10.9459 Max. :11.226 Max. :11.770 \n HSPC_627 HSPC_628 HSPC_629 HSPC_630 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.269 Mean : 2.302 Mean : 2.212 Mean : 2.519 \n 3rd Qu.: 3.952 3rd Qu.: 2.875 3rd Qu.: 2.625 3rd Qu.: 4.511 \n Max. :11.426 Max. :11.792 Max. :11.139 Max. :11.519 \n HSPC_631 HSPC_633 HSPC_634 HSPC_635 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.303 Mean : 2.329 Mean : 2.268 Mean : 2.054 \n 3rd Qu.: 2.685 3rd Qu.: 3.619 3rd Qu.: 3.662 3rd Qu.: 2.629 \n Max. :10.996 Max. :12.011 Max. :11.406 Max. :11.178 \n HSPC_636 HSPC_637 HSPC_638 HSPC_639 \n Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.9389 Median : 0.5101 Median : 0.9966 \n Mean : 1.953 Mean : 2.1351 Mean : 1.6966 Mean : 1.5879 \n 3rd Qu.: 2.129 3rd Qu.: 2.4817 3rd Qu.: 1.6879 3rd Qu.: 1.6840 \n Max. :11.057 Max. :11.1881 Max. :10.8837 Max. :10.9561 \n HSPC_640 HSPC_641 HSPC_643 HSPC_644 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.025 Median : 1.000 Median : 1.706 Median : 0.4904 \n Mean : 2.136 Mean : 1.957 Mean : 2.468 Mean : 2.4726 \n 3rd Qu.: 2.119 3rd Qu.: 2.001 3rd Qu.: 3.329 3rd Qu.: 5.6227 \n Max. :11.173 Max. :11.056 Max. :12.016 Max. :11.0232 \n HSPC_645 HSPC_646 HSPC_648 HSPC_649 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.7157 Median : 0.9959 Median : 1.519 Median : 0.7139 \n Mean : 2.3517 Mean : 2.0594 Mean : 2.267 Mean : 2.3593 \n 3rd Qu.: 4.5630 3rd Qu.: 2.3154 3rd Qu.: 2.722 3rd Qu.: 4.1542 \n Max. :10.9922 Max. :11.6070 Max. :11.243 Max. :10.7707 \n HSPC_651 HSPC_652 HSPC_654 HSPC_656 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 \n Median : 0.000 Median : 0.000 Median : 1.398 Median :0.000 \n Mean : 2.550 Mean : 1.764 Mean : 2.108 Mean :1.983 \n 3rd Qu.: 5.615 3rd Qu.: 2.038 3rd Qu.: 2.562 3rd Qu.:2.505 \n Max. :11.202 Max. :10.897 Max. :10.367 Max. :9.673 \n HSPC_657 HSPC_658 HSPC_660 HSPC_661 \n Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median :1.253 Median : 1.491 \n Mean : 1.839 Mean : 2.319 Mean :2.542 Mean : 2.401 \n 3rd Qu.: 2.239 3rd Qu.: 4.021 3rd Qu.:5.274 3rd Qu.: 2.775 \n Max. :12.132 Max. :11.264 Max. :9.852 Max. :11.647 \n HSPC_662 HSPC_663 HSPC_664 HSPC_665 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.9407 Median : 0.6452 \n Mean : 2.298 Mean : 2.726 Mean : 2.4039 Mean : 2.1211 \n 3rd Qu.: 2.939 3rd Qu.: 6.519 3rd Qu.: 3.4095 3rd Qu.: 2.0744 \n Max. :11.277 Max. :12.152 Max. :10.9423 Max. :12.0111 \n HSPC_666 HSPC_667 HSPC_668 HSPC_669 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.130 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.815 Mean : 2.075 Mean : 2.245 Mean : 1.992 \n 3rd Qu.: 6.359 3rd Qu.: 2.549 3rd Qu.: 2.407 3rd Qu.: 2.426 \n Max. :11.052 Max. :11.406 Max. :11.061 Max. :11.752 \n HSPC_670 HSPC_671 HSPC_672 HSPC_673 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.336 Mean : 2.349 Mean : 2.041 Mean : 2.148 \n 3rd Qu.: 3.188 3rd Qu.: 3.777 3rd Qu.: 2.057 3rd Qu.: 2.723 \n Max. :11.021 Max. :10.846 Max. :11.212 Max. :11.579 \n HSPC_674 HSPC_676 HSPC_678 HSPC_679 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.525 Median : 1.531 \n Mean : 1.801 Mean : 2.239 Mean : 2.298 Mean : 2.133 \n 3rd Qu.: 1.892 3rd Qu.: 3.097 3rd Qu.: 3.089 3rd Qu.: 2.737 \n Max. :10.875 Max. :10.496 Max. :12.125 Max. :11.583 \n HSPC_680 HSPC_681 HSPC_682 HSPC_683 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.037 Median : 1.043 Median : 1.180 \n Mean : 2.573 Mean : 2.277 Mean : 2.586 Mean : 2.498 \n 3rd Qu.: 4.165 3rd Qu.: 4.210 3rd Qu.: 5.432 3rd Qu.: 3.929 \n Max. :11.100 Max. :10.154 Max. :11.095 Max. :10.859 \n HSPC_687 HSPC_689 HSPC_690 HSPC_692 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 2.091 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.136 Mean : 2.489 Mean : 2.686 Mean : 2.285 \n 3rd Qu.: 2.911 3rd Qu.: 4.106 3rd Qu.: 5.055 3rd Qu.: 3.427 \n Max. :11.380 Max. :10.693 Max. :10.408 Max. :12.242 \n HSPC_695 HSPC_696 HSPC_697 HSPC_698 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.2681 Median : 1.538 Median : 1.271 Median : 0.000 \n Mean : 1.6151 Mean : 2.688 Mean : 2.529 Mean : 2.531 \n 3rd Qu.: 0.6895 3rd Qu.: 5.560 3rd Qu.: 4.779 3rd Qu.: 4.387 \n Max. :12.4139 Max. :10.880 Max. :10.292 Max. :12.146 \n HSPC_699 HSPC_700 HSPC_701 HSPC_702 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.157 Median : 0.000 \n Mean : 2.586 Mean : 2.402 Mean : 2.401 Mean : 2.723 \n 3rd Qu.: 4.595 3rd Qu.: 4.797 3rd Qu.: 3.889 3rd Qu.: 4.822 \n Max. :11.389 Max. :10.630 Max. :11.750 Max. :11.805 \n HSPC_703 HSPC_704 HSPC_705 HSPC_706 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 2.193 Median : 0.000 Median : 0.9795 Median : 1.273 \n Mean : 2.543 Mean : 2.598 Mean : 2.5048 Mean : 2.364 \n 3rd Qu.: 3.935 3rd Qu.: 4.335 3rd Qu.: 5.0680 3rd Qu.: 3.492 \n Max. :11.710 Max. :11.488 Max. :11.3580 Max. :10.447 \n HSPC_707 HSPC_708 HSPC_709 HSPC_714 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.361 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.371 Mean : 2.509 Mean : 2.601 Mean : 2.326 \n 3rd Qu.: 3.626 3rd Qu.: 3.832 3rd Qu.: 5.060 3rd Qu.: 3.324 \n Max. :11.796 Max. :10.865 Max. :10.145 Max. :11.126 \n HSPC_716 HSPC_717 HSPC_719 HSPC_720 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 1.154 Median : 1.855 Median : 0.8206 \n Mean : 2.325 Mean : 2.302 Mean : 2.519 Mean : 2.5768 \n 3rd Qu.: 3.356 3rd Qu.: 2.833 3rd Qu.: 4.115 3rd Qu.: 5.5594 \n Max. :11.812 Max. :11.047 Max. :12.237 Max. :10.5895 \n HSPC_721 HSPC_722 HSPC_723 HSPC_724 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 2.113 Median : 1.185 Median : 0.8421 Median : 0.6485 \n Mean : 2.205 Mean : 1.814 Mean : 2.6174 Mean : 1.9644 \n 3rd Qu.: 3.456 3rd Qu.: 2.269 3rd Qu.: 4.9545 3rd Qu.: 1.9402 \n Max. :10.706 Max. :10.709 Max. :11.5956 Max. :11.3505 \n HSPC_725 HSPC_727 HSPC_729 HSPC_730 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 1.577 Median : 1.576 Median : 0.9579 Median : 1.135 \n Mean : 2.483 Mean : 2.436 Mean : 2.2448 Mean : 2.445 \n 3rd Qu.: 3.741 3rd Qu.: 3.447 3rd Qu.: 2.7343 3rd Qu.: 3.475 \n Max. :10.647 Max. :11.512 Max. :10.9657 Max. :11.121 \n HSPC_731 HSPC_732 HSPC_733 HSPC_734 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.130 Median : 0.6937 Median : 1.436 Median : 0.7333 \n Mean : 2.854 Mean : 2.1051 Mean : 2.489 Mean : 2.5404 \n 3rd Qu.: 6.019 3rd Qu.: 2.0311 3rd Qu.: 3.738 3rd Qu.: 5.6282 \n Max. :10.471 Max. :11.0494 Max. :10.929 Max. :10.4547 \n HSPC_735 HSPC_736 HSPC_737 HSPC_738 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.033 Median : 0.6789 Median : 1.185 Median : 1.514 \n Mean : 2.389 Mean : 2.0224 Mean : 2.722 Mean : 2.503 \n 3rd Qu.: 3.056 3rd Qu.: 2.0017 3rd Qu.: 5.669 3rd Qu.: 3.602 \n Max. :10.866 Max. :11.8100 Max. :11.076 Max. :10.473 \n HSPC_740 HSPC_742 HSPC_743 HSPC_744 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8437 Median : 1.122 Median : 1.213 \n Mean : 2.506 Mean : 1.8949 Mean : 2.028 Mean : 2.048 \n 3rd Qu.: 3.794 3rd Qu.: 1.7586 3rd Qu.: 2.840 3rd Qu.: 2.309 \n Max. :10.618 Max. :11.6327 Max. :10.449 Max. :10.598 \n HSPC_745 HSPC_746 HSPC_747 HSPC_748 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 2.403 Median : 2.184 Median : 0.000 Median : 1.181 \n Mean : 2.309 Mean : 2.153 Mean : 2.543 Mean : 2.017 \n 3rd Qu.: 3.793 3rd Qu.: 3.016 3rd Qu.: 4.751 3rd Qu.: 2.264 \n Max. :10.882 Max. :10.988 Max. :10.860 Max. :12.153 \n HSPC_749 HSPC_750 HSPC_751 HSPC_752 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.444 Median : 1.030 Median : 1.567 Median : 2.228 \n Mean : 2.477 Mean : 2.370 Mean : 2.416 Mean : 2.529 \n 3rd Qu.: 3.501 3rd Qu.: 3.052 3rd Qu.: 3.435 3rd Qu.: 3.976 \n Max. :11.391 Max. :11.167 Max. :10.239 Max. :10.586 \n HSPC_753 HSPC_755 HSPC_756 HSPC_757 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.062 Median : 0.740 Median : 1.731 Median : 1.395 \n Mean : 2.313 Mean : 2.102 Mean : 2.592 Mean : 2.477 \n 3rd Qu.: 2.961 3rd Qu.: 2.509 3rd Qu.: 4.107 3rd Qu.: 3.253 \n Max. :11.202 Max. :10.559 Max. :10.783 Max. :10.973 \n HSPC_758 HSPC_759 HSPC_760 HSPC_761 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.8648 Median : 0.9415 Median : 1.052 Median : 0.6917 \n Mean : 2.6819 Mean : 2.1274 Mean : 2.288 Mean : 2.2992 \n 3rd Qu.: 4.7233 3rd Qu.: 2.2271 3rd Qu.: 2.404 3rd Qu.: 2.6015 \n Max. :11.1096 Max. :11.2534 Max. :11.008 Max. :11.7228 \n HSPC_762 HSPC_764 HSPC_765 HSPC_766 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.271 Median : 1.784 Median : 2.116 Median : 0.9828 \n Mean : 2.242 Mean : 2.068 Mean : 2.100 Mean : 2.1721 \n 3rd Qu.: 2.734 3rd Qu.: 3.059 3rd Qu.: 2.939 3rd Qu.: 2.6115 \n Max. :12.043 Max. :11.003 Max. :12.757 Max. :10.2002 \n HSPC_767 HSPC_768 HSPC_769 HSPC_770 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.6646 Median : 1.703 Median : 0.000 Median : 1.760 \n Mean : 1.9552 Mean : 2.365 Mean : 2.080 Mean : 2.343 \n 3rd Qu.: 1.9730 3rd Qu.: 3.325 3rd Qu.: 3.289 3rd Qu.: 3.122 \n Max. :11.3033 Max. :10.958 Max. :11.176 Max. :10.497 \n HSPC_771 HSPC_772 HSPC_773 HSPC_774 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 1.178 Median : 1.601 Median : 0.9901 Median : 0.9736 \n Mean : 2.527 Mean : 2.283 Mean : 1.8628 Mean : 2.5263 \n 3rd Qu.: 3.342 3rd Qu.: 2.828 3rd Qu.: 1.9851 3rd Qu.: 5.4694 \n Max. :11.156 Max. :10.625 Max. :10.7274 Max. :10.7701 \n HSPC_776 HSPC_777 HSPC_778 HSPC_780 \n Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.053 Median :1.435 Median : 1.178 \n Mean : 2.315 Mean : 2.110 Mean :2.130 Mean : 2.476 \n 3rd Qu.: 3.788 3rd Qu.: 2.673 3rd Qu.:3.488 3rd Qu.: 3.769 \n Max. :11.105 Max. :11.646 Max. :9.535 Max. :11.265 \n HSPC_781 HSPC_782 HSPC_783 HSPC_784 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.050 \n Mean : 1.911 Mean : 2.416 Mean : 2.254 Mean : 2.175 \n 3rd Qu.: 2.884 3rd Qu.: 3.872 3rd Qu.: 2.548 3rd Qu.: 2.468 \n Max. :11.445 Max. :10.161 Max. :10.970 Max. :10.958 \n HSPC_785 HSPC_786 HSPC_787 HSPC_788 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 1.148 Median : 0.000 Median : 0.9386 \n Mean : 2.230 Mean : 2.467 Mean : 2.100 Mean : 1.9749 \n 3rd Qu.: 2.466 3rd Qu.: 3.899 3rd Qu.: 2.991 3rd Qu.: 2.6662 \n Max. :11.041 Max. :11.080 Max. :10.690 Max. :11.1078 \n HSPC_789 HSPC_790 HSPC_791 HSPC_794 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.181 Median : 1.353 Median : 1.790 Median : 1.113 \n Mean : 2.225 Mean : 2.255 Mean : 2.699 Mean : 2.225 \n 3rd Qu.: 2.876 3rd Qu.: 2.852 3rd Qu.: 4.931 3rd Qu.: 2.768 \n Max. :11.245 Max. :11.558 Max. :11.104 Max. :11.118 \n HSPC_795 HSPC_796 HSPC_797 HSPC_798 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.8317 Median : 0.7001 Median : 0.8722 Median : 1.531 \n Mean : 2.3985 Mean : 2.6865 Mean : 2.6172 Mean : 2.485 \n 3rd Qu.: 3.4461 3rd Qu.: 5.6688 3rd Qu.: 5.3078 3rd Qu.: 3.098 \n Max. :11.0956 Max. :11.0829 Max. :11.4339 Max. :10.933 \n HSPC_799 HSPC_800 HSPC_801 HSPC_802 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.033 \n Mean : 2.179 Mean : 2.173 Mean : 2.427 Mean : 2.613 \n 3rd Qu.: 3.517 3rd Qu.: 2.865 3rd Qu.: 4.665 3rd Qu.: 3.780 \n Max. :11.666 Max. :11.263 Max. :10.905 Max. :10.864 \n HSPC_803 HSPC_804 HSPC_806 HSPC_807 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 2.103 Median : 0.7501 \n Mean : 2.395 Mean : 2.301 Mean : 2.222 Mean : 2.2476 \n 3rd Qu.: 3.883 3rd Qu.: 3.167 3rd Qu.: 3.445 3rd Qu.: 2.3481 \n Max. :10.766 Max. :11.298 Max. :10.326 Max. :11.2700 \n HSPC_808 HSPC_809 HSPC_810 HSPC_812 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.6619 Median : 1.788 Median : 1.651 Median : 0.8459 \n Mean : 2.1677 Mean : 2.544 Mean : 2.471 Mean : 2.2960 \n 3rd Qu.: 2.5355 3rd Qu.: 3.730 3rd Qu.: 3.662 3rd Qu.: 2.6906 \n Max. :10.9302 Max. :11.791 Max. :10.829 Max. :11.5500 \n HSPC_813 HSPC_814 HSPC_815 HSPC_816 \n Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.6278 Median : 1.110 Median : 0.9631 Median : 1.346 \n Mean : 2.2448 Mean : 2.762 Mean : 2.4587 Mean : 2.341 \n 3rd Qu.: 2.3066 3rd Qu.: 5.996 3rd Qu.: 3.4228 3rd Qu.: 2.842 \n Max. :12.0043 Max. :10.406 Max. :11.4527 Max. :11.151 \n HSPC_818 HSPC_819 HSPC_820 HSPC_821 \n Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.9967 Median : 1.365 Median : 0.8099 Median : 1.382 \n Mean : 2.3081 Mean : 2.426 Mean : 2.1063 Mean : 2.532 \n 3rd Qu.: 2.9942 3rd Qu.: 3.632 3rd Qu.: 2.4643 3rd Qu.: 3.462 \n Max. :11.9931 Max. :10.672 Max. :11.2412 Max. :12.126 \n HSPC_822 HSPC_824 HSPC_825 HSPC_826 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.387 Median : 0.000 Median : 1.386 Median : 1.324 \n Mean : 2.503 Mean : 2.084 Mean : 2.162 Mean : 2.398 \n 3rd Qu.: 3.799 3rd Qu.: 2.342 3rd Qu.: 2.897 3rd Qu.: 3.150 \n Max. :11.892 Max. :11.365 Max. :11.498 Max. :11.198 \n HSPC_827 HSPC_828 HSPC_831 HSPC_832 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.746 Median : 1.003 Median : 1.304 Median : 1.035 \n Mean : 2.239 Mean : 2.145 Mean : 2.589 Mean : 2.384 \n 3rd Qu.: 2.638 3rd Qu.: 2.326 3rd Qu.: 3.866 3rd Qu.: 3.450 \n Max. :12.101 Max. :10.710 Max. :10.839 Max. :10.686 \n HSPC_833 HSPC_834 HSPC_835 HSPC_836 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.7166 Median : 0.9245 Median : 1.006 Median : 0.000 \n Mean : 2.3553 Mean : 2.0872 Mean : 2.552 Mean : 2.471 \n 3rd Qu.: 3.9364 3rd Qu.: 2.4568 3rd Qu.: 4.034 3rd Qu.: 3.994 \n Max. :11.1695 Max. :11.1803 Max. :11.779 Max. :11.316 \n HSPC_837 HSPC_838 HSPC_839 HSPC_840 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.312 Median : 0.9838 Median : 1.083 Median : 1.867 \n Mean : 2.590 Mean : 2.5281 Mean : 2.380 Mean : 2.548 \n 3rd Qu.: 4.443 3rd Qu.: 3.5551 3rd Qu.: 3.743 3rd Qu.: 3.609 \n Max. :10.672 Max. :11.2707 Max. :10.966 Max. :10.867 \n HSPC_841 HSPC_842 HSPC_843 HSPC_844 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.439 Median : 1.774 Median : 1.257 Median : 1.584 \n Mean : 2.408 Mean : 2.380 Mean : 2.845 Mean : 2.627 \n 3rd Qu.: 3.494 3rd Qu.: 3.490 3rd Qu.: 6.768 3rd Qu.: 3.951 \n Max. :10.930 Max. :11.137 Max. :11.933 Max. :11.446 \n HSPC_845 HSPC_846 HSPC_848 HSPC_849 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.227 Median : 1.401 Median : 0.000 Median : 1.602 \n Mean : 2.464 Mean : 2.240 Mean : 2.152 Mean : 2.402 \n 3rd Qu.: 3.377 3rd Qu.: 2.920 3rd Qu.: 2.554 3rd Qu.: 2.920 \n Max. :10.535 Max. :11.519 Max. :11.266 Max. :11.678 \n HSPC_851 HSPC_852 \n Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 \n Mean : 2.319 Mean : 2.143 \n 3rd Qu.: 3.373 3rd Qu.: 2.901 \n Max. :11.602 Max. :11.469 \n\n\nHmmmm, did you get all that? Nope, me neither! We have 701 cells but we only have 6 samples for the frogs. We will need a different approach to get an overview but I find it is still useful to look at the few columns\n🎬 Get a quick overview the first 20 columns:\n\nsummary(hspc[1:20])\n\n ensembl_gene_id HSPC_001 HSPC_002 HSPC_003 \n Length:280 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Mode :character Median : 0.000 Median : 0.000 Median : 0.9929 \n Mean : 2.143 Mean : 1.673 Mean : 2.5964 \n 3rd Qu.: 2.120 3rd Qu.: 2.239 3rd Qu.: 6.1559 \n Max. :12.567 Max. :11.976 Max. :11.1138 \n HSPC_004 HSPC_006 HSPC_008 HSPC_009 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 \n Median : 0.000 Median : 1.276 Median : 0.000 Median :0.000 \n Mean : 1.851 Mean : 2.338 Mean : 2.375 Mean :2.220 \n 3rd Qu.: 2.466 3rd Qu.: 3.536 3rd Qu.: 3.851 3rd Qu.:3.594 \n Max. :11.133 Max. :10.014 Max. :11.574 Max. :9.997 \n HSPC_011 HSPC_012 HSPC_014 HSPC_015 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.750 Median : 0.000 Median : 0.000 \n Mean : 2.285 Mean : 2.431 Mean : 2.295 Mean : 2.515 \n 3rd Qu.: 3.193 3rd Qu.: 3.741 3rd Qu.: 3.150 3rd Qu.: 3.789 \n Max. :11.260 Max. :10.905 Max. :11.051 Max. :10.751 \n HSPC_016 HSPC_017 HSPC_018 HSPC_020 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.9488 Median : 0.000 Median : 1.248 Median : 0.000 \n Mean : 2.6115 Mean : 2.146 Mean : 2.710 Mean : 2.509 \n 3rd Qu.: 5.9412 3rd Qu.: 2.357 3rd Qu.: 6.006 3rd Qu.: 4.470 \n Max. :11.3082 Max. :12.058 Max. :11.894 Max. :11.281 \n HSPC_021 HSPC_022 HSPC_023 HSPC_024 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.170 Mean : 2.287 Mean : 2.314 Mean : 2.195 \n 3rd Qu.: 2.996 3rd Qu.: 3.351 3rd Qu.: 2.749 3rd Qu.: 2.944 \n Max. :10.709 Max. :11.814 Max. :12.113 Max. :11.279 \n\n\nNotice that:\n\nthe maximum value is much less high than for the frogs and has decimals. That is because the mouse data are logged (to base 2) normalised counts, not raw counts as they are in the frog data set.\na minimum value of 0 appears in all 20 columns - perhaps that is true across the whole dataset (or at least common)\nat least some of the medians are zeros so there must be quite a lot of zeros\nthe few columns we can see are roughly similar\nit would not be very practical to plot the distributions of values in cell cell using facet_wrap().\n\nIn this data set, there is even more of an advantage of using the pivot_longer(), group_by() and summarise() approach. We will be able to open the dataframe in the Viewer and make plots to examine whether the distributions are similar across cells.\n🎬 Summarise all the cells:\n\nhspc_summary_samp <- hspc |>\n pivot_longer(cols = -ensembl_gene_id,\n names_to = \"cell\",\n values_to = \"expr\") |>\n group_by(cell) |>\n summarise(min = min(expr),\n lowerq = quantile(expr, 0.25),\n mean = mean(expr),\n median = median(expr),\n sd = sd(expr),\n upperq = quantile(expr, 0.75),\n max = max(expr),\n n_zero = sum(expr == 0))\n\nNotice 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.\n🎬 View the hspc_summary_samp dataframe (click on it in the environment).\nAll 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.\nTo 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 +/- one s.d. Not unlike a boxplot, but when you need the boxes too be very narrow!\n🎬 Create a pointrange plot.\n\nhspc_summary_samp |> \n ggplot(aes(x = cell, y = mean)) +\n geom_pointrange(aes(ymin = mean - sd, \n ymax = mean + sd ),\n size = 0.1)\n\n\n\n\nYou will need to use the Zoom button to pop the plot window out so you can make it as wide as possible\nThe things to notice are:\n\nthe average expression in cells is similar for all cells. This is good to know - if some cells had much lower expression perhaps there is something wrong with them, or their sequencing, and they should be excluded.\nthe distributions are roughly similar in width too\n\nThe default order of cell is alphabetical. It can be easier to see these (non-) effects if we order the lines by the size of the mean.\n🎬 Order a pointrange plot with reorder(variable_to_order, order_by).\n\nhspc_summary_samp |> \n ggplot(aes(x = reorder(cell, mean), y = mean)) +\n geom_pointrange(aes(ymin = mean - sd, \n ymax = mean + sd ),\n size = 0.1)\n\n\n\n\nreorder() arranges cell in increasing size of mean\n🎬 Write hspc_summary_samp to a file called “hspc_summary_samp.csv”:\nDistribution of values across the genes\n🐸 Frog genes\nThere are lots of genes in this dataset therefore we will take the same approach as that we took for the distributions across mouse cells. We will pivot the data to tidy and then summarise the counts for each gene.\n🎬 Summarise the counts for each genes:\n\ns30_summary_gene <- s30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n group_by(xenbase_gene_id) |>\n summarise(min = min(count),\n lowerq = quantile(count, 0.25),\n sd = sd(count),\n mean = mean(count),\n median = median(count),\n upperq = quantile(count, 0.75),\n max = max(count),\n total = sum(count),\n n_zero = sum(count == 0))\n\nI have calculated the values we used before with one addition: the sum of the counts (total).\n🎬 View the s30_summary_gene dataframe.\nNotice that we have:\n\na lot of genes with counts of zero in every sample\na lot of genes with zero counts in several of the samples\nsome very very low counts.\n\nThese should be filtered out because they are unreliable - or, at the least, uninformative. The goal of our downstream analysis will be to see if there is a signifcance difference in gene expression between the control and FGF-treated sibling. Since we have only three replicates in each group, having one or two unreliable, missing or zero values, makes such a determination impossible for a particular gene. We will use the total counts and the number of samples with non-zero values to filter our genes later.\nAs we have a lot of genes, it is again helpful to plot the mean counts with pointrange to get an overview. We will plot the log of the counts - we saw earlier that logging made it easier to understand the distribution of counts over such a wide range. We will also order the genes from lowest to highest mean count.\n🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():\n\ns30_summary_gene |> \n ggplot(aes(x = reorder(xenbase_gene_id, mean), y = log10(mean))) +\n geom_pointrange(aes(ymin = log10(mean - sd), \n ymax = log10(mean + sd )),\n size = 0.1)\n\n\n\n\n(Remember, the warning is expected since we have zeros).\nYou can see we also have quite a few genes with means less than 1 (log below zero). Note that the variability between genes (average counts between 0 and 102586) is far greater than between samples (average counts from 260 to 426) which is exactly what we would expect to see.\n🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:\n🐭 Mouse genes\nThere are fewer genes in this dataset, but still more than you can understand without the overview provided by a plot. We will again pivot the data to tidy and then summarise the expression for each gene.\n🎬 Summarise the expression for each genes:\n\nhspc_summary_gene <- hspc |>\n pivot_longer(cols = -ensembl_gene_id,\n names_to = \"cell\",\n values_to = \"expr\") |>\n group_by(ensembl_gene_id) |>\n summarise(min = min(expr),\n lowerq = quantile(expr, 0.25),\n sd = sd(expr),\n mean = mean(expr),\n median = median(expr),\n upperq = quantile(expr, 0.75),\n max = max(expr),\n total = sum(expr),\n n_zero = sum(expr == 0))\n\n🎬 View the hspc_summary_gene dataframe. Remember these are normalised and logged (base 2) so we should not see very large values.\nNotice that we have:\n\nno genes with 0 in every cell\nvery few genes (9) with no zeros at all\nquite a few genes with zero in many cells but this matters less than zeros in the frog samples because we had just 6 samples and we have 701 cells.\n\nAs we have a lot of genes, it is again helpful to plot the mean expression with pointrange to get an overview. We do not need to log the values but ordering the genes will help.\n🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():\n\nhspc_summary_gene |> \n ggplot(aes(x = reorder(ensembl_gene_id, mean), y = mean))+\n geom_pointrange(aes(ymin = mean - sd, \n ymax = mean + sd),\n size = 0.1)\n\n\n\n\nNote again that the variability between genes (average expression between 0.02 and and 10.03) is far greater than between cells (average expression from1.46 to 3.18) which is expected.\n🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:" + }, + { + "objectID": "omics/week-3/workshop.html#filtering-for-qc", + "href": "omics/week-3/workshop.html#filtering-for-qc", + "title": "Workshop", + "section": "Filtering for QC", + "text": "Filtering for QC\n🐸 Frog filtering\nOur samples look to be similarly well sequenced. There are no samples we should remove. However, some genes are not express or the expression values are so low in for a gene that they are uninformative. We will filter the s30_summary_gene dataframe to obtain a list of xenbase_gene_id we can use to filter s30.\nMy suggestion is to include only the genes with counts in all 6 or 5 out of 6 samples and those with total counts above 20.\n🎬 Filter the summary by gene dataframe:\n\ns30_summary_gene_filtered <- s30_summary_gene |> \n filter(total > 20) |> \n filter(n_zero < 2)\n\n🎬 Write the filtered summary by gene to file:\n\nwrite_csv(s30_summary_gene_filtered, \n file = \"data-processed/s30_summary_gene_filtered.csv\")\n\n🎬 Use the list of xenbase_gene_id in the filtered summary to filter the original dataset:\n\ns30_filtered <- s30 |> \n filter(xenbase_gene_id %in% s30_summary_gene_filtered$xenbase_gene_id)\n\n🎬 Write the filtered original to file:\n\nwrite_csv(s30_filtered, \n file = \"data-processed/s30_filtered.csv\")\n\n🐭 Mouse filtering\nI would take a different approach to filtering the single cell data. For the Frog samples we are examining the control and the FGF treated samples. This means have a low number of counts overall means the gene is not really expressed (detected) in any condition, and filtering out those genes is removing things that definitely are not interesting. For the mice, we have examined only one cell type but will be making comparisons between cells types. It may be that low expression of a gene in this cell type tells us something if that gene is highly expressed in another cell type. Instead, we will make statistical comparisons between the cell types and then filter based on overall expression, the difference in expression between cell types and whether that difference is significant.\nThe number of “replicates” is also important. When you have only three in each group it is not possible to make statistical comparisons when several replicates are zero. This is less of an issue with single cell data." + }, + { + "objectID": "omics/week-3/workshop.html#look-after-future-you", + "href": "omics/week-3/workshop.html#look-after-future-you", + "title": "Workshop", + "section": "🤗 Look after future you!", + "text": "🤗 Look after future you!\nYou need only do the section for your own project data\n🐸 Frogs and future you\n🎬 Create a new Project, frogs-88H, populated with folders and your 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. Make sure you can close down RStudio, reopen it and run your whole script again.\n🐭 Mice and future you\n🎬 Create a new Project, mice-88H, populated with folders and your data. Make a script file called hspc-prog.R. This will a be commented analysis of the hspc cells vs the prog cells. At this point you will have only code for the hspc cells. 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. Make sure you can close down RStudio, reopen it and run your whole script again." + }, + { + "objectID": "omics/week-3/workshop.html#footnotes", + "href": "omics/week-3/workshop.html#footnotes", + "title": "Workshop", + "section": "Footnotes", + "text": "Footnotes\n\nThis a result of the Central limit theorem,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.↩︎\nThis a result of the Central limit theorem,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.↩︎" }, { "objectID": "omics/week-3/study_before_workshop.html#omics-workshops", @@ -104,6 +314,13 @@ "section": "Deliverables", "text": "Deliverables\n\nDescribe the data\n\nNumber of cells/samples/reps/treatments\nnumber of genes\ntype of expression values\nprior processing\nmissing values\noverview of expression\nclustering of genes/samples\n\nReport on differential expression between two groups\n\nnumber of DE at 1%, 5% and 10%.\ntable of expression, fold changes, signifcance at each sig.\nVolcano plot\n\nReport list of marker candidate gene IDs for a cell type of choice. Justify filters. Table with fold FC, p values, IDs, canonical gene names\nInterpret the biology by reporting on a few group of genes and the processes in which they are involved.\nReport on your chosen genes and explain why you think they are good candidates for follow up work\n\n\n\n\n\n\n\nNestorowa, Sonia, Fiona K. Hamey, Blanca Pijuan Sala, Evangelia Diamanti, Mairi Shepherd, Elisa Laurenti, Nicola K. Wilson, David G. Kent, and Berthold Göttgens. 2016. “A Single-Cell Resolution Map of Mouse Hematopoietic Stem and Progenitor Cell Differentiation.” Blood 128 (8): e20–31. https://doi.org/10.1182/blood-2016-05-716480." }, + { + "objectID": "omics/week-3/overview.html", + "href": "omics/week-3/overview.html", + "title": "Overview", + "section": "", + "text": "xxxxx\n\nLearning objectives\n\ndd\ndd.\ndd\nd\n\n\n\nInstructions\n\nPrepare\n\n📖 Read how the data were generated and how they have been processed so far.\n\nWorkshop\n\n💻 Set up a Project\n💻 Import data\n💻 Explore the distribution of values across samples/cells and across genes/species\n💻 Perform quality control\n💻 Look after future you!\n\nConsolidate\n\n💻 Use the work you completed in the workshop as a template to apply to a new case." + }, { "objectID": "omics/omics.html", "href": "omics/omics.html", @@ -133,137 +350,18 @@ "text": "before - recap what we have - volcano plot described - GO terms -\nworkshop - open project import the data - revise! - volcano plot - kable table? knitr::kable(df) |> kableExtra::kable_styling() - annotating with go terms\nafter - document what you have done - repeat on another comparison" }, { - "objectID": "core/week-6/study_after_workshop.html", - "href": "core/week-6/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\n💻 xx." - }, - { - "objectID": "core/week-6/workshop.html", - "href": "core/week-6/workshop.html", - "title": "Workshop", - "section": "", - "text": "In this workshop you will\n\n\n\n\n\n\nKey" - }, - { - "objectID": "core/week-6/workshop.html#session-overview", - "href": "core/week-6/workshop.html#session-overview", - "title": "Workshop", - "section": "", - "text": "In this workshop you will\n\n\n\n\n\n\nKey" - }, - { - "objectID": "core/week-1/study_after_workshop.html#msc-bioinformatics-students-doing-bio00070m", - "href": "core/week-1/study_after_workshop.html#msc-bioinformatics-students-doing-bio00070m", - "title": "Independent Study to consolidate this week", - "section": "MSc Bioinformatics students doing BIO00070M", - "text": "MSc Bioinformatics students doing BIO00070M" - }, - { - "objectID": "core/week-1/workshop.html", - "href": "core/week-1/workshop.html", - "title": "Workshop - 🚧 still in Construction", - "section": "", - "text": "In this workshop you will" - }, - { - "objectID": "core/week-1/workshop.html#session-overview", - "href": "core/week-1/workshop.html#session-overview", - "title": "Workshop - 🚧 still in Construction", - "section": "", - "text": "In this workshop you will" - }, - { - "objectID": "core/week-1/workshop.html#project-oriented-workflow", - "href": "core/week-1/workshop.html#project-oriented-workflow", - "title": "Workshop - 🚧 still in Construction", - "section": "Project oriented workflow", - "text": "Project oriented workflow\n\nReadme\nfolders, files" - }, - { - "objectID": "core/week-1/workshop.html#naming-things", - "href": "core/week-1/workshop.html#naming-things", - "title": "Workshop - 🚧 still in Construction", - "section": "Naming things", - "text": "Naming things" - }, - { - "objectID": "core/week-1/workshop.html#file-formats", - "href": "core/week-1/workshop.html#file-formats", - "title": "Workshop - 🚧 still in Construction", - "section": "File formats", - "text": "File formats\nData files. - Sequences data - Image data - Structure data\nSimilarities and differences" - }, - { - "objectID": "core/week-1/workshop.html#tidy-data-reminder", - "href": "core/week-1/workshop.html#tidy-data-reminder", - "title": "Workshop - 🚧 still in Construction", - "section": "Tidy data reminder", - "text": "Tidy data reminder\nYou’re finished!" - }, - { - "objectID": "core/week-2/study_after_workshop.html", - "href": "core/week-2/study_after_workshop.html", - "title": "Independent Study to consolidate this week", - "section": "", - "text": "Set up\nIf you have just opened RStudio you will want to load the packages and import the data.\n\nlibrary(tidyverse)\nlibrary(readxl)\n\n\n💻 xx." - }, - { - "objectID": "core/week-2/workshop.html", - "href": "core/week-2/workshop.html", - "title": "Workshop", - "section": "", - "text": "In this workshop you will\n\n\n\n\n\n\nKey" - }, - { - "objectID": "core/week-2/workshop.html#session-overview", - "href": "core/week-2/workshop.html#session-overview", - "title": "Workshop", - "section": "", - "text": "In this workshop you will\n\n\n\n\n\n\nKey" - }, - { - "objectID": "about.html", - "href": "about.html", - "title": "About", - "section": "", - "text": "About this site" - }, - { - "objectID": "index.html", - "href": "index.html", - "title": "Data Analysis for the Group Research Project", - "section": "", - "text": "You are either\n\nan integrated masters student doing BIO00088H Group Research Project or\nan MSc Bioinformatics student doing BIO00070M Research, Professional and Team Skills\n\nFor students doing BIO00088H, Data Analysis compromises six workshops covering computational skills needed in your project. Three of these are core and taken by everyone and three are specific to your project type. MSc Bioinformatics students do the Core workshops and the ’omics workshops as part of BIO00070M.\nThe project types are:\n\n\n\n\n\n\n\nProject\nData Strand\n\n\n\n\nStem Cells, Jillian Barlow\n’omics, Emma Rand\n\n\nDevelopmental Biology, Betsy Pownal\n’omics, Emma Rand\n\n\nMicrobial Ecology, Kelly Redeker\n’omics, Emma Rand\n\n\nStructural Biochemistry, Michael Plevin\nmolecular-structure, Jon Agirre\n\n\nNeuroscience, Sean Sweeney\nimage-analysis, Richard Bingham\n\n\nxxxxxxxxxxxx, Richard Maguire\nimage-analysis, Richard Bingham\n\n\n\nThe data analysis workshops are:\n\n\n\nWeek\nData Strand\n\n\n\n\n1\nCore 1\n\n\n2\nCore 2\n\n\n3\nomics/structure/images 1\n\n\n4\nomics/structure/images 2\n\n\n5\nomics/structure/images 3\n\n\n6\nCore 3\n\n\n\n\n\nStudents who successfully complete this module will be able to\n\nuse appropriate computational techniques to reproducibly process, analyse and visualise data and generate scientific reports based on project work.\n\n\n\n\nAll material is on the VLE so why is this site useful? This site collects everything together in a searchable way. The search icon is on the top right." - }, - { - "objectID": "index.html#module-learning-outcome-linked-to-this-content", - "href": "index.html#module-learning-outcome-linked-to-this-content", - "title": "Data Analysis for the Group Research Project", - "section": "", - "text": "Students who successfully complete this module will be able to\n\nuse appropriate computational techniques to reproducibly process, analyse and visualise data and generate scientific reports based on project work." - }, - { - "objectID": "index.html#what-is-this-site-for", - "href": "index.html#what-is-this-site-for", - "title": "Data Analysis for the Group Research Project", - "section": "", - "text": "All material is on the VLE so why is this site useful? This site collects everything together in a searchable way. The search icon is on the top right." - }, - { - "objectID": "core/core.html", - "href": "core/core.html", - "title": "Core Data Analysis", + "objectID": "core/week-6/study_before_workshop.html", + "href": "core/week-6/study_before_workshop.html", + "title": "Independent Study to prepare for workshop", "section": "", - "text": "There are three workshops taken by everyone on BIO00088H and BIO00070M. These are in weeks 1, 2 and 6. The first two cover some useful workflow tips and how to organise your analyses effectively so they are reproducible but you will also have the chance to revise material from stage 1 and 2.\nGood organisation is important because you will want to be able to set work aside for holidays and assessment periods and then restart easily. You will also be assessed on the organisation, reproducibility and transparency of your work.\n\n\nThis week you will revise some essential concepts for scientific computing: file system organisation, file types, working directories and paths. The workshop will cover a rationale for working reproducibly, project oriented workflow, naming things and documenting your work. We will also examine some file types and the concept of tidy data." + "text": "📖 Read xxxx" }, { - "objectID": "core/core.html#week-1-core-1-organising-reproducible-data-analyses", - "href": "core/core.html#week-1-core-1-organising-reproducible-data-analyses", - "title": "Core Data Analysis", + "objectID": "core/week-6/overview.html", + "href": "core/week-6/overview.html", + "title": "Overview", "section": "", - "text": "This week you will revise some essential concepts for scientific computing: file system organisation, file types, working directories and paths. The workshop will cover a rationale for working reproducibly, project oriented workflow, naming things and documenting your work. We will also examine some file types and the concept of tidy data." + "text": "xxxxx\n\nLearning objectives\n\ndd\ndd.\ndd\nd\n\n\n\nInstructions\n\nPrepare\n\n📖 Read\n\nWorkshop\n\n💻 dd.\n💻 ddd\n💻 ddd\n\nConsolidate\n\n💻 dd\n💻 dd" }, { "objectID": "core/week-2/study_before_workshop.html", @@ -294,87 +392,24 @@ "text": "This week you will revise some essential concepts for scientific computing: file system organisation, file types, working directories and paths. The workshop will cover a rationale for working reproducibly, project oriented workflow, naming things and documenting your work. We will also examine some file types and the concept of tidy data.\n\nLearning objectives\nThe successful student will be able to:\n\nexplain the organisation of files and directories in a file systems including root, home and working directories\nexplain absolute and relative file paths\nexplain why working reproducibly is important\nknow how to use a project-oriented workflow to organise work\nbe able to give files human- and machine-readable names\noutline some common biological data file formats\n\n\n\nInstructions\n\nPrepare\n\n📖 Read Understanding file systems\n\nWorkshop\nConsolidate" }, { - "objectID": "core/week-6/study_before_workshop.html", - "href": "core/week-6/study_before_workshop.html", - "title": "Independent Study to prepare for workshop", - "section": "", - "text": "📖 Read xxxx" - }, - { - "objectID": "core/week-6/overview.html", - "href": "core/week-6/overview.html", - "title": "Overview", - "section": "", - "text": "xxxxx\n\nLearning objectives\n\ndd\ndd.\ndd\nd\n\n\n\nInstructions\n\nPrepare\n\n📖 Read\n\nWorkshop\n\n💻 dd.\n💻 ddd\n💻 ddd\n\nConsolidate\n\n💻 dd\n💻 dd" - }, - { - "objectID": "omics/week-3/workshop.html", - "href": "omics/week-3/workshop.html", - "title": "Workshop", + "objectID": "index.html", + "href": "index.html", + "title": "Data Analysis for the Group Research Project", "section": "", - "text": "In this workshop you will learn what steps to take to get a good understanding of your ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.\nYou should examine all three data sets because the comparisons will give you a stronger understanding of your own project data." + "text": "You are either\n\nan integrated masters student doing BIO00088H Group Research Project or\nan MSc Bioinformatics student doing BIO00070M Research, Professional and Team Skills\n\nFor students doing BIO00088H, Data Analysis compromises six workshops covering computational skills needed in your project. Three of these are core and taken by everyone and three are specific to your project type. MSc Bioinformatics students do the Core workshops and the ’omics workshops as part of BIO00070M.\nThe project types are:\n\n\n\n\n\n\n\nProject\nData Strand\n\n\n\n\nStem Cells, Jillian Barlow\n’omics, Emma Rand\n\n\nDevelopmental Biology, Betsy Pownal\n’omics, Emma Rand\n\n\nMicrobial Ecology, Kelly Redeker\n’omics, Emma Rand\n\n\nStructural Biochemistry, Michael Plevin\nmolecular-structure, Jon Agirre\n\n\nNeuroscience, Sean Sweeney\nimage-analysis, Richard Bingham\n\n\nxxxxxxxxxxxx, Richard Maguire\nimage-analysis, Richard Bingham\n\n\n\nThe data analysis workshops are:\n\n\n\nWeek\nData Strand\n\n\n\n\n1\nCore 1\n\n\n2\nCore 2\n\n\n3\nomics/structure/images 1\n\n\n4\nomics/structure/images 2\n\n\n5\nomics/structure/images 3\n\n\n6\nCore 3\n\n\n\n\n\nStudents who successfully complete this module will be able to\n\nuse appropriate computational techniques to reproducibly process, analyse and visualise data and generate scientific reports based on project work.\n\n\n\n\nAll material is on the VLE so why is this site useful? This site collects everything together in a searchable way. The search icon is on the top right." }, { - "objectID": "omics/week-3/workshop.html#session-overview", - "href": "omics/week-3/workshop.html#session-overview", - "title": "Workshop", + "objectID": "index.html#module-learning-outcome-linked-to-this-content", + "href": "index.html#module-learning-outcome-linked-to-this-content", + "title": "Data Analysis for the Group Research Project", "section": "", - "text": "In this workshop you will learn what steps to take to get a good understanding of your ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.\nYou should examine all three data sets because the comparisons will give you a stronger understanding of your own project data." - }, - { - "objectID": "omics/week-3/workshop.html#set-up-a-project", - "href": "omics/week-3/workshop.html#set-up-a-project", - "title": "Workshop", - "section": "Set up a Project", - "text": "Set up a Project\n🎬 Start RStudio from the Start menu\n🎬 Make an RStudio project. Be deliberate about where you create it so that it is a good place for you\n🎬 Use the Files pane to make new folders for the data. I suggest data-raw and data-processed\n🎬 Make a new script called workshop-1.R to carry out the rest of the work.\n🎬 Record what you do and what you find out. All of it!\n🎬 Load tidyverse (Wickham et al. 2019) for importing, summarising, plotting and filtering.\n\nlibrary(tidyverse)" - }, - { - "objectID": "omics/week-3/workshop.html#examine-the-data-in-a-spreadsheet", - "href": "omics/week-3/workshop.html#examine-the-data-in-a-spreadsheet", - "title": "Workshop", - "section": "Examine the data in a spreadsheet", - "text": "Examine the data in a spreadsheet\nThese are the three datasets. Each set compromises several files.\n🐸 Frog development data:\n\nxlaevis_counts_S14.csv\nxlaevis_counts_S20.csv\nxlaevis_counts_S30.csv\nmeta_data.txt\n\n🐭 Stem cell data:\n\nsurfaceome_hspc.csv\nsurfaceome_prog.csv\nsurfaceome_lthsc.csv\n\n🍂 xxxx data:\n\nxxx\nxxx\n\n🎬 Save the files to data-raw and open them in Excel\n🎬 Answer the following questions:\n\nDescribe how the sets of data are similar and how they are different.\nWhat is in the rows and columns of each file?\nHow many rows and columns are there in each file? Are these the same? In all cases or some cases? Why?\nGoogle an id. Where does your search take you? How much information is available?\n\n🎬 Did you record all that??" - }, - { - "objectID": "omics/week-3/workshop.html#import", - "href": "omics/week-3/workshop.html#import", - "title": "Workshop", - "section": "Import", - "text": "Import\nNow let’s get the data into R and visualise it.\n🎬 Import xlaevis_counts_S30.csv, surfaceome_hspc.csv and xxxxxxxx\n\n# 🐸 import the s30 data\ns30 <- read_csv(\"data-raw/xlaevis_counts_S30.csv\")\n\n\n# 🐭 import the hspc data\nhspc <- read_csv(\"data-raw/surfaceome_hspc.csv\")\n\n\n# 🍂 xxxx import the xxxx data\n# prog <- read_csv(\"\")\n\n🎬 Check these have the number of rows and column you were expecting and that column types and names are as expected." - }, - { - "objectID": "omics/week-3/workshop.html#explore", - "href": "omics/week-3/workshop.html#explore", - "title": "Workshop", - "section": "Explore", - "text": "Explore\nThe first task is to get an overview. We want to know\n\nare there any missing values? If so, how many and how are they distributed?\nhow may zeros are there and how are they distributed\ndoes it look as tough all the samples/cells were equally “successful”? Can we spot any problematic anomalies?\nwhat is the distribution of values?\n\nIf 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. That is replicates should be similar. We would also expect to see that the average expression of genes varies. We might have genes which are zero in every cell/sample. We will want to to filter those out.\nWe get this overview by looking at:\n\nThe distribution of values across the whole dataset\nThe distribution of values across the sample/cells (i.e., averaged across genes). This allows us to see variation between samples/cells:\nThe distribution of values across the genes (i.e., averaged across samples/cells). This allows us to see variation between genes.\n\n\nDistribution of values across the whole dataset\nIn all data sets, the values are spread over multiple columns so in order to plot the distribution as a whole, we will need to first use pivot_longer() to put the data in ‘tidy’ format (Wickham 2014) by stacking the columns. We could save a copy of the stacked data and then plot it, but here, I have just piped the stacked data straight into ggplot().\n\n🐸 Frogs\n🎬 Pivot the counts (stack the columns) so all the counts are in a single column (count) and pipe into ggplot() to create a histogram:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n ggplot(aes(x = count)) +\n geom_histogram()\n\n\n\n\nThis data is very skewed - there are so many low values that we can’t see the the tiny bars for the higher values. Logging the counts is a way to make the distribution more visible.\n🎬 Repeat the plot on log of the counts.\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n ggplot(aes(x = log10(count))) +\n geom_histogram()\n\n\n\n\nI’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 peak at zero suggests quite a few counts of 1. We would expect we would expect the distribution of counts to be roughly log normal because this is expression of all the genes in the genome1. That small peak near the low end suggests that these lower counts might be anomalies.\nThe 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 samples and genes.\n\n\n🐭 Mice\n🎬 Pivot the expression values (stack the columns) so all the counts are in a single column (expr) and pipe into ggplot() to create a histogram:\n\nhspc |>\n pivot_longer(cols = -ensembl_gene_id,\n names_to = \"cell\",\n values_to = \"expr\") |> \n ggplot(aes(x = expr)) +\n geom_histogram()\n\n\n\n\nThis is a very striking distribution. Is it what we are expecting? Again,the excess number of low values is almost certainly anomalous. They will be inaccurate measure and we will want to exclude expression values below (about) 1. We will revisit this after we have considered the distribution of expression across cells and genes.\nWhat about the bimodal appearance of the the ‘real’ values? If we had the whole genome we would not expect to see such a pattern - we’d expect to see a roughly normal distribution2. However, this is a subset of the genome and the nature of the subsetting has had an influence here. These are a subset of cell surface proteins that show a significant difference between at least two of twelve cell subtypes. That is, all of these genes are either high or low.\n\n\n\nDistribution of values across the sample/cells\n\n🐸 Frog samples\nSummary 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.\n🎬 Get a quick overview of the columns:\n\n# examine all the columns quickly\n# works well with smaller numbers of column\nsummary(s30)\n\n xenbase_gene_id S30_C_5 S30_C_6 S30_C_A \n Length:11893 Min. : 0.0 Min. : 0.0 Min. : 0.0 \n Class :character 1st Qu.: 14.0 1st Qu.: 14.0 1st Qu.: 23.0 \n Mode :character Median : 70.0 Median : 75.0 Median : 107.0 \n Mean : 317.1 Mean : 335.8 Mean : 426.3 \n 3rd Qu.: 205.0 3rd Qu.: 220.0 3rd Qu.: 301.0 \n Max. :101746.0 Max. :118708.0 Max. :117945.0 \n S30_F_5 S30_F_6 S30_F_A \n Min. : 0.0 Min. : 0.0 Min. : 0.0 \n 1st Qu.: 19.0 1st Qu.: 17.0 1st Qu.: 16.0 \n Median : 88.0 Median : 84.0 Median : 69.0 \n Mean : 376.2 Mean : 376.5 Mean : 260.4 \n 3rd Qu.: 251.0 3rd Qu.: 246.0 3rd Qu.: 187.0 \n Max. :117573.0 Max. :130672.0 Max. :61531.0 \n\n\nNotice 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.\nTo 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 0 in the S30_C_5 column\n🎬 Find the number of zeros in all six columns:\n\ns30 |>\n summarise(sum(S30_C_5 == 0),\n sum(S30_C_6 == 0),\n sum(S30_C_A == 0),\n sum(S30_F_5 == 0),\n sum(S30_F_6 == 0),\n sum(S30_F_A == 0))\n\n# A tibble: 1 × 6\n `sum(S30_C_5 == 0)` `sum(S30_C_6 == 0)` `sum(S30_C_A == 0)`\n <int> <int> <int>\n1 1340 1361 998\n# ℹ 3 more variables: `sum(S30_F_5 == 0)` <int>, `sum(S30_F_6 == 0)` <int>,\n# `sum(S30_F_A == 0)` <int>\n\n\nThere 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.\n🎬 Find the number of zeros in all columns:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n group_by(sample) |>\n summarise(n_zero = sum(count == 0))\n\n# A tibble: 6 × 2\n sample n_zero\n <chr> <int>\n1 S30_C_5 1340\n2 S30_C_6 1361\n3 S30_C_A 998\n4 S30_F_5 1210\n5 S30_F_6 1199\n6 S30_F_A 963\n\n\nYou could expand to get all the summary information\n🎬 Summarise all the samples:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n group_by(sample) |>\n summarise(min = min(count),\n lowerq = quantile(count, 0.25),\n mean = mean(count),\n median = median(count),\n upperq = quantile(count, 0.75),\n max = max(count),\n n_zero = sum(count == 0))\n\n# A tibble: 6 × 8\n sample min lowerq mean median upperq max n_zero\n <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>\n1 S30_C_5 0 14 317. 70 205 101746 1340\n2 S30_C_6 0 14 336. 75 220 118708 1361\n3 S30_C_A 0 23 426. 107 301 117945 998\n4 S30_F_5 0 19 376. 88 251 117573 1210\n5 S30_F_6 0 17 376. 84 246 130672 1199\n6 S30_F_A 0 16 260. 69 187 61531 963\n\n\nThe mean count ranges from 260 to 426.\nOne advantage this has over using summary() is that the output is a dataframe. For results, this is useful, and makes it easier to:\n\nwrite to file\nuse in ggplot()\nformat in a Quarto report\n\n🎬 Save the summary as a dataframe, s30_summary_samp.\nWe can write to file using write_csv()\n🎬 Write s30_summary_samp_by to a file called “s30_summary_samp_by.csv”:\n\nwrite_csv(s30_summary_samp_by, file = \"data-processed/s30_summary_samp_by.csv\")\n\nError in eval(expr, envir, enclos): object 's30_summary_samp_by' not found\n\n\nPlotting 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()\n🎬 Pivot the data and pipe into ggplot:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n ggplot(aes(count)) +\n geom_density() +\n facet_wrap(. ~ sample, nrow = 3)\n\n\n\n\nWe have many values (11893) so we are not limited to using geom_histogram(). geom_density() gives us a smooth distribution.\nWe 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.\n🎬 Repeat the graph but taking the base 10 log of the counts:\n\ns30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n ggplot(aes(log10(count))) +\n geom_density() +\n facet_wrap(. ~ sample, nrow = 3)\n\n\n\n\nThe key information to take from these plots is:\n\nthe distributions are roughly similar in width, height, location and overall shape so it doesn’t look as though we have any suspect samples\nthe peak at zero suggests quite a few counts of 1.\nsince we would expect the distribution of counts in each sample to be roughly log normal so that the small rise near the low end, even before the peak at zero, suggests that these lower counts might be anomalies.\n\nThe 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).\n\n\n🐭 Mouse cells\nWe used the summary() function to get an overview of the columns in the frog data. Let’s try that here.\n🎬 Get a quick overview of the columns:\n\nsummary(hspc)\n\n ensembl_gene_id HSPC_001 HSPC_002 HSPC_003 \n Length:280 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Mode :character Median : 0.000 Median : 0.000 Median : 0.9929 \n Mean : 2.143 Mean : 1.673 Mean : 2.5964 \n 3rd Qu.: 2.120 3rd Qu.: 2.239 3rd Qu.: 6.1559 \n Max. :12.567 Max. :11.976 Max. :11.1138 \n HSPC_004 HSPC_006 HSPC_008 HSPC_009 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 \n Median : 0.000 Median : 1.276 Median : 0.000 Median :0.000 \n Mean : 1.851 Mean : 2.338 Mean : 2.375 Mean :2.220 \n 3rd Qu.: 2.466 3rd Qu.: 3.536 3rd Qu.: 3.851 3rd Qu.:3.594 \n Max. :11.133 Max. :10.014 Max. :11.574 Max. :9.997 \n HSPC_011 HSPC_012 HSPC_014 HSPC_015 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.750 Median : 0.000 Median : 0.000 \n Mean : 2.285 Mean : 2.431 Mean : 2.295 Mean : 2.515 \n 3rd Qu.: 3.193 3rd Qu.: 3.741 3rd Qu.: 3.150 3rd Qu.: 3.789 \n Max. :11.260 Max. :10.905 Max. :11.051 Max. :10.751 \n HSPC_016 HSPC_017 HSPC_018 HSPC_020 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.9488 Median : 0.000 Median : 1.248 Median : 0.000 \n Mean : 2.6115 Mean : 2.146 Mean : 2.710 Mean : 2.509 \n 3rd Qu.: 5.9412 3rd Qu.: 2.357 3rd Qu.: 6.006 3rd Qu.: 4.470 \n Max. :11.3082 Max. :12.058 Max. :11.894 Max. :11.281 \n HSPC_021 HSPC_022 HSPC_023 HSPC_024 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.170 Mean : 2.287 Mean : 2.314 Mean : 2.195 \n 3rd Qu.: 2.996 3rd Qu.: 3.351 3rd Qu.: 2.749 3rd Qu.: 2.944 \n Max. :10.709 Max. :11.814 Max. :12.113 Max. :11.279 \n HSPC_025 HSPC_026 HSPC_027 HSPC_028 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.572 Median : 1.385 Median : 0.000 Median : 0.000 \n Mean : 2.710 Mean : 2.721 Mean : 2.458 Mean : 1.906 \n 3rd Qu.: 5.735 3rd Qu.: 6.392 3rd Qu.: 5.496 3rd Qu.: 2.037 \n Max. :11.309 Max. :10.865 Max. :11.266 Max. :10.777 \n HSPC_030 HSPC_031 HSPC_033 HSPC_034 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.119 Median : 0.9026 Median : 0.000 Median : 0.7984 \n Mean : 2.338 Mean : 2.3049 Mean : 1.938 Mean : 2.3220 \n 3rd Qu.: 3.005 3rd Qu.: 2.9919 3rd Qu.: 2.434 3rd Qu.: 4.8324 \n Max. :11.391 Max. :11.1748 Max. :10.808 Max. :10.6707 \n HSPC_035 HSPC_036 HSPC_037 HSPC_038 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8879 Median : 1.517 Median : 0.000 \n Mean : 1.810 Mean : 2.6918 Mean : 2.327 Mean : 2.212 \n 3rd Qu.: 2.175 3rd Qu.: 5.9822 3rd Qu.: 3.079 3rd Qu.: 2.867 \n Max. :11.221 Max. :11.3018 Max. :11.399 Max. :12.275 \n HSPC_040 HSPC_041 HSPC_042 HSPC_043 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.8673 Median : 1.342 \n Mean : 2.509 Mean : 2.492 Mean : 2.3673 Mean : 2.420 \n 3rd Qu.: 3.995 3rd Qu.: 3.943 3rd Qu.: 3.8371 3rd Qu.: 3.731 \n Max. :11.863 Max. :11.016 Max. :11.4852 Max. :11.123 \n HSPC_044 HSPC_045 HSPC_046 HSPC_047 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.8452 Median : 2.195 \n Mean : 2.382 Mean : 2.277 Mean : 1.9707 Mean : 2.498 \n 3rd Qu.: 3.998 3rd Qu.: 2.843 3rd Qu.: 2.0656 3rd Qu.: 3.937 \n Max. :10.782 Max. :10.629 Max. :11.0311 Max. :10.180 \n HSPC_048 HSPC_049 HSPC_050 HSPC_051 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.108 Median : 1.275 Median : 0.000 Median : 0.9757 \n Mean : 2.289 Mean : 2.453 Mean : 2.673 Mean : 2.2693 \n 3rd Qu.: 2.988 3rd Qu.: 3.819 3rd Qu.: 5.772 3rd Qu.: 3.1644 \n Max. :10.335 Max. :11.844 Max. :11.301 Max. :10.8692 \n HSPC_052 HSPC_053 HSPC_054 HSPC_055 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.509 Median : 0.818 Median : 0.000 Median : 0.000 \n Mean : 2.561 Mean : 2.684 Mean : 2.107 Mean : 1.959 \n 3rd Qu.: 4.644 3rd Qu.: 5.937 3rd Qu.: 2.568 3rd Qu.: 2.573 \n Max. :11.674 Max. :11.624 Max. :10.770 Max. :11.105 \n HSPC_056 HSPC_057 HSPC_058 HSPC_060 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.399 Median : 0.000 \n Mean : 2.295 Mean : 2.430 Mean : 2.296 Mean : 2.112 \n 3rd Qu.: 3.721 3rd Qu.: 3.806 3rd Qu.: 3.199 3rd Qu.: 2.201 \n Max. :11.627 Max. :10.575 Max. :11.134 Max. :10.631 \n HSPC_061 HSPC_062 HSPC_063 HSPC_064 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.515 Median : 1.101 \n Mean : 1.934 Mean : 2.129 Mean : 2.508 Mean : 2.696 \n 3rd Qu.: 2.489 3rd Qu.: 2.875 3rd Qu.: 4.895 3rd Qu.: 6.412 \n Max. :11.190 Max. :10.433 Max. :10.994 Max. :10.873 \n HSPC_065 HSPC_066 HSPC_067 HSPC_068 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.4852 Median : 0.000 Median : 1.441 Median : 0.000 \n Mean : 2.2676 Mean : 2.136 Mean : 2.480 Mean : 2.449 \n 3rd Qu.: 3.8217 3rd Qu.: 2.632 3rd Qu.: 3.548 3rd Qu.: 4.517 \n Max. :10.9023 Max. :11.608 Max. :11.147 Max. :10.901 \n HSPC_069 HSPC_070 HSPC_071 HSPC_072 \n Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8949 Median : 0.9272 Median : 1.121 \n Mean : 2.406 Mean : 2.5826 Mean : 2.2844 Mean : 2.545 \n 3rd Qu.: 4.705 3rd Qu.: 5.4749 3rd Qu.: 3.2531 3rd Qu.: 4.939 \n Max. :11.258 Max. :11.6715 Max. :10.7886 Max. :11.397 \n HSPC_073 HSPC_074 HSPC_075 HSPC_076 \n Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.00 Median : 1.674 Median : 0.000 \n Mean : 2.491 Mean : 2.46 Mean : 2.413 Mean : 2.289 \n 3rd Qu.: 4.134 3rd Qu.: 3.40 3rd Qu.: 3.013 3rd Qu.: 2.550 \n Max. :11.844 Max. :11.66 Max. :11.976 Max. :12.136 \n HSPC_077 HSPC_078 HSPC_079 HSPC_080 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.6624 Median : 1.492 Median : 1.384 Median : 1.013 \n Mean : 2.4336 Mean : 2.637 Mean : 2.432 Mean : 2.881 \n 3rd Qu.: 5.4937 3rd Qu.: 5.472 3rd Qu.: 3.617 3rd Qu.: 7.220 \n Max. :11.6020 Max. :10.673 Max. :11.199 Max. :11.836 \n HSPC_081 HSPC_082 HSPC_083 HSPC_084 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.7671 Median : 0.000 Median : 1.896 Median : 1.128 \n Mean : 1.9227 Mean : 2.474 Mean : 2.864 Mean : 2.289 \n 3rd Qu.: 1.6349 3rd Qu.: 3.488 3rd Qu.: 5.101 3rd Qu.: 2.792 \n Max. :11.4681 Max. :11.962 Max. :10.865 Max. :11.834 \n HSPC_085 HSPC_087 HSPC_088 HSPC_089 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.157 Mean : 2.314 Mean : 2.202 Mean : 2.329 \n 3rd Qu.: 3.010 3rd Qu.: 3.245 3rd Qu.: 2.092 3rd Qu.: 3.246 \n Max. :10.809 Max. :10.976 Max. :11.362 Max. :11.301 \n HSPC_090 HSPC_094 HSPC_095 HSPC_096 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 \n Median : 0.000 Median : 0.000 Median : 2.055 Median :0.000 \n Mean : 2.286 Mean : 2.186 Mean : 2.756 Mean :2.348 \n 3rd Qu.: 4.174 3rd Qu.: 2.002 3rd Qu.: 4.370 3rd Qu.:4.482 \n Max. :11.124 Max. :11.694 Max. :11.385 Max. :9.601 \n HSPC_098 HSPC_099 HSPC_100 HSPC_101 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.007 \n Mean : 2.209 Mean : 2.082 Mean : 2.313 Mean : 2.587 \n 3rd Qu.: 3.354 3rd Qu.: 2.505 3rd Qu.: 2.775 3rd Qu.: 5.334 \n Max. :11.070 Max. :10.200 Max. :11.452 Max. :11.456 \n HSPC_102 HSPC_103 HSPC_104 HSPC_105 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.111 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.210 Mean : 2.853 Mean : 2.099 Mean : 1.893 \n 3rd Qu.: 2.993 3rd Qu.: 6.123 3rd Qu.: 2.720 3rd Qu.: 2.129 \n Max. :11.153 Max. :11.328 Max. :10.746 Max. :10.721 \n HSPC_106 HSPC_107 HSPC_108 HSPC_109 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.595 \n Mean : 1.980 Mean : 2.279 Mean : 2.296 Mean : 2.420 \n 3rd Qu.: 2.425 3rd Qu.: 3.396 3rd Qu.: 3.361 3rd Qu.: 4.006 \n Max. :10.919 Max. :10.982 Max. :11.744 Max. :10.463 \n HSPC_110 HSPC_111 HSPC_114 HSPC_115 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.9173 Median : 2.349 \n Mean : 2.159 Mean : 1.800 Mean : 1.8376 Mean : 2.943 \n 3rd Qu.: 2.667 3rd Qu.: 2.214 3rd Qu.: 1.8741 3rd Qu.: 6.223 \n Max. :11.121 Max. :11.109 Max. :10.4645 Max. :11.124 \n HSPC_117 HSPC_118 HSPC_119 HSPC_120 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.187 \n Mean : 1.919 Mean : 1.855 Mean : 2.289 Mean : 2.041 \n 3rd Qu.: 2.306 3rd Qu.: 2.387 3rd Qu.: 3.292 3rd Qu.: 2.610 \n Max. :14.579 Max. :11.119 Max. :12.534 Max. :11.438 \n HSPC_121 HSPC_122 HSPC_123 HSPC_125 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.803 Mean : 2.072 Mean : 2.200 Mean : 2.116 \n 3rd Qu.: 5.798 3rd Qu.: 2.140 3rd Qu.: 3.215 3rd Qu.: 2.409 \n Max. :11.320 Max. :11.013 Max. :11.163 Max. :11.368 \n HSPC_126 HSPC_127 HSPC_130 HSPC_131 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.9381 Median : 1.147 Median : 0.000 Median : 0.000 \n Mean : 2.0014 Mean : 2.287 Mean : 2.551 Mean : 2.240 \n 3rd Qu.: 2.2215 3rd Qu.: 3.051 3rd Qu.: 3.968 3rd Qu.: 3.773 \n Max. :10.9622 Max. :11.028 Max. :10.585 Max. :11.216 \n HSPC_132 HSPC_133 HSPC_134 HSPC_135 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.4438 Median : 2.234 Median : 0.000 Median : 0.000 \n Mean : 2.1659 Mean : 2.582 Mean : 2.335 Mean : 2.402 \n 3rd Qu.: 1.8512 3rd Qu.: 4.591 3rd Qu.: 3.659 3rd Qu.: 4.134 \n Max. :10.6431 Max. :10.730 Max. :11.995 Max. :11.573 \n HSPC_136 HSPC_138 HSPC_139 HSPC_140 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.7062 Median : 2.078 Median : 0.000 \n Mean : 2.546 Mean : 2.1054 Mean : 2.876 Mean : 2.220 \n 3rd Qu.: 5.219 3rd Qu.: 1.8181 3rd Qu.: 4.604 3rd Qu.: 3.716 \n Max. :11.281 Max. :11.1177 Max. :11.013 Max. :10.893 \n HSPC_141 HSPC_142 HSPC_143 HSPC_144 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.075 \n Mean : 2.385 Mean : 2.232 Mean : 2.592 Mean : 2.004 \n 3rd Qu.: 4.149 3rd Qu.: 2.523 3rd Qu.: 4.248 3rd Qu.: 2.441 \n Max. :11.099 Max. :11.902 Max. :12.932 Max. :11.121 \n HSPC_146 HSPC_148 HSPC_149 HSPC_151 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.9711 \n Mean : 2.418 Mean : 2.385 Mean : 2.314 Mean : 2.4375 \n 3rd Qu.: 4.430 3rd Qu.: 3.288 3rd Qu.: 3.139 3rd Qu.: 3.2523 \n Max. :10.385 Max. :12.823 Max. :10.910 Max. :11.7148 \n HSPC_152 HSPC_153 HSPC_154 HSPC_155 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.247 Mean : 2.415 Mean : 2.476 Mean : 2.468 \n 3rd Qu.: 3.293 3rd Qu.: 3.524 3rd Qu.: 4.653 3rd Qu.: 3.621 \n Max. :12.463 Max. :12.205 Max. :11.437 Max. :11.207 \n HSPC_156 HSPC_157 HSPC_158 HSPC_159 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.5545 Median : 1.993 Median : 0.000 Median : 0.000 \n Mean : 2.2297 Mean : 2.493 Mean : 2.119 Mean : 2.461 \n 3rd Qu.: 2.0977 3rd Qu.: 3.692 3rd Qu.: 2.930 3rd Qu.: 3.340 \n Max. :11.2431 Max. :10.539 Max. :11.336 Max. :11.123 \n HSPC_161 HSPC_162 HSPC_164 HSPC_165 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.701 Median : 0.7152 Median : 0.000 Median : 0.000 \n Mean : 2.533 Mean : 2.3473 Mean : 2.161 Mean : 2.084 \n 3rd Qu.: 3.616 3rd Qu.: 2.4973 3rd Qu.: 2.553 3rd Qu.: 3.020 \n Max. :11.429 Max. :11.0065 Max. :11.865 Max. :10.282 \n HSPC_166 HSPC_168 HSPC_169 HSPC_170 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.002 Median : 1.158 Median : 0.000 \n Mean : 2.177 Mean : 2.390 Mean : 2.038 Mean : 2.401 \n 3rd Qu.: 3.296 3rd Qu.: 4.701 3rd Qu.: 2.232 3rd Qu.: 3.703 \n Max. :11.427 Max. :10.393 Max. :10.447 Max. :11.288 \n HSPC_171 HSPC_172 HSPC_173 HSPC_174 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.525 Median : 0.7679 Median : 0.000 Median : 1.257 \n Mean : 2.312 Mean : 2.3115 Mean : 2.288 Mean : 2.444 \n 3rd Qu.: 2.729 3rd Qu.: 3.7889 3rd Qu.: 3.037 3rd Qu.: 4.996 \n Max. :10.468 Max. :11.1442 Max. :11.074 Max. :11.095 \n HSPC_175 HSPC_176 HSPC_177 HSPC_178 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.496 Median : 2.024 Median : 1.971 Median : 1.003 \n Mean : 2.613 Mean : 2.593 Mean : 2.421 Mean : 2.277 \n 3rd Qu.: 4.845 3rd Qu.: 4.092 3rd Qu.: 3.665 3rd Qu.: 2.812 \n Max. :11.235 Max. :10.379 Max. :10.864 Max. :10.979 \n HSPC_179 HSPC_180 HSPC_181 HSPC_182 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.836 Median : 1.544 Median : 2.030 Median : 0.000 \n Mean : 2.205 Mean : 2.556 Mean : 2.890 Mean : 2.363 \n 3rd Qu.: 2.300 3rd Qu.: 4.798 3rd Qu.: 4.846 3rd Qu.: 3.779 \n Max. :11.244 Max. :10.802 Max. :10.945 Max. :10.399 \n HSPC_183 HSPC_185 HSPC_186 HSPC_187 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.020 Median : 0.000 Median : 1.606 Median : 0.000 \n Mean : 2.242 Mean : 2.708 Mean : 2.053 Mean : 2.360 \n 3rd Qu.: 2.842 3rd Qu.: 4.855 3rd Qu.: 2.834 3rd Qu.: 3.541 \n Max. :10.530 Max. :11.079 Max. :11.016 Max. :10.923 \n HSPC_189 HSPC_190 HSPC_191 HSPC_192 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.412 \n Mean : 2.120 Mean : 2.417 Mean : 2.175 Mean : 2.192 \n 3rd Qu.: 2.652 3rd Qu.: 5.226 3rd Qu.: 2.574 3rd Qu.: 2.669 \n Max. :11.300 Max. :11.023 Max. :11.454 Max. :10.225 \n HSPC_193 HSPC_195 HSPC_196 HSPC_198 \n Min. :0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.:0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median :0.9691 Median : 0.9175 Median : 1.379 Median : 1.105 \n Mean :2.5448 Mean : 2.7307 Mean : 2.327 Mean : 2.155 \n 3rd Qu.:5.1191 3rd Qu.: 5.8899 3rd Qu.: 2.625 3rd Qu.: 2.756 \n Max. :9.8728 Max. :10.4757 Max. :11.319 Max. :11.405 \n HSPC_199 HSPC_200 HSPC_202 HSPC_203 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 1.069 Median : 1.572 Median : 0.8045 Median : 1.311 \n Mean : 1.909 Mean : 2.346 Mean : 2.1384 Mean : 2.058 \n 3rd Qu.: 2.431 3rd Qu.: 2.791 3rd Qu.: 2.0569 3rd Qu.: 2.792 \n Max. :11.377 Max. :11.334 Max. :11.0516 Max. :10.852 \n HSPC_204 HSPC_205 HSPC_206 HSPC_207 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.342 Median : 1.997 Median : 1.076 Median : 0.9235 \n Mean : 2.716 Mean : 2.520 Mean : 2.426 Mean : 2.2974 \n 3rd Qu.: 5.611 3rd Qu.: 4.244 3rd Qu.: 4.057 3rd Qu.: 2.6736 \n Max. :10.269 Max. :10.817 Max. :11.866 Max. :11.4287 \n HSPC_208 HSPC_210 HSPC_211 HSPC_212 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 2.263 Median : 1.021 Median : 1.351 Median : 0.000 \n Mean : 2.893 Mean : 2.315 Mean : 2.425 Mean : 2.336 \n 3rd Qu.: 5.014 3rd Qu.: 2.676 3rd Qu.: 3.820 3rd Qu.: 3.443 \n Max. :11.375 Max. :12.208 Max. :11.360 Max. :11.808 \n HSPC_213 HSPC_214 HSPC_215 HSPC_216 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.270 Median : 0.9195 Median : 1.653 Median : 0.8022 \n Mean : 2.483 Mean : 2.1976 Mean : 2.563 Mean : 2.6010 \n 3rd Qu.: 4.903 3rd Qu.: 2.7139 3rd Qu.: 4.344 3rd Qu.: 6.0076 \n Max. :11.548 Max. :10.6947 Max. :10.933 Max. :11.2119 \n HSPC_218 HSPC_219 HSPC_220 HSPC_221 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.027 Median : 0.000 Median : 1.269 \n Mean : 2.467 Mean : 2.291 Mean : 2.449 Mean : 2.641 \n 3rd Qu.: 3.980 3rd Qu.: 2.853 3rd Qu.: 4.486 3rd Qu.: 3.617 \n Max. :11.654 Max. :10.801 Max. :10.410 Max. :11.651 \n HSPC_222 HSPC_223 HSPC_224 HSPC_225 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.449 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.262 Mean : 2.271 Mean : 2.492 Mean : 2.585 \n 3rd Qu.: 3.271 3rd Qu.: 3.727 3rd Qu.: 3.769 3rd Qu.: 5.253 \n Max. :11.133 Max. :12.000 Max. :11.114 Max. :11.671 \n HSPC_227 HSPC_228 HSPC_229 HSPC_230 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 2.484 Median : 0.000 \n Mean : 2.492 Mean : 2.370 Mean : 2.742 Mean : 2.586 \n 3rd Qu.: 3.692 3rd Qu.: 4.488 3rd Qu.: 4.836 3rd Qu.: 5.188 \n Max. :10.815 Max. :10.165 Max. :11.143 Max. :10.734 \n HSPC_231 HSPC_232 HSPC_233 HSPC_235 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.869 Median : 1.254 Median : 0.000 \n Mean : 2.379 Mean : 2.264 Mean : 2.531 Mean : 2.552 \n 3rd Qu.: 4.787 3rd Qu.: 3.163 3rd Qu.: 3.925 3rd Qu.: 4.389 \n Max. :10.790 Max. :12.098 Max. :11.533 Max. :11.765 \n HSPC_236 HSPC_237 HSPC_239 HSPC_240 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 2.207 Median : 0.892 \n Mean : 2.205 Mean : 2.457 Mean : 2.656 Mean : 2.049 \n 3rd Qu.: 3.748 3rd Qu.: 3.488 3rd Qu.: 4.904 3rd Qu.: 2.617 \n Max. :10.234 Max. :10.630 Max. :10.858 Max. :10.528 \n HSPC_243 HSPC_244 HSPC_245 HSPC_246 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.118 Median : 0.7872 Median : 1.459 Median : 1.629 \n Mean : 2.311 Mean : 2.6638 Mean : 2.360 Mean : 2.321 \n 3rd Qu.: 2.574 3rd Qu.: 6.2395 3rd Qu.: 3.000 3rd Qu.: 3.229 \n Max. :11.069 Max. :10.0730 Max. :11.297 Max. :11.237 \n HSPC_247 HSPC_248 HSPC_249 HSPC_250 \n Min. :0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.:0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median :0.000 Median : 0.8453 Median : 0.000 Median : 1.278 \n Mean :2.537 Mean : 2.3719 Mean : 1.803 Mean : 2.751 \n 3rd Qu.:4.687 3rd Qu.: 3.3090 3rd Qu.: 2.335 3rd Qu.: 6.330 \n Max. :9.821 Max. :10.8128 Max. :10.568 Max. :11.256 \n HSPC_251 HSPC_253 HSPC_254 HSPC_255 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.9714 Median : 1.265 Median : 0.000 Median : 0.9098 \n Mean : 2.5626 Mean : 2.492 Mean : 2.177 Mean : 2.1878 \n 3rd Qu.: 4.9167 3rd Qu.: 4.185 3rd Qu.: 3.437 3rd Qu.: 2.4313 \n Max. :11.1252 Max. :10.435 Max. :10.422 Max. :10.7952 \n HSPC_256 HSPC_257 HSPC_258 HSPC_261 \n Min. : 0.0000 Min. : 0.000 Min. :0.0000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 0.0000 \n Median : 0.8248 Median : 1.241 Median :0.8526 Median : 0.5387 \n Mean : 2.1051 Mean : 2.630 Mean :2.0295 Mean : 2.1419 \n 3rd Qu.: 2.3331 3rd Qu.: 5.646 3rd Qu.:3.0784 3rd Qu.: 1.9352 \n Max. :13.0375 Max. :11.499 Max. :9.9116 Max. :11.3247 \n HSPC_263 HSPC_264 HSPC_265 HSPC_266 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.538 Median : 1.426 Median : 1.883 Median : 1.839 \n Mean : 2.613 Mean : 2.374 Mean : 3.177 Mean : 2.833 \n 3rd Qu.: 4.485 3rd Qu.: 3.238 3rd Qu.: 5.702 3rd Qu.: 5.801 \n Max. :10.571 Max. :11.136 Max. :12.436 Max. :10.338 \n HSPC_267 HSPC_268 HSPC_269 HSPC_270 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.9675 Median : 0.7787 Median : 0.8632 Median : 0.9637 \n Mean : 2.4910 Mean : 2.5342 Mean : 2.4029 Mean : 2.6899 \n 3rd Qu.: 3.5345 3rd Qu.: 4.9871 3rd Qu.: 4.3176 3rd Qu.: 5.7266 \n Max. :10.0139 Max. :10.7848 Max. :11.2689 Max. :11.1648 \n HSPC_271 HSPC_274 HSPC_275 HSPC_276 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 1.352 Median : 1.730 Median : 0.5252 Median : 1.156 \n Mean : 2.493 Mean : 2.382 Mean : 2.5375 Mean : 2.485 \n 3rd Qu.: 4.430 3rd Qu.: 3.360 3rd Qu.: 5.7329 3rd Qu.: 4.623 \n Max. :11.636 Max. :11.165 Max. :11.6234 Max. :11.562 \n HSPC_278 HSPC_279 HSPC_280 HSPC_281 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.487 Median : 1.608 Median : 2.611 \n Mean : 2.161 Mean : 2.497 Mean : 2.580 Mean : 2.737 \n 3rd Qu.: 2.270 3rd Qu.: 3.813 3rd Qu.: 3.985 3rd Qu.: 4.731 \n Max. :11.734 Max. :10.900 Max. :11.673 Max. :10.076 \n HSPC_282 HSPC_283 HSPC_285 HSPC_286 \n Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.7021 Median : 1.911 Median : 0.8658 Median : 1.178 \n Mean : 2.4272 Mean : 2.534 Mean : 2.4868 Mean : 2.293 \n 3rd Qu.: 4.1254 3rd Qu.: 3.888 3rd Qu.: 5.3804 3rd Qu.: 2.597 \n Max. :11.1094 Max. :10.258 Max. :10.5533 Max. :11.112 \n HSPC_287 HSPC_288 HSPC_289 HSPC_290 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.049 Median : 0.8548 Median : 1.953 Median : 1.176 \n Mean : 2.775 Mean : 2.6412 Mean : 2.925 Mean : 2.304 \n 3rd Qu.: 5.476 3rd Qu.: 5.4204 3rd Qu.: 5.613 3rd Qu.: 3.445 \n Max. :10.925 Max. :11.0814 Max. :10.199 Max. :11.094 \n HSPC_291 HSPC_292 HSPC_293 HSPC_294 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.176 Median : 1.320 Median : 1.077 Median : 0.9161 \n Mean : 2.662 Mean : 2.534 Mean : 2.538 Mean : 2.4365 \n 3rd Qu.: 5.690 3rd Qu.: 4.297 3rd Qu.: 3.458 3rd Qu.: 4.8204 \n Max. :12.255 Max. :11.090 Max. :10.987 Max. :10.6135 \n HSPC_295 HSPC_296 HSPC_297 HSPC_298 \n Min. :0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median :1.479 Median : 2.157 Median : 2.444 Median : 1.281 \n Mean :2.849 Mean : 2.977 Mean : 3.062 Mean : 2.277 \n 3rd Qu.:5.282 3rd Qu.: 5.006 3rd Qu.: 5.005 3rd Qu.: 2.749 \n Max. :9.986 Max. :10.830 Max. :11.009 Max. :10.636 \n HSPC_299 HSPC_300 HSPC_301 HSPC_302 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.716 Median : 1.163 Median : 2.235 Median : 2.240 \n Mean : 2.597 Mean : 2.346 Mean : 2.739 Mean : 2.890 \n 3rd Qu.: 3.762 3rd Qu.: 2.876 3rd Qu.: 4.593 3rd Qu.: 4.945 \n Max. :11.663 Max. :11.690 Max. :10.364 Max. :10.498 \n HSPC_303 HSPC_304 HSPC_305 HSPC_306 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.8348 Median : 0.9727 Median : 1.152 Median : 1.303 \n Mean : 2.3400 Mean : 2.3710 Mean : 2.469 Mean : 2.496 \n 3rd Qu.: 3.2942 3rd Qu.: 2.9942 3rd Qu.: 3.300 3rd Qu.: 3.015 \n Max. :10.3022 Max. :11.7185 Max. :11.051 Max. :11.211 \n HSPC_307 HSPC_308 HSPC_309 HSPC_310 \n Min. :0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median :1.976 Median : 1.634 Median : 1.804 Median : 1.743 \n Mean :2.873 Mean : 2.812 Mean : 2.892 Mean : 2.874 \n 3rd Qu.:5.396 3rd Qu.: 5.089 3rd Qu.: 5.165 3rd Qu.: 5.004 \n Max. :9.921 Max. :10.527 Max. :10.476 Max. :11.107 \n HSPC_312 HSPC_313 HSPC_314 HSPC_315 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.420 Median : 1.592 Median : 1.635 Median : 2.262 \n Mean : 2.645 Mean : 2.637 Mean : 2.564 Mean : 2.628 \n 3rd Qu.: 4.925 3rd Qu.: 4.257 3rd Qu.: 4.297 3rd Qu.: 4.092 \n Max. :11.367 Max. :10.644 Max. :10.882 Max. :12.140 \n HSPC_317 HSPC_318 HSPC_320 HSPC_321 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 2.335 Median : 1.728 Median : 2.340 Median : 1.835 \n Mean : 2.648 Mean : 2.637 Mean : 3.064 Mean : 2.742 \n 3rd Qu.: 4.103 3rd Qu.: 4.483 3rd Qu.: 5.325 3rd Qu.: 4.340 \n Max. :10.933 Max. :11.712 Max. :11.589 Max. :11.695 \n HSPC_322 HSPC_323 HSPC_324 HSPC_325 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.9842 Median : 0.989 Median : 1.088 Median : 2.132 \n Mean : 2.5948 Mean : 2.905 Mean : 2.655 Mean : 3.091 \n 3rd Qu.: 3.4619 3rd Qu.: 5.629 3rd Qu.: 3.772 3rd Qu.: 5.191 \n Max. :11.9594 Max. :12.267 Max. :11.310 Max. :11.134 \n HSPC_326 HSPC_327 HSPC_328 HSPC_329 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.781 Median : 1.085 Median : 1.936 Median : 1.954 \n Mean : 3.021 Mean : 2.838 Mean : 2.582 Mean : 3.034 \n 3rd Qu.: 5.582 3rd Qu.: 6.388 3rd Qu.: 4.048 3rd Qu.: 5.497 \n Max. :11.268 Max. :11.433 Max. :11.908 Max. :10.927 \n HSPC_330 HSPC_331 HSPC_332 HSPC_333 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.870 Median : 2.953 Median : 1.644 Median : 1.320 \n Mean : 2.791 Mean : 3.058 Mean : 2.768 Mean : 2.428 \n 3rd Qu.: 4.409 3rd Qu.: 5.118 3rd Qu.: 5.141 3rd Qu.: 2.985 \n Max. :11.561 Max. :10.855 Max. :10.420 Max. :11.946 \n HSPC_334 HSPC_335 HSPC_336 HSPC_337 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.931 Median : 1.541 Median : 2.761 Median : 0.000 \n Mean : 2.894 Mean : 2.746 Mean : 3.051 Mean : 2.415 \n 3rd Qu.: 4.160 3rd Qu.: 4.461 3rd Qu.: 4.408 3rd Qu.: 4.188 \n Max. :11.592 Max. :11.076 Max. :11.246 Max. :10.205 \n HSPC_338 HSPC_339 HSPC_341 HSPC_342 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.9553 Median : 0.4452 \n Mean : 2.205 Mean : 2.325 Mean : 2.0823 Mean : 2.4572 \n 3rd Qu.: 2.449 3rd Qu.: 3.136 3rd Qu.: 2.0118 3rd Qu.: 4.9582 \n Max. :12.052 Max. :11.858 Max. :11.3855 Max. :11.8066 \n HSPC_343 HSPC_344 HSPC_345 HSPC_346 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.5197 \n Mean : 2.363 Mean : 2.290 Mean : 1.984 Mean : 2.5126 \n 3rd Qu.: 4.285 3rd Qu.: 3.238 3rd Qu.: 2.561 3rd Qu.: 5.2033 \n Max. :11.422 Max. :11.877 Max. :10.939 Max. :11.1527 \n HSPC_348 HSPC_349 HSPC_350 HSPC_351 \n Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 \n Median : 1.113 Median : 0.000 Median : 0.00 Median : 0.000 \n Mean : 2.232 Mean : 1.949 Mean : 2.11 Mean : 2.259 \n 3rd Qu.: 2.875 3rd Qu.: 2.784 3rd Qu.: 3.07 3rd Qu.: 3.214 \n Max. :11.161 Max. :10.720 Max. :11.15 Max. :10.912 \n HSPC_352 HSPC_353 HSPC_354 HSPC_356 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.333 Mean : 2.162 Mean : 2.427 Mean : 2.135 \n 3rd Qu.: 3.197 3rd Qu.: 2.819 3rd Qu.: 3.808 3rd Qu.: 2.709 \n Max. :12.275 Max. :11.351 Max. :11.190 Max. :10.662 \n HSPC_358 HSPC_359 HSPC_360 HSPC_361 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.278 Mean : 2.012 Mean : 2.381 Mean : 2.137 \n 3rd Qu.: 3.608 3rd Qu.: 1.460 3rd Qu.: 3.044 3rd Qu.: 2.875 \n Max. :10.924 Max. :11.678 Max. :11.203 Max. :10.847 \n HSPC_362 HSPC_363 HSPC_365 HSPC_367 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 1.783 Mean : 1.987 Mean : 2.937 Mean : 2.449 \n 3rd Qu.: 1.594 3rd Qu.: 2.750 3rd Qu.: 5.572 3rd Qu.: 3.936 \n Max. :11.889 Max. :10.389 Max. :12.427 Max. :11.081 \n HSPC_368 HSPC_370 HSPC_371 HSPC_372 \n Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.7971 Median : 0.7613 Median : 0.000 \n Mean : 1.877 Mean : 2.7681 Mean : 2.4278 Mean : 2.487 \n 3rd Qu.: 2.018 3rd Qu.: 6.5358 3rd Qu.: 4.9578 3rd Qu.: 4.226 \n Max. :11.523 Max. :11.9636 Max. :11.4223 Max. :11.700 \n HSPC_373 HSPC_374 HSPC_376 HSPC_377 \n Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.00 Median : 0.000 Median : 0.000 \n Mean : 2.330 Mean : 2.21 Mean : 2.625 Mean : 2.456 \n 3rd Qu.: 3.784 3rd Qu.: 2.44 3rd Qu.: 4.365 3rd Qu.: 4.875 \n Max. :11.672 Max. :12.04 Max. :12.011 Max. :11.282 \n HSPC_380 HSPC_382 HSPC_383 HSPC_386 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.9728 Median : 1.753 Median : 0.000 \n Mean : 2.291 Mean : 2.3318 Mean : 2.307 Mean : 2.351 \n 3rd Qu.: 2.403 3rd Qu.: 2.7605 3rd Qu.: 3.113 3rd Qu.: 3.704 \n Max. :11.415 Max. :11.3370 Max. :11.592 Max. :11.079 \n HSPC_387 HSPC_388 HSPC_389 HSPC_390 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.9037 Median : 0.000 Median : 0.000 \n Mean : 2.255 Mean : 2.4969 Mean : 2.081 Mean : 2.131 \n 3rd Qu.: 3.151 3rd Qu.: 5.3587 3rd Qu.: 2.723 3rd Qu.: 2.738 \n Max. :11.700 Max. :10.9923 Max. :11.868 Max. :10.913 \n HSPC_391 HSPC_392 HSPC_393 HSPC_395 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.026 Mean : 2.356 Mean : 2.063 Mean : 1.779 \n 3rd Qu.: 2.126 3rd Qu.: 3.781 3rd Qu.: 2.163 3rd Qu.: 1.924 \n Max. :12.021 Max. :11.370 Max. :10.530 Max. :12.219 \n HSPC_396 HSPC_398 HSPC_399 HSPC_400 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.164 Mean : 2.309 Mean : 1.831 Mean : 2.091 \n 3rd Qu.: 2.681 3rd Qu.: 3.994 3rd Qu.: 1.844 3rd Qu.: 2.781 \n Max. :11.292 Max. :11.431 Max. :11.343 Max. :10.863 \n HSPC_402 HSPC_403 HSPC_404 HSPC_405 \n Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.00 Median : 0.000 Median : 0.5496 \n Mean : 2.343 Mean : 2.06 Mean : 1.878 Mean : 2.3660 \n 3rd Qu.: 4.552 3rd Qu.: 2.45 3rd Qu.: 1.644 3rd Qu.: 2.5449 \n Max. :11.444 Max. :12.00 Max. :11.188 Max. :12.2605 \n HSPC_406 HSPC_407 HSPC_408 HSPC_409 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.565 Median : 0.5775 Median : 0.000 \n Mean : 2.169 Mean : 2.611 Mean : 1.9174 Mean : 2.234 \n 3rd Qu.: 2.606 3rd Qu.: 6.000 3rd Qu.: 1.3086 3rd Qu.: 3.044 \n Max. :10.866 Max. :11.296 Max. :12.8185 Max. :11.595 \n HSPC_410 HSPC_411 HSPC_412 HSPC_413 \n Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.9059 Median : 0.6614 Median : 0.000 \n Mean : 2.308 Mean : 3.1194 Mean : 3.0437 Mean : 2.433 \n 3rd Qu.: 4.022 3rd Qu.: 7.7574 3rd Qu.: 7.4695 3rd Qu.: 3.329 \n Max. :11.620 Max. :12.0858 Max. :11.5582 Max. :12.549 \n HSPC_415 HSPC_416 HSPC_417 HSPC_418 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.7222 Median : 0.000 \n Mean : 2.904 Mean : 2.228 Mean : 2.4242 Mean : 2.508 \n 3rd Qu.: 5.531 3rd Qu.: 3.111 3rd Qu.: 3.0795 3rd Qu.: 3.249 \n Max. :12.359 Max. :11.338 Max. :12.0314 Max. :11.857 \n HSPC_419 HSPC_420 HSPC_421 HSPC_422 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.6924 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.6246 Mean : 2.514 Mean : 2.075 Mean : 2.552 \n 3rd Qu.: 4.8156 3rd Qu.: 5.709 3rd Qu.: 3.682 3rd Qu.: 5.382 \n Max. :12.0526 Max. :11.270 Max. :10.250 Max. :11.691 \n HSPC_423 HSPC_424 HSPC_425 HSPC_426 \n Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.00 Median : 0.000 Median : 1.016 Median : 0.000 \n Mean : 2.12 Mean : 2.225 Mean : 2.658 Mean : 2.235 \n 3rd Qu.: 1.55 3rd Qu.: 2.471 3rd Qu.: 6.474 3rd Qu.: 3.134 \n Max. :11.56 Max. :11.734 Max. :11.303 Max. :10.888 \n HSPC_427 HSPC_431 HSPC_432 HSPC_435 \n Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.102 Median :0.000 Median : 1.098 \n Mean : 1.829 Mean : 2.360 Mean :2.169 Mean : 2.060 \n 3rd Qu.: 2.980 3rd Qu.: 3.640 3rd Qu.:3.261 3rd Qu.: 2.744 \n Max. :10.517 Max. :10.533 Max. :9.911 Max. :10.677 \n HSPC_436 HSPC_440 HSPC_441 HSPC_442 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.4719 Median : 1.385 Median : 1.084 Median : 0.595 \n Mean : 2.3880 Mean : 1.712 Mean : 2.265 Mean : 2.109 \n 3rd Qu.: 4.3738 3rd Qu.: 2.079 3rd Qu.: 2.828 3rd Qu.: 2.193 \n Max. :11.2839 Max. :11.065 Max. :11.152 Max. :11.560 \n HSPC_443 HSPC_444 HSPC_446 HSPC_447 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.7734 Median : 1.374 Median : 0.000 Median : 1.113 \n Mean : 2.5663 Mean : 2.262 Mean : 1.475 Mean : 2.446 \n 3rd Qu.: 4.9423 3rd Qu.: 2.952 3rd Qu.: 1.683 3rd Qu.: 4.733 \n Max. :10.9262 Max. :10.705 Max. :10.545 Max. :10.303 \n HSPC_448 HSPC_449 HSPC_450 HSPC_451 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.139 Median : 1.344 Median : 0.000 Median : 1.759 \n Mean : 2.396 Mean : 2.164 Mean : 1.946 Mean : 1.806 \n 3rd Qu.: 3.660 3rd Qu.: 2.490 3rd Qu.: 2.483 3rd Qu.: 2.528 \n Max. :11.091 Max. :11.324 Max. :10.397 Max. :10.395 \n HSPC_453 HSPC_454 HSPC_455 HSPC_456 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.9321 Median : 0.5303 Median : 0.000 Median : 0.6497 \n Mean : 2.4906 Mean : 2.4477 Mean : 2.379 Mean : 2.4263 \n 3rd Qu.: 4.9604 3rd Qu.: 4.8773 3rd Qu.: 3.016 3rd Qu.: 5.4740 \n Max. :10.5263 Max. :11.1628 Max. :11.437 Max. :10.9787 \n HSPC_457 HSPC_459 HSPC_460 HSPC_461 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.313 \n Mean : 2.060 Mean : 2.403 Mean : 1.712 Mean : 1.875 \n 3rd Qu.: 2.937 3rd Qu.: 3.029 3rd Qu.: 1.598 3rd Qu.: 2.104 \n Max. :11.746 Max. :12.135 Max. :12.526 Max. :10.210 \n HSPC_462 HSPC_463 HSPC_465 HSPC_466 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.7257 Median : 0.000 Median : 0.5816 \n Mean : 2.095 Mean : 2.2325 Mean : 2.000 Mean : 1.9972 \n 3rd Qu.: 2.578 3rd Qu.: 2.3442 3rd Qu.: 2.633 3rd Qu.: 2.2384 \n Max. :11.429 Max. :11.1776 Max. :11.064 Max. :11.5475 \n HSPC_467 HSPC_468 HSPC_470 HSPC_471 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.177 Median : 0.649 Median : 0.000 Median : 0.000 \n Mean : 1.866 Mean : 2.130 Mean : 1.774 Mean : 2.279 \n 3rd Qu.: 2.258 3rd Qu.: 2.513 3rd Qu.: 1.931 3rd Qu.: 2.744 \n Max. :10.632 Max. :10.527 Max. :10.781 Max. :11.533 \n HSPC_472 HSPC_473 HSPC_474 HSPC_475 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.265 Mean : 2.168 Mean : 2.016 Mean : 2.339 \n 3rd Qu.: 2.982 3rd Qu.: 2.677 3rd Qu.: 2.061 3rd Qu.: 3.319 \n Max. :11.795 Max. :12.071 Max. :11.732 Max. :10.672 \n HSPC_477 HSPC_478 HSPC_479 HSPC_480 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.6278 Median : 0.000 Median : 1.281 Median : 1.034 \n Mean : 1.9910 Mean : 2.068 Mean : 2.175 Mean : 2.239 \n 3rd Qu.: 1.6695 3rd Qu.: 3.402 3rd Qu.: 3.028 3rd Qu.: 2.642 \n Max. :11.1171 Max. :12.113 Max. :11.277 Max. :10.641 \n HSPC_482 HSPC_483 HSPC_485 HSPC_486 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.088 Median : 0.6036 Median : 1.411 \n Mean : 1.998 Mean : 2.454 Mean : 2.3824 Mean : 2.078 \n 3rd Qu.: 2.648 3rd Qu.: 3.006 3rd Qu.: 4.8213 3rd Qu.: 2.579 \n Max. :13.948 Max. :10.722 Max. :11.8691 Max. :10.155 \n HSPC_488 HSPC_489 HSPC_490 HSPC_491 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.310 Median : 0.000 \n Mean : 1.809 Mean : 1.947 Mean : 2.518 Mean : 2.268 \n 3rd Qu.: 2.120 3rd Qu.: 2.330 3rd Qu.: 4.140 3rd Qu.: 3.300 \n Max. :11.271 Max. :11.518 Max. :11.646 Max. :10.366 \n HSPC_492 HSPC_493 HSPC_494 HSPC_495 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.127 Mean : 2.054 Mean : 2.255 Mean : 2.326 \n 3rd Qu.: 2.322 3rd Qu.: 3.060 3rd Qu.: 3.386 3rd Qu.: 3.812 \n Max. :11.674 Max. :10.404 Max. :10.461 Max. :10.304 \n HSPC_496 HSPC_497 HSPC_498 HSPC_499 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.945 Median : 0.5839 Median : 0.000 \n Mean : 1.938 Mean : 2.287 Mean : 2.3731 Mean : 2.045 \n 3rd Qu.: 2.227 3rd Qu.: 2.872 3rd Qu.: 3.6112 3rd Qu.: 2.358 \n Max. :11.323 Max. :11.873 Max. :11.3264 Max. :10.632 \n HSPC_500 HSPC_501 HSPC_502 HSPC_503 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.9146 Median : 0.7789 \n Mean : 2.199 Mean : 2.209 Mean : 2.2727 Mean : 2.4495 \n 3rd Qu.: 2.678 3rd Qu.: 3.150 3rd Qu.: 2.8888 3rd Qu.: 5.4034 \n Max. :11.665 Max. :10.727 Max. :11.4591 Max. :11.5376 \n HSPC_504 HSPC_505 HSPC_506 HSPC_507 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.137 Mean : 2.132 Mean : 2.017 Mean : 2.314 \n 3rd Qu.: 3.035 3rd Qu.: 2.744 3rd Qu.: 2.794 3rd Qu.: 3.175 \n Max. :11.625 Max. :11.385 Max. :11.467 Max. :11.232 \n HSPC_508 HSPC_509 HSPC_510 HSPC_512 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.2297 Median : 1.691 Median : 1.166 Median : 0.000 \n Mean : 1.9265 Mean : 2.548 Mean : 2.319 Mean : 2.482 \n 3rd Qu.: 0.8975 3rd Qu.: 4.397 3rd Qu.: 3.492 3rd Qu.: 3.753 \n Max. :12.0747 Max. :10.603 Max. :10.885 Max. :12.492 \n HSPC_514 HSPC_515 HSPC_516 HSPC_518 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.109 Median : 0.8853 Median : 0.000 \n Mean : 2.295 Mean : 2.298 Mean : 2.5439 Mean : 2.649 \n 3rd Qu.: 2.429 3rd Qu.: 2.560 3rd Qu.: 4.6629 3rd Qu.: 5.581 \n Max. :11.783 Max. :12.193 Max. :12.1718 Max. :11.838 \n HSPC_520 HSPC_521 HSPC_522 HSPC_523 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.3648 \n Mean : 2.295 Mean : 2.348 Mean : 2.529 Mean : 1.9471 \n 3rd Qu.: 2.975 3rd Qu.: 3.375 3rd Qu.: 5.350 3rd Qu.: 1.5726 \n Max. :12.289 Max. :11.712 Max. :10.364 Max. :12.5906 \n HSPC_524 HSPC_526 HSPC_527 HSPC_528 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.777 Median : 0.532 \n Mean : 1.989 Mean : 2.218 Mean : 2.133 Mean : 2.238 \n 3rd Qu.: 3.267 3rd Qu.: 2.431 3rd Qu.: 1.651 3rd Qu.: 2.095 \n Max. :12.105 Max. :10.870 Max. :12.017 Max. :12.183 \n HSPC_530 HSPC_532 HSPC_533 HSPC_534 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.7537 Median : 0.000 \n Mean : 2.017 Mean : 1.856 Mean : 1.7546 Mean : 2.183 \n 3rd Qu.: 2.514 3rd Qu.: 1.816 3rd Qu.: 1.3378 3rd Qu.: 2.311 \n Max. :11.549 Max. :11.255 Max. :11.5862 Max. :11.696 \n HSPC_535 HSPC_537 HSPC_538 HSPC_539 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.122 Mean : 2.010 Mean : 2.501 Mean : 2.463 \n 3rd Qu.: 2.733 3rd Qu.: 2.541 3rd Qu.: 4.886 3rd Qu.: 4.100 \n Max. :10.793 Max. :10.305 Max. :11.359 Max. :11.755 \n HSPC_540 HSPC_541 HSPC_543 HSPC_544 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.9898 Median : 2.362 Median : 0.000 Median : 0.8222 \n Mean : 2.1775 Mean : 2.613 Mean : 2.275 Mean : 2.8070 \n 3rd Qu.: 1.9846 3rd Qu.: 4.440 3rd Qu.: 2.690 3rd Qu.: 6.4209 \n Max. :12.2963 Max. :11.844 Max. :10.983 Max. :10.7976 \n HSPC_545 HSPC_546 HSPC_547 HSPC_548 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 1.485 Median : 0.000 Median : 0.6548 Median : 1.456 \n Mean : 2.215 Mean : 2.424 Mean : 2.5255 Mean : 2.415 \n 3rd Qu.: 2.677 3rd Qu.: 3.573 3rd Qu.: 2.8714 3rd Qu.: 2.639 \n Max. :11.815 Max. :11.235 Max. :11.8801 Max. :11.955 \n HSPC_549 HSPC_550 HSPC_551 HSPC_552 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.750 Median : 1.287 Median : 1.226 \n Mean : 2.149 Mean : 2.592 Mean : 2.680 Mean : 2.236 \n 3rd Qu.: 2.289 3rd Qu.: 4.686 3rd Qu.: 4.007 3rd Qu.: 2.669 \n Max. :11.827 Max. :12.064 Max. :11.874 Max. :11.581 \n HSPC_553 HSPC_554 HSPC_555 HSPC_556 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.4709 Median : 0.000 Median : 0.000 Median : 0.9369 \n Mean : 2.6931 Mean : 2.090 Mean : 1.903 Mean : 2.4784 \n 3rd Qu.: 6.4420 3rd Qu.: 2.158 3rd Qu.: 2.579 3rd Qu.: 3.4024 \n Max. :11.0566 Max. :11.755 Max. :11.245 Max. :11.9838 \n HSPC_557 HSPC_559 HSPC_560 HSPC_562 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.681 Median : 0.000 \n Mean : 1.972 Mean : 1.937 Mean : 2.082 Mean : 2.470 \n 3rd Qu.: 1.880 3rd Qu.: 2.411 3rd Qu.: 2.436 3rd Qu.: 4.148 \n Max. :11.792 Max. :11.871 Max. :11.761 Max. :11.958 \n HSPC_563 HSPC_566 HSPC_567 HSPC_568 \n Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.00 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 1.83 Mean : 2.486 Mean : 2.186 Mean : 2.267 \n 3rd Qu.: 2.29 3rd Qu.: 3.577 3rd Qu.: 2.254 3rd Qu.: 2.957 \n Max. :10.59 Max. :12.452 Max. :11.302 Max. :10.851 \n HSPC_569 HSPC_571 HSPC_573 HSPC_574 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.771 Median : 1.042 Median : 0.000 Median : 0.7547 \n Mean : 2.283 Mean : 2.213 Mean : 2.089 Mean : 2.3196 \n 3rd Qu.: 3.021 3rd Qu.: 2.879 3rd Qu.: 2.291 3rd Qu.: 5.6078 \n Max. :10.720 Max. :10.939 Max. :11.397 Max. :10.4741 \n HSPC_575 HSPC_576 HSPC_577 HSPC_578 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.606 Median : 0.000 \n Mean : 2.016 Mean : 2.206 Mean : 2.358 Mean : 2.257 \n 3rd Qu.: 2.267 3rd Qu.: 2.741 3rd Qu.: 3.198 3rd Qu.: 2.923 \n Max. :10.687 Max. :11.201 Max. :11.613 Max. :12.323 \n HSPC_579 HSPC_580 HSPC_582 HSPC_584 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.182 Median : 0.9442 Median : 0.000 Median : 0.000 \n Mean : 2.472 Mean : 2.4264 Mean : 2.218 Mean : 2.276 \n 3rd Qu.: 5.009 3rd Qu.: 3.5841 3rd Qu.: 3.332 3rd Qu.: 3.067 \n Max. :11.096 Max. :10.6790 Max. :10.882 Max. :10.954 \n HSPC_585 HSPC_586 HSPC_589 HSPC_590 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8915 Median : 0.000 Median : 1.192 \n Mean : 2.034 Mean : 2.0490 Mean : 2.274 Mean : 2.252 \n 3rd Qu.: 2.157 3rd Qu.: 1.8340 3rd Qu.: 3.655 3rd Qu.: 2.364 \n Max. :11.956 Max. :11.4729 Max. :11.198 Max. :10.673 \n HSPC_592 HSPC_593 HSPC_594 HSPC_595 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.228 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.317 Mean : 2.329 Mean : 2.474 Mean : 1.463 \n 3rd Qu.: 2.671 3rd Qu.: 3.263 3rd Qu.: 4.396 3rd Qu.: 1.757 \n Max. :12.036 Max. :10.626 Max. :11.347 Max. :11.286 \n HSPC_596 HSPC_597 HSPC_598 HSPC_599 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.392 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.283 Mean : 1.858 Mean : 1.954 Mean : 1.905 \n 3rd Qu.: 3.425 3rd Qu.: 2.296 3rd Qu.: 2.320 3rd Qu.: 2.497 \n Max. :10.899 Max. :11.002 Max. :11.117 Max. :11.248 \n HSPC_600 HSPC_601 HSPC_602 HSPC_603 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.662 Median : 0.000 \n Mean : 2.335 Mean : 1.905 Mean : 2.343 Mean : 2.281 \n 3rd Qu.: 3.827 3rd Qu.: 2.376 3rd Qu.: 3.272 3rd Qu.: 3.048 \n Max. :11.208 Max. :11.022 Max. :10.908 Max. :11.464 \n HSPC_604 HSPC_606 HSPC_607 HSPC_608 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.136 Mean : 2.392 Mean : 2.142 Mean : 2.139 \n 3rd Qu.: 2.516 3rd Qu.: 4.726 3rd Qu.: 3.187 3rd Qu.: 2.885 \n Max. :11.743 Max. :11.210 Max. :10.319 Max. :10.802 \n HSPC_610 HSPC_612 HSPC_613 HSPC_614 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.315 \n Mean : 2.327 Mean : 2.298 Mean : 2.228 Mean : 2.364 \n 3rd Qu.: 3.718 3rd Qu.: 3.138 3rd Qu.: 2.705 3rd Qu.: 3.136 \n Max. :10.860 Max. :11.564 Max. :10.560 Max. :11.824 \n HSPC_615 HSPC_617 HSPC_618 HSPC_620 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8525 Median : 0.000 Median : 0.000 \n Mean : 1.964 Mean : 2.2100 Mean : 2.229 Mean : 1.881 \n 3rd Qu.: 2.451 3rd Qu.: 2.3301 3rd Qu.: 2.885 3rd Qu.: 2.518 \n Max. :11.058 Max. :10.9434 Max. :11.210 Max. :11.388 \n HSPC_623 HSPC_624 HSPC_625 HSPC_626 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.7201 Median : 0.000 Median : 0.000 \n Mean : 2.563 Mean : 2.0968 Mean : 2.042 Mean : 2.262 \n 3rd Qu.: 4.626 3rd Qu.: 1.8437 3rd Qu.: 2.938 3rd Qu.: 3.424 \n Max. :10.954 Max. :10.9459 Max. :11.226 Max. :11.770 \n HSPC_627 HSPC_628 HSPC_629 HSPC_630 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.269 Mean : 2.302 Mean : 2.212 Mean : 2.519 \n 3rd Qu.: 3.952 3rd Qu.: 2.875 3rd Qu.: 2.625 3rd Qu.: 4.511 \n Max. :11.426 Max. :11.792 Max. :11.139 Max. :11.519 \n HSPC_631 HSPC_633 HSPC_634 HSPC_635 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.303 Mean : 2.329 Mean : 2.268 Mean : 2.054 \n 3rd Qu.: 2.685 3rd Qu.: 3.619 3rd Qu.: 3.662 3rd Qu.: 2.629 \n Max. :10.996 Max. :12.011 Max. :11.406 Max. :11.178 \n HSPC_636 HSPC_637 HSPC_638 HSPC_639 \n Min. : 0.000 Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.9389 Median : 0.5101 Median : 0.9966 \n Mean : 1.953 Mean : 2.1351 Mean : 1.6966 Mean : 1.5879 \n 3rd Qu.: 2.129 3rd Qu.: 2.4817 3rd Qu.: 1.6879 3rd Qu.: 1.6840 \n Max. :11.057 Max. :11.1881 Max. :10.8837 Max. :10.9561 \n HSPC_640 HSPC_641 HSPC_643 HSPC_644 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.025 Median : 1.000 Median : 1.706 Median : 0.4904 \n Mean : 2.136 Mean : 1.957 Mean : 2.468 Mean : 2.4726 \n 3rd Qu.: 2.119 3rd Qu.: 2.001 3rd Qu.: 3.329 3rd Qu.: 5.6227 \n Max. :11.173 Max. :11.056 Max. :12.016 Max. :11.0232 \n HSPC_645 HSPC_646 HSPC_648 HSPC_649 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.7157 Median : 0.9959 Median : 1.519 Median : 0.7139 \n Mean : 2.3517 Mean : 2.0594 Mean : 2.267 Mean : 2.3593 \n 3rd Qu.: 4.5630 3rd Qu.: 2.3154 3rd Qu.: 2.722 3rd Qu.: 4.1542 \n Max. :10.9922 Max. :11.6070 Max. :11.243 Max. :10.7707 \n HSPC_651 HSPC_652 HSPC_654 HSPC_656 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 \n Median : 0.000 Median : 0.000 Median : 1.398 Median :0.000 \n Mean : 2.550 Mean : 1.764 Mean : 2.108 Mean :1.983 \n 3rd Qu.: 5.615 3rd Qu.: 2.038 3rd Qu.: 2.562 3rd Qu.:2.505 \n Max. :11.202 Max. :10.897 Max. :10.367 Max. :9.673 \n HSPC_657 HSPC_658 HSPC_660 HSPC_661 \n Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median :1.253 Median : 1.491 \n Mean : 1.839 Mean : 2.319 Mean :2.542 Mean : 2.401 \n 3rd Qu.: 2.239 3rd Qu.: 4.021 3rd Qu.:5.274 3rd Qu.: 2.775 \n Max. :12.132 Max. :11.264 Max. :9.852 Max. :11.647 \n HSPC_662 HSPC_663 HSPC_664 HSPC_665 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 0.9407 Median : 0.6452 \n Mean : 2.298 Mean : 2.726 Mean : 2.4039 Mean : 2.1211 \n 3rd Qu.: 2.939 3rd Qu.: 6.519 3rd Qu.: 3.4095 3rd Qu.: 2.0744 \n Max. :11.277 Max. :12.152 Max. :10.9423 Max. :12.0111 \n HSPC_666 HSPC_667 HSPC_668 HSPC_669 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.130 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.815 Mean : 2.075 Mean : 2.245 Mean : 1.992 \n 3rd Qu.: 6.359 3rd Qu.: 2.549 3rd Qu.: 2.407 3rd Qu.: 2.426 \n Max. :11.052 Max. :11.406 Max. :11.061 Max. :11.752 \n HSPC_670 HSPC_671 HSPC_672 HSPC_673 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.336 Mean : 2.349 Mean : 2.041 Mean : 2.148 \n 3rd Qu.: 3.188 3rd Qu.: 3.777 3rd Qu.: 2.057 3rd Qu.: 2.723 \n Max. :11.021 Max. :10.846 Max. :11.212 Max. :11.579 \n HSPC_674 HSPC_676 HSPC_678 HSPC_679 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.525 Median : 1.531 \n Mean : 1.801 Mean : 2.239 Mean : 2.298 Mean : 2.133 \n 3rd Qu.: 1.892 3rd Qu.: 3.097 3rd Qu.: 3.089 3rd Qu.: 2.737 \n Max. :10.875 Max. :10.496 Max. :12.125 Max. :11.583 \n HSPC_680 HSPC_681 HSPC_682 HSPC_683 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.037 Median : 1.043 Median : 1.180 \n Mean : 2.573 Mean : 2.277 Mean : 2.586 Mean : 2.498 \n 3rd Qu.: 4.165 3rd Qu.: 4.210 3rd Qu.: 5.432 3rd Qu.: 3.929 \n Max. :11.100 Max. :10.154 Max. :11.095 Max. :10.859 \n HSPC_687 HSPC_689 HSPC_690 HSPC_692 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 2.091 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.136 Mean : 2.489 Mean : 2.686 Mean : 2.285 \n 3rd Qu.: 2.911 3rd Qu.: 4.106 3rd Qu.: 5.055 3rd Qu.: 3.427 \n Max. :11.380 Max. :10.693 Max. :10.408 Max. :12.242 \n HSPC_695 HSPC_696 HSPC_697 HSPC_698 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.2681 Median : 1.538 Median : 1.271 Median : 0.000 \n Mean : 1.6151 Mean : 2.688 Mean : 2.529 Mean : 2.531 \n 3rd Qu.: 0.6895 3rd Qu.: 5.560 3rd Qu.: 4.779 3rd Qu.: 4.387 \n Max. :12.4139 Max. :10.880 Max. :10.292 Max. :12.146 \n HSPC_699 HSPC_700 HSPC_701 HSPC_702 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 1.157 Median : 0.000 \n Mean : 2.586 Mean : 2.402 Mean : 2.401 Mean : 2.723 \n 3rd Qu.: 4.595 3rd Qu.: 4.797 3rd Qu.: 3.889 3rd Qu.: 4.822 \n Max. :11.389 Max. :10.630 Max. :11.750 Max. :11.805 \n HSPC_703 HSPC_704 HSPC_705 HSPC_706 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 2.193 Median : 0.000 Median : 0.9795 Median : 1.273 \n Mean : 2.543 Mean : 2.598 Mean : 2.5048 Mean : 2.364 \n 3rd Qu.: 3.935 3rd Qu.: 4.335 3rd Qu.: 5.0680 3rd Qu.: 3.492 \n Max. :11.710 Max. :11.488 Max. :11.3580 Max. :10.447 \n HSPC_707 HSPC_708 HSPC_709 HSPC_714 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.361 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.371 Mean : 2.509 Mean : 2.601 Mean : 2.326 \n 3rd Qu.: 3.626 3rd Qu.: 3.832 3rd Qu.: 5.060 3rd Qu.: 3.324 \n Max. :11.796 Max. :10.865 Max. :10.145 Max. :11.126 \n HSPC_716 HSPC_717 HSPC_719 HSPC_720 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 1.154 Median : 1.855 Median : 0.8206 \n Mean : 2.325 Mean : 2.302 Mean : 2.519 Mean : 2.5768 \n 3rd Qu.: 3.356 3rd Qu.: 2.833 3rd Qu.: 4.115 3rd Qu.: 5.5594 \n Max. :11.812 Max. :11.047 Max. :12.237 Max. :10.5895 \n HSPC_721 HSPC_722 HSPC_723 HSPC_724 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 2.113 Median : 1.185 Median : 0.8421 Median : 0.6485 \n Mean : 2.205 Mean : 1.814 Mean : 2.6174 Mean : 1.9644 \n 3rd Qu.: 3.456 3rd Qu.: 2.269 3rd Qu.: 4.9545 3rd Qu.: 1.9402 \n Max. :10.706 Max. :10.709 Max. :11.5956 Max. :11.3505 \n HSPC_725 HSPC_727 HSPC_729 HSPC_730 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 1.577 Median : 1.576 Median : 0.9579 Median : 1.135 \n Mean : 2.483 Mean : 2.436 Mean : 2.2448 Mean : 2.445 \n 3rd Qu.: 3.741 3rd Qu.: 3.447 3rd Qu.: 2.7343 3rd Qu.: 3.475 \n Max. :10.647 Max. :11.512 Max. :10.9657 Max. :11.121 \n HSPC_731 HSPC_732 HSPC_733 HSPC_734 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.130 Median : 0.6937 Median : 1.436 Median : 0.7333 \n Mean : 2.854 Mean : 2.1051 Mean : 2.489 Mean : 2.5404 \n 3rd Qu.: 6.019 3rd Qu.: 2.0311 3rd Qu.: 3.738 3rd Qu.: 5.6282 \n Max. :10.471 Max. :11.0494 Max. :10.929 Max. :10.4547 \n HSPC_735 HSPC_736 HSPC_737 HSPC_738 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.033 Median : 0.6789 Median : 1.185 Median : 1.514 \n Mean : 2.389 Mean : 2.0224 Mean : 2.722 Mean : 2.503 \n 3rd Qu.: 3.056 3rd Qu.: 2.0017 3rd Qu.: 5.669 3rd Qu.: 3.602 \n Max. :10.866 Max. :11.8100 Max. :11.076 Max. :10.473 \n HSPC_740 HSPC_742 HSPC_743 HSPC_744 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.8437 Median : 1.122 Median : 1.213 \n Mean : 2.506 Mean : 1.8949 Mean : 2.028 Mean : 2.048 \n 3rd Qu.: 3.794 3rd Qu.: 1.7586 3rd Qu.: 2.840 3rd Qu.: 2.309 \n Max. :10.618 Max. :11.6327 Max. :10.449 Max. :10.598 \n HSPC_745 HSPC_746 HSPC_747 HSPC_748 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 2.403 Median : 2.184 Median : 0.000 Median : 1.181 \n Mean : 2.309 Mean : 2.153 Mean : 2.543 Mean : 2.017 \n 3rd Qu.: 3.793 3rd Qu.: 3.016 3rd Qu.: 4.751 3rd Qu.: 2.264 \n Max. :10.882 Max. :10.988 Max. :10.860 Max. :12.153 \n HSPC_749 HSPC_750 HSPC_751 HSPC_752 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.444 Median : 1.030 Median : 1.567 Median : 2.228 \n Mean : 2.477 Mean : 2.370 Mean : 2.416 Mean : 2.529 \n 3rd Qu.: 3.501 3rd Qu.: 3.052 3rd Qu.: 3.435 3rd Qu.: 3.976 \n Max. :11.391 Max. :11.167 Max. :10.239 Max. :10.586 \n HSPC_753 HSPC_755 HSPC_756 HSPC_757 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.062 Median : 0.740 Median : 1.731 Median : 1.395 \n Mean : 2.313 Mean : 2.102 Mean : 2.592 Mean : 2.477 \n 3rd Qu.: 2.961 3rd Qu.: 2.509 3rd Qu.: 4.107 3rd Qu.: 3.253 \n Max. :11.202 Max. :10.559 Max. :10.783 Max. :10.973 \n HSPC_758 HSPC_759 HSPC_760 HSPC_761 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.8648 Median : 0.9415 Median : 1.052 Median : 0.6917 \n Mean : 2.6819 Mean : 2.1274 Mean : 2.288 Mean : 2.2992 \n 3rd Qu.: 4.7233 3rd Qu.: 2.2271 3rd Qu.: 2.404 3rd Qu.: 2.6015 \n Max. :11.1096 Max. :11.2534 Max. :11.008 Max. :11.7228 \n HSPC_762 HSPC_764 HSPC_765 HSPC_766 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 1.271 Median : 1.784 Median : 2.116 Median : 0.9828 \n Mean : 2.242 Mean : 2.068 Mean : 2.100 Mean : 2.1721 \n 3rd Qu.: 2.734 3rd Qu.: 3.059 3rd Qu.: 2.939 3rd Qu.: 2.6115 \n Max. :12.043 Max. :11.003 Max. :12.757 Max. :10.2002 \n HSPC_767 HSPC_768 HSPC_769 HSPC_770 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.6646 Median : 1.703 Median : 0.000 Median : 1.760 \n Mean : 1.9552 Mean : 2.365 Mean : 2.080 Mean : 2.343 \n 3rd Qu.: 1.9730 3rd Qu.: 3.325 3rd Qu.: 3.289 3rd Qu.: 3.122 \n Max. :11.3033 Max. :10.958 Max. :11.176 Max. :10.497 \n HSPC_771 HSPC_772 HSPC_773 HSPC_774 \n Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000 \n Median : 1.178 Median : 1.601 Median : 0.9901 Median : 0.9736 \n Mean : 2.527 Mean : 2.283 Mean : 1.8628 Mean : 2.5263 \n 3rd Qu.: 3.342 3rd Qu.: 2.828 3rd Qu.: 1.9851 3rd Qu.: 5.4694 \n Max. :11.156 Max. :10.625 Max. :10.7274 Max. :10.7701 \n HSPC_776 HSPC_777 HSPC_778 HSPC_780 \n Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.053 Median :1.435 Median : 1.178 \n Mean : 2.315 Mean : 2.110 Mean :2.130 Mean : 2.476 \n 3rd Qu.: 3.788 3rd Qu.: 2.673 3rd Qu.:3.488 3rd Qu.: 3.769 \n Max. :11.105 Max. :11.646 Max. :9.535 Max. :11.265 \n HSPC_781 HSPC_782 HSPC_783 HSPC_784 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.050 \n Mean : 1.911 Mean : 2.416 Mean : 2.254 Mean : 2.175 \n 3rd Qu.: 2.884 3rd Qu.: 3.872 3rd Qu.: 2.548 3rd Qu.: 2.468 \n Max. :11.445 Max. :10.161 Max. :10.970 Max. :10.958 \n HSPC_785 HSPC_786 HSPC_787 HSPC_788 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 1.148 Median : 0.000 Median : 0.9386 \n Mean : 2.230 Mean : 2.467 Mean : 2.100 Mean : 1.9749 \n 3rd Qu.: 2.466 3rd Qu.: 3.899 3rd Qu.: 2.991 3rd Qu.: 2.6662 \n Max. :11.041 Max. :11.080 Max. :10.690 Max. :11.1078 \n HSPC_789 HSPC_790 HSPC_791 HSPC_794 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.181 Median : 1.353 Median : 1.790 Median : 1.113 \n Mean : 2.225 Mean : 2.255 Mean : 2.699 Mean : 2.225 \n 3rd Qu.: 2.876 3rd Qu.: 2.852 3rd Qu.: 4.931 3rd Qu.: 2.768 \n Max. :11.245 Max. :11.558 Max. :11.104 Max. :11.118 \n HSPC_795 HSPC_796 HSPC_797 HSPC_798 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.8317 Median : 0.7001 Median : 0.8722 Median : 1.531 \n Mean : 2.3985 Mean : 2.6865 Mean : 2.6172 Mean : 2.485 \n 3rd Qu.: 3.4461 3rd Qu.: 5.6688 3rd Qu.: 5.3078 3rd Qu.: 3.098 \n Max. :11.0956 Max. :11.0829 Max. :11.4339 Max. :10.933 \n HSPC_799 HSPC_800 HSPC_801 HSPC_802 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.033 \n Mean : 2.179 Mean : 2.173 Mean : 2.427 Mean : 2.613 \n 3rd Qu.: 3.517 3rd Qu.: 2.865 3rd Qu.: 4.665 3rd Qu.: 3.780 \n Max. :11.666 Max. :11.263 Max. :10.905 Max. :10.864 \n HSPC_803 HSPC_804 HSPC_806 HSPC_807 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.000 Median : 0.000 Median : 2.103 Median : 0.7501 \n Mean : 2.395 Mean : 2.301 Mean : 2.222 Mean : 2.2476 \n 3rd Qu.: 3.883 3rd Qu.: 3.167 3rd Qu.: 3.445 3rd Qu.: 2.3481 \n Max. :10.766 Max. :11.298 Max. :10.326 Max. :11.2700 \n HSPC_808 HSPC_809 HSPC_810 HSPC_812 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Median : 0.6619 Median : 1.788 Median : 1.651 Median : 0.8459 \n Mean : 2.1677 Mean : 2.544 Mean : 2.471 Mean : 2.2960 \n 3rd Qu.: 2.5355 3rd Qu.: 3.730 3rd Qu.: 3.662 3rd Qu.: 2.6906 \n Max. :10.9302 Max. :11.791 Max. :10.829 Max. :11.5500 \n HSPC_813 HSPC_814 HSPC_815 HSPC_816 \n Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.6278 Median : 1.110 Median : 0.9631 Median : 1.346 \n Mean : 2.2448 Mean : 2.762 Mean : 2.4587 Mean : 2.341 \n 3rd Qu.: 2.3066 3rd Qu.: 5.996 3rd Qu.: 3.4228 3rd Qu.: 2.842 \n Max. :12.0043 Max. :10.406 Max. :11.4527 Max. :11.151 \n HSPC_818 HSPC_819 HSPC_820 HSPC_821 \n Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 \n Median : 0.9967 Median : 1.365 Median : 0.8099 Median : 1.382 \n Mean : 2.3081 Mean : 2.426 Mean : 2.1063 Mean : 2.532 \n 3rd Qu.: 2.9942 3rd Qu.: 3.632 3rd Qu.: 2.4643 3rd Qu.: 3.462 \n Max. :11.9931 Max. :10.672 Max. :11.2412 Max. :12.126 \n HSPC_822 HSPC_824 HSPC_825 HSPC_826 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.387 Median : 0.000 Median : 1.386 Median : 1.324 \n Mean : 2.503 Mean : 2.084 Mean : 2.162 Mean : 2.398 \n 3rd Qu.: 3.799 3rd Qu.: 2.342 3rd Qu.: 2.897 3rd Qu.: 3.150 \n Max. :11.892 Max. :11.365 Max. :11.498 Max. :11.198 \n HSPC_827 HSPC_828 HSPC_831 HSPC_832 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.746 Median : 1.003 Median : 1.304 Median : 1.035 \n Mean : 2.239 Mean : 2.145 Mean : 2.589 Mean : 2.384 \n 3rd Qu.: 2.638 3rd Qu.: 2.326 3rd Qu.: 3.866 3rd Qu.: 3.450 \n Max. :12.101 Max. :10.710 Max. :10.839 Max. :10.686 \n HSPC_833 HSPC_834 HSPC_835 HSPC_836 \n Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.7166 Median : 0.9245 Median : 1.006 Median : 0.000 \n Mean : 2.3553 Mean : 2.0872 Mean : 2.552 Mean : 2.471 \n 3rd Qu.: 3.9364 3rd Qu.: 2.4568 3rd Qu.: 4.034 3rd Qu.: 3.994 \n Max. :11.1695 Max. :11.1803 Max. :11.779 Max. :11.316 \n HSPC_837 HSPC_838 HSPC_839 HSPC_840 \n Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.312 Median : 0.9838 Median : 1.083 Median : 1.867 \n Mean : 2.590 Mean : 2.5281 Mean : 2.380 Mean : 2.548 \n 3rd Qu.: 4.443 3rd Qu.: 3.5551 3rd Qu.: 3.743 3rd Qu.: 3.609 \n Max. :10.672 Max. :11.2707 Max. :10.966 Max. :10.867 \n HSPC_841 HSPC_842 HSPC_843 HSPC_844 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.439 Median : 1.774 Median : 1.257 Median : 1.584 \n Mean : 2.408 Mean : 2.380 Mean : 2.845 Mean : 2.627 \n 3rd Qu.: 3.494 3rd Qu.: 3.490 3rd Qu.: 6.768 3rd Qu.: 3.951 \n Max. :10.930 Max. :11.137 Max. :11.933 Max. :11.446 \n HSPC_845 HSPC_846 HSPC_848 HSPC_849 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 1.227 Median : 1.401 Median : 0.000 Median : 1.602 \n Mean : 2.464 Mean : 2.240 Mean : 2.152 Mean : 2.402 \n 3rd Qu.: 3.377 3rd Qu.: 2.920 3rd Qu.: 2.554 3rd Qu.: 2.920 \n Max. :10.535 Max. :11.519 Max. :11.266 Max. :11.678 \n HSPC_851 HSPC_852 \n Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 \n Mean : 2.319 Mean : 2.143 \n 3rd Qu.: 3.373 3rd Qu.: 2.901 \n Max. :11.602 Max. :11.469 \n\n\nHmmmm, did you get all that? Nope, me neither! We have 701 cells but we only have 6 samples for the frogs. We will need a different approach to get an overview but I find it is still useful to look at the few columns\n🎬 Get a quick overview the first 20 columns:\n\nsummary(hspc[1:20])\n\n ensembl_gene_id HSPC_001 HSPC_002 HSPC_003 \n Length:280 Min. : 0.000 Min. : 0.000 Min. : 0.0000 \n Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 \n Mode :character Median : 0.000 Median : 0.000 Median : 0.9929 \n Mean : 2.143 Mean : 1.673 Mean : 2.5964 \n 3rd Qu.: 2.120 3rd Qu.: 2.239 3rd Qu.: 6.1559 \n Max. :12.567 Max. :11.976 Max. :11.1138 \n HSPC_004 HSPC_006 HSPC_008 HSPC_009 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 \n Median : 0.000 Median : 1.276 Median : 0.000 Median :0.000 \n Mean : 1.851 Mean : 2.338 Mean : 2.375 Mean :2.220 \n 3rd Qu.: 2.466 3rd Qu.: 3.536 3rd Qu.: 3.851 3rd Qu.:3.594 \n Max. :11.133 Max. :10.014 Max. :11.574 Max. :9.997 \n HSPC_011 HSPC_012 HSPC_014 HSPC_015 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 1.750 Median : 0.000 Median : 0.000 \n Mean : 2.285 Mean : 2.431 Mean : 2.295 Mean : 2.515 \n 3rd Qu.: 3.193 3rd Qu.: 3.741 3rd Qu.: 3.150 3rd Qu.: 3.789 \n Max. :11.260 Max. :10.905 Max. :11.051 Max. :10.751 \n HSPC_016 HSPC_017 HSPC_018 HSPC_020 \n Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.9488 Median : 0.000 Median : 1.248 Median : 0.000 \n Mean : 2.6115 Mean : 2.146 Mean : 2.710 Mean : 2.509 \n 3rd Qu.: 5.9412 3rd Qu.: 2.357 3rd Qu.: 6.006 3rd Qu.: 4.470 \n Max. :11.3082 Max. :12.058 Max. :11.894 Max. :11.281 \n HSPC_021 HSPC_022 HSPC_023 HSPC_024 \n Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 \n 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 \n Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000 \n Mean : 2.170 Mean : 2.287 Mean : 2.314 Mean : 2.195 \n 3rd Qu.: 2.996 3rd Qu.: 3.351 3rd Qu.: 2.749 3rd Qu.: 2.944 \n Max. :10.709 Max. :11.814 Max. :12.113 Max. :11.279 \n\n\nNotice that:\n\nthe maximum value is much less high than for the frogs and has decimals. That is because the mouse data are logged (to base 2) normalised counts, not raw counts as they are in the frog data set.\na minimum value of 0 appears in all 20 columns - perhaps that is true across the whole dataset (or at least common)\nat least some of the medians are zeros so there must be quite a lot of zeros\nthe few columns we can see are roughly similar\nit would not be very practical to plot the distributions of values in cell cell using facet_wrap().\n\nIn this data set, there is even more of an advantage of using the pivot_longer(), group_by() and summarise() approach. We will be able to open the dataframe in the Viewer and make plots to examine whether the distributions are similar across cells.\n🎬 Summarise all the cells:\n\nhspc_summary_samp <- hspc |>\n pivot_longer(cols = -ensembl_gene_id,\n names_to = \"cell\",\n values_to = \"expr\") |>\n group_by(cell) |>\n summarise(min = min(expr),\n lowerq = quantile(expr, 0.25),\n mean = mean(expr),\n median = median(expr),\n sd = sd(expr),\n upperq = quantile(expr, 0.75),\n max = max(expr),\n n_zero = sum(expr == 0))\n\nNotice 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.\n🎬 View the hspc_summary_samp dataframe (click on it in the environment).\nAll 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.\nTo 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 +/- one s.d. Not unlike a boxplot, but when you need the boxes too be very narrow!\n🎬 Create a pointrange plot.\n\nhspc_summary_samp |> \n ggplot(aes(x = cell, y = mean)) +\n geom_pointrange(aes(ymin = mean - sd, \n ymax = mean + sd ),\n size = 0.1)\n\n\n\n\nYou will need to use the Zoom button to pop the plot window out so you can make it as wide as possible\nThe things to notice are:\n\nthe average expression in cells is similar for all cells. This is good to know - if some cells had much lower expression perhaps there is something wrong with them, or their sequencing, and they should be excluded.\nthe distributions are roughly similar in width too\n\nThe default order of cell is alphabetical. It can be easier to see these (non-) effects if we order the lines by the size of the mean.\n🎬 Order a pointrange plot with reorder(variable_to_order, order_by).\n\nhspc_summary_samp |> \n ggplot(aes(x = reorder(cell, mean), y = mean)) +\n geom_pointrange(aes(ymin = mean - sd, \n ymax = mean + sd ),\n size = 0.1)\n\n\n\n\nreorder() arranges cell in increasing size of mean\n🎬 Write hspc_summary_samp to a file called “hspc_summary_samp.csv”:\n\n\n\nDistribution of values across the genes\n\n🐸 Frog genes\nThere are lots of genes in this dataset therefore we will take the same approach as that we took for the distributions across mouse cells. We will pivot the data to tidy and then summarise the counts for each gene.\n🎬 Summarise the counts for each genes:\n\ns30_summary_gene <- s30 |>\n pivot_longer(cols = -xenbase_gene_id,\n names_to = \"sample\",\n values_to = \"count\") |>\n group_by(xenbase_gene_id) |>\n summarise(min = min(count),\n lowerq = quantile(count, 0.25),\n sd = sd(count),\n mean = mean(count),\n median = median(count),\n upperq = quantile(count, 0.75),\n max = max(count),\n total = sum(count),\n n_zero = sum(count == 0))\n\nI have calculated the values we used before with one addition: the sum of the counts (total).\n🎬 View the s30_summary_gene dataframe.\nNotice that we have:\n\na lot of genes with counts of zero in every sample\na lot of genes with zero counts in several of the samples\nsome very very low counts.\n\nThese should be filtered out because they are unreliable - or, at the least, uninformative. The goal of our downstream analysis will be to see if there is a signifcance difference in gene expression between the control and FGF-treated sibling. Since we have only three replicates in each group, having one or two unreliable, missing or zero values, makes such a determination impossible for a particular gene. We will use the total counts and the number of samples with non-zero values to filter our genes later.\nAs we have a lot of genes, it is again helpful to plot the mean counts with pointrange to get an overview. We will plot the log of the counts - we saw earlier that logging made it easier to understand the distribution of counts over such a wide range. We will also order the genes from lowest to highest mean count.\n🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():\n\ns30_summary_gene |> \n ggplot(aes(x = reorder(xenbase_gene_id, mean), y = log10(mean))) +\n geom_pointrange(aes(ymin = log10(mean - sd), \n ymax = log10(mean + sd )),\n size = 0.1)\n\n\n\n\n(Remember, the warning is expected since we have zeros).\nYou can see we also have quite a few genes with means less than 1 (log below zero). Note that the variability between genes (average counts between 0 and 102586) is far greater than between samples (average counts from 260 to 426) which is exactly what we would expect to see.\n🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:\n\n\n🐭 Mouse genes\nThere are fewer genes in this dataset, but still more than you can understand without the overview provided by a plot. We will again pivot the data to tidy and then summarise the expression for each gene.\n🎬 Summarise the expression for each genes:\n\nhspc_summary_gene <- hspc |>\n pivot_longer(cols = -ensembl_gene_id,\n names_to = \"cell\",\n values_to = \"expr\") |>\n group_by(ensembl_gene_id) |>\n summarise(min = min(expr),\n lowerq = quantile(expr, 0.25),\n sd = sd(expr),\n mean = mean(expr),\n median = median(expr),\n upperq = quantile(expr, 0.75),\n max = max(expr),\n total = sum(expr),\n n_zero = sum(expr == 0))\n\n🎬 View the hspc_summary_gene dataframe. Remember these are normalised and logged (base 2) so we should not see very large values.\nNotice that we have:\n\nno genes with 0 in every cell\nvery few genes (9) with no zeros at all\nquite a few genes with zero in many cells but this matters less than zeros in the frog samples because we had just 6 samples and we have 701 cells.\n\nAs we have a lot of genes, it is again helpful to plot the mean expression with pointrange to get an overview. We do not need to log the values but ordering the genes will help.\n🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():\n\nhspc_summary_gene |> \n ggplot(aes(x = reorder(ensembl_gene_id, mean), y = mean))+\n geom_pointrange(aes(ymin = mean - sd, \n ymax = mean + sd),\n size = 0.1)\n\n\n\n\nNote again that the variability between genes (average expression between 0.02 and and 10.03) is far greater than between cells (average expression from1.46 to 3.18) which is expected.\n🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:" - }, - { - "objectID": "omics/week-3/workshop.html#filtering-for-qc", - "href": "omics/week-3/workshop.html#filtering-for-qc", - "title": "Workshop", - "section": "Filtering for QC", - "text": "Filtering for QC\n\n🐸 Frog filtering\nOur samples look to be similarly well sequenced. There are no samples we should remove. However, some genes are not express or the expression values are so low in for a gene that they are uninformative. We will filter the s30_summary_gene dataframe to obtain a list of xenbase_gene_id we can use to filter s30.\nMy suggestion is to include only the genes with counts in all 6 or 5 out of 6 samples and those with total counts above 20.\n🎬 Filter the summary by gene dataframe:\n\ns30_summary_gene_filtered <- s30_summary_gene |> \n filter(total > 20) |> \n filter(n_zero < 2)\n\n🎬 Write the filtered summary by gene to file:\n\nwrite_csv(s30_summary_gene_filtered, \n file = \"data-processed/s30_summary_gene_filtered.csv\")\n\n🎬 Use the list of xenbase_gene_id in the filtered summary to filter the original dataset:\n\ns30_filtered <- s30 |> \n filter(xenbase_gene_id %in% s30_summary_gene_filtered$xenbase_gene_id)\n\n🎬 Write the filtered original to file:\n\nwrite_csv(s30_filtered, \n file = \"data-processed/s30_filtered.csv\")\n\n\n\n🐭 Mouse filtering\nI would take a different approach to filtering the single cell data. For the Frog samples we are examining the control and the FGF treated samples. This means have a low number of counts overall means the gene is not really expressed (detected) in any condition, and filtering out those genes is removing things that definitely are not interesting. For the mice, we have examined only one cell type but will be making comparisons between cells types. It may be that low expression of a gene in this cell type tells us something if that gene is highly expressed in another cell type. Instead, we will make statistical comparisons between the cell types and then filter based on overall expression, the difference in expression between cell types and whether that difference is significant.\nThe number of “replicates” is also important. When you have only three in each group it is not possible to make statistical comparisons when several replicates are zero. This is less of an issue with single cell data." - }, - { - "objectID": "omics/week-3/workshop.html#look-after-future-you", - "href": "omics/week-3/workshop.html#look-after-future-you", - "title": "Workshop", - "section": "🤗 Look after future you!", - "text": "🤗 Look after future you!\nYou need only do the section for your own project data\n\n🐸 Frogs and future you\n🎬 Create a new Project, frogs-88H, populated with folders and your 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. Make sure you can close down RStudio, reopen it and run your whole script again.\n\n\n🐭 Mice and future you\n🎬 Create a new Project, mice-88H, populated with folders and your data. Make a script file called hspc-prog.R. This will a be commented analysis of the hspc cells vs the prog cells. At this point you will have only code for the hspc cells. 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. Make sure you can close down RStudio, reopen it and run your whole script again." - }, - { - "objectID": "omics/week-3/workshop.html#footnotes", - "href": "omics/week-3/workshop.html#footnotes", - "title": "Workshop", - "section": "Footnotes", - "text": "Footnotes\n\n\nThis a result of the Central limit theorem,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.↩︎\nThis a result of the Central limit theorem,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.↩︎" + "text": "Students who successfully complete this module will be able to\n\nuse appropriate computational techniques to reproducibly process, analyse and visualise data and generate scientific reports based on project work." }, { - "objectID": "omics/week-3/study_after_workshop.html", - "href": "omics/week-3/study_after_workshop.html", - "title": "Independent Study to consolidate this week", + "objectID": "index.html#what-is-this-site-for", + "href": "index.html#what-is-this-site-for", + "title": "Data Analysis for the Group Research Project", "section": "", - "text": "You need only do the section for your own project data\n🐸 Frogs\n🎬 Open your frogs-88H Project. Make a new script and, using cont-fgf-s30.R as a template, repeat the analysis on one of the other comparisons.\n🐭 Mice\n🎬 Open your mice-88H Project. Open your hspc-prog.R script and, using you code working with the hspc cells as a template, repeat the analysis on the prog cells.\n🍂 xxxx\n🎬 Open your xxxx-88H Project. Make a new script and, using xxxxxxx.R as a template, repeat the analysis on xxxxxxxx" + "text": "All material is on the VLE so why is this site useful? This site collects everything together in a searchable way. The search icon is on the top right." } ] \ No newline at end of file