google drive: https://www.york.ac.uk/it-services/services/drive/#tab-6
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documenting
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organisation within files
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Data files. Similarities and differences
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Sequences data
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Image data
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Structure data.
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Keeping a lab book
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Readme
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Reference managers: Zotero?
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Week 2 Core 2 NEEDS A TITLE
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Before Possibly: Code formatting and style, π Cool code Tips, Code βalgorithmically.β, Writing functions (R and python??) (BIO00058M-Data-science-2020/slides/03_repro_and_intro_to_rmarkdown)
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Workshop
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Week 6 Core 3 Reproducible Reporting
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Before (BIO00058M-Data-science-2020/slides/03_repro_and_intro_to_rmarkdown and BIO00058M-Data-science-2020/slides/04_advanced_rmarkdown.html) Literate programming What is quarto markdown basics: text, code chunks, headings yaml automatic numbering of figures and tables cross references special characters citations
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Workshop
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practice doing the above with your project and data
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diff --git a/_site/core/week-1/overview.html b/_site/core/week-1/overview.html
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+Data Analysis for Group Project - Overview
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These contain all the code needed in the workshop even where it is not visible on the webpage.
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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---
+R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
+
+
+Xie, Yihui. 2022. βKnitr: A General-Purpose Package for Dynamic Report Generation in r.βhttps://yihui.org/knitr/.
+
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---
+R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
+
+
+Xie, Yihui. 2022. βKnitr: A General-Purpose Package for Dynamic Report Generation in r.βhttps://yihui.org/knitr/.
+
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---
+R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
+
+
+Xie, Yihui. 2022. βKnitr: A General-Purpose Package for Dynamic Report Generation in r.βhttps://yihui.org/knitr/.
+
+
+
+
+
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+Data Analysis for Group Project - Omics Data Analysis for Group Project
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Omics Data Analysis for Group Project
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Content
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Omics 1: Hello data!
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before - sequence data fundamentals - where have the data come from - processing so far - why log2 - overarching aims
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workshop - set up a project and create the organisation - meta data - open in understanding the data numbers of variables, organisation - importing - exploring, what kind of values are they, how many missing - qc plots - removing the useless, writing to files
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after - document what you have done - format your figures
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Omics 2: Statisitcal Analysis
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before - which genes matter: expression, DE - goal of DE - how does DE work - multiple testing: FDR - say what comparisons we will do
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workshop - open project import the data - revise! - prep for DE - lists of genes - writing to files
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after - document what you have done - repeat on another comparison
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Omics 3: Visualising and Interpreting
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before - recap what we have - volcano plot described - GO terms -
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workshop - open project import the data - revise! - volcano plot - kable table? knitr::kable(df) |> kableExtra::kable_styling() - annotating with go terms
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after - document what you have done - repeat on another comparison
π» Use the work you completed in the workshop as a template to apply to a new case.
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+Data Analysis for Group Project - Independent Study to consolidate this week
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Independent Study to consolidate this week
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Core 1
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You need only do the section for your own project data
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πΈ Frogs
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π¬ 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.
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π Mice
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π¬ 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.
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π xxxx
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π¬ Open your xxxx-88H Project. Make a new script and, using xxxxxxx.R as a template, repeat the analysis on xxxxxxxx
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- Data Analysis for Group Project - Independent Study to prepare for workshop
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Independent Study to prepare for workshop
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Core 1
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Omics workshops
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πΈ the difference between the control and the FGF treated sibling at S30
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π the difference between HSPC and Prog cells
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π ???????
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See later workshops.
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Sequence Data
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Sequence Data
reads
quality control
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Sequence Data
normalise
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Aims
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Aims
find important genes
what is βimportantβ
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Aims
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What is a read
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What is a read
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FASTQ format files
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sequences and information about each sequenceβs read accuracy
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Differential expression
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what is differentially expressed
+
how are DE genes related
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what are DE genes involved with
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Stem cells
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Stem cells: background
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Cells were sorted using flow cytometry on the basis of cell surface markers. There are three cell types:
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long-term haematopoetic stem cells (LT-HSCs) defined as : Lineage- ckit+ Sca1+ CD34- Flk2-
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haematopoetic stem and progenitor cells (HSPCs) defined as : Lineage- Sca1+ ckit+
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progenitor cells (Progs) defined as : Lineage- Sca1- ckit+
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Each cell is then sequenced to quantify all the transcripts in each cell. different transcripts (genes) were identified.
-Nestorowa, Sonia, Fiona K. Hamey, Blanca Pijuan Sala, Evangelia Diamanti, Mairi Shepherd, Elisa Laurenti, Nicola K. Wilson, David G. Kent, and Berthold GΓΆttgens. 2016. βA Single-Cell Resolution Map of Mouse Hematopoietic Stem and Progenitor Cell Differentiation.βBlood 128 (8): e20β31. https://doi.org/10.1182/blood-2016-05-716480.
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Frog development: Processing so far
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selection of cells
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selection of genes: subset of surfaceome
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log2 normalised values
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+ const cites = parentEl.dataset.cites;
+ if (cites) {
+ return {
+ el,
+ cites: cites.split(' ')
+ };
+ } else {
+ return findCites(el.parentElement)
+ }
+ } else {
+ return undefined;
+ }
+ };
+ var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
+ for (var i=0; i {
+ return filterRegex.test(href) || localhostRegex.test(href);
+ }
+ // Inspect non-navigation links and adorn them if external
+ var links = window.document.querySelectorAll('a[href]:not(.nav-link):not(.navbar-brand):not(.toc-action):not(.sidebar-link):not(.sidebar-item-toggle):not(.pagination-link):not(.no-external):not([aria-hidden]):not(.dropdown-item)');
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+Data Analysis for Group Project - Workshop
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Workshop
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Omics 1: Hello data!
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Author
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Emma Rand
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Introduction
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Session overview
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In this workshop you will learn what steps to take to get a good understanding of your βomics 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.
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You should examine all three data sets because the comparisons will give you a stronger understanding of your own project data.
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Exercises
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Set up a Project
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π¬ Start RStudio from the Start menu
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π¬ Make an RStudio project. Be deliberate about where you create it so that it is a good place for you
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π¬ Use the Files pane to make a new folder for the data. I suggest data-raw
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π¬ Make a new script called workshop-1.R to carry out the rest of the work.
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π¬ Record what you do and what you find out. All of it!
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π¬ Load tidyverse(Wickham et al. 2019) for importing, summarising, plotting and filtering.
# πΈ import the s30 data
+s30<-read_csv("data-raw/xlaevis_counts_S30.csv")
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# π import the hspc data
+hspc<-read_csv("data-raw/surfaceome_hspc.csv")
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# π xxxx import the xxxx data
+# prog <- read_csv("")
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π¬ Check these have the number of rows and column you were expecting and that column types and names are as expected.
+
Explore
+
The first task is to get an overview. We want to know
+
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are there any missing values? If so, how many and how are they distributed?
+
how may zeros are there and how are they distributed
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does it look as tough all the samples/cells were equally βsuccessfulβ? Can we spot any problematic anomalies?
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what is the distribution of values?
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If our data collection has gone well we would hope to see approximately the same average expression in each sample or cell of the same type. 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.
+
We get this overview by looking at:
+
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The distribution of values across the whole dataset
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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.
+
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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 βtidyβ 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().
+
πΈ Frog
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π¬ Pivot the counts (stack the columns) so all the counts are in a single column (count) and pipe into ggplot() to create a histogram:
This data is very skewed - there are so many low values that we canβt see the the tiny bars for the higher values. Logging the counts is a way to make the distribution more visible.
Iβve 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 canβt 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 βomic data. We will revisit this after we have considered the distribution of counts across samples and genes.
+
π Mouse cells
+
π¬ 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:
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 βrealβ values? If we had the whole genome we would not expect to see such a pattern - weβd 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
+
πΈ 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.
+
π¬ Get a quick overview of the columns:
+
+
# 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
+ Class :character 1st Qu.: 14.0 1st Qu.: 14.0 1st Qu.: 23.0
+ Mode :character Median : 70.0 Median : 75.0 Median : 107.0
+ Mean : 317.1 Mean : 335.8 Mean : 426.3
+ 3rd Qu.: 205.0 3rd Qu.: 220.0 3rd Qu.: 301.0
+ Max. :101746.0 Max. :118708.0 Max. :117945.0
+ S30_F_5 S30_F_6 S30_F_A
+ Min. : 0.0 Min. : 0.0 Min. : 0.0
+ 1st Qu.: 19.0 1st Qu.: 17.0 1st Qu.: 16.0
+ Median : 88.0 Median : 84.0 Median : 69.0
+ Mean : 376.2 Mean : 376.5 Mean : 260.4
+ 3rd Qu.: 251.0 3rd Qu.: 246.0 3rd Qu.: 187.0
+ Max. :117573.0 Max. :130672.0 Max. :61531.0
+
+
+
Notice that: - the minimum count is 0 and the maximums are very high in all the columns - the medians are quite a lot lower than the means so the data are skewed (hump to the left, tail to the right) - there must be quite a lot of zeros - the columns are roughly similar and it doesnβt look like there is an anomalous replicate.
+
To find out how may zeros there are in a column we can make use of the fact that TRUE evaluates to 1 and FALSE evaluates to 0. This means sum(S30_C_5 == 0) gives the number of 0 in the S30_C_5 column
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.
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()
the distributions are roughly similar in width, height, location and overall shape so it doesnβt look as though we have any suspect samples
+
the peak at zero suggests quite a few counts of 1.
+
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 βomic data. We will revisit this after we have considered the distribution of counts across genes (averaged over the samples).
+
π Mouse cells
+
We used the summary() function to get an overview of the columns in the frog data. Letβs try that here.
ensembl_gene_id HSPC_001 HSPC_002 HSPC_003
+ Length:280 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Mode :character Median : 0.000 Median : 0.000 Median : 0.9929
+ Mean : 2.143 Mean : 1.673 Mean : 2.5964
+ 3rd Qu.: 2.120 3rd Qu.: 2.239 3rd Qu.: 6.1559
+ Max. :12.567 Max. :11.976 Max. :11.1138
+ HSPC_004 HSPC_006 HSPC_008 HSPC_009
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000
+ Median : 0.000 Median : 1.276 Median : 0.000 Median :0.000
+ Mean : 1.851 Mean : 2.338 Mean : 2.375 Mean :2.220
+ 3rd Qu.: 2.466 3rd Qu.: 3.536 3rd Qu.: 3.851 3rd Qu.:3.594
+ Max. :11.133 Max. :10.014 Max. :11.574 Max. :9.997
+ HSPC_011 HSPC_012 HSPC_014 HSPC_015
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 1.750 Median : 0.000 Median : 0.000
+ Mean : 2.285 Mean : 2.431 Mean : 2.295 Mean : 2.515
+ 3rd Qu.: 3.193 3rd Qu.: 3.741 3rd Qu.: 3.150 3rd Qu.: 3.789
+ Max. :11.260 Max. :10.905 Max. :11.051 Max. :10.751
+ HSPC_016 HSPC_017 HSPC_018 HSPC_020
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.9488 Median : 0.000 Median : 1.248 Median : 0.000
+ Mean : 2.6115 Mean : 2.146 Mean : 2.710 Mean : 2.509
+ 3rd Qu.: 5.9412 3rd Qu.: 2.357 3rd Qu.: 6.006 3rd Qu.: 4.470
+ Max. :11.3082 Max. :12.058 Max. :11.894 Max. :11.281
+ HSPC_021 HSPC_022 HSPC_023 HSPC_024
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.170 Mean : 2.287 Mean : 2.314 Mean : 2.195
+ 3rd Qu.: 2.996 3rd Qu.: 3.351 3rd Qu.: 2.749 3rd Qu.: 2.944
+ Max. :10.709 Max. :11.814 Max. :12.113 Max. :11.279
+ HSPC_025 HSPC_026 HSPC_027 HSPC_028
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.572 Median : 1.385 Median : 0.000 Median : 0.000
+ Mean : 2.710 Mean : 2.721 Mean : 2.458 Mean : 1.906
+ 3rd Qu.: 5.735 3rd Qu.: 6.392 3rd Qu.: 5.496 3rd Qu.: 2.037
+ Max. :11.309 Max. :10.865 Max. :11.266 Max. :10.777
+ HSPC_030 HSPC_031 HSPC_033 HSPC_034
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 1.119 Median : 0.9026 Median : 0.000 Median : 0.7984
+ Mean : 2.338 Mean : 2.3049 Mean : 1.938 Mean : 2.3220
+ 3rd Qu.: 3.005 3rd Qu.: 2.9919 3rd Qu.: 2.434 3rd Qu.: 4.8324
+ Max. :11.391 Max. :11.1748 Max. :10.808 Max. :10.6707
+ HSPC_035 HSPC_036 HSPC_037 HSPC_038
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.8879 Median : 1.517 Median : 0.000
+ Mean : 1.810 Mean : 2.6918 Mean : 2.327 Mean : 2.212
+ 3rd Qu.: 2.175 3rd Qu.: 5.9822 3rd Qu.: 3.079 3rd Qu.: 2.867
+ Max. :11.221 Max. :11.3018 Max. :11.399 Max. :12.275
+ HSPC_040 HSPC_041 HSPC_042 HSPC_043
+ Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.8673 Median : 1.342
+ Mean : 2.509 Mean : 2.492 Mean : 2.3673 Mean : 2.420
+ 3rd Qu.: 3.995 3rd Qu.: 3.943 3rd Qu.: 3.8371 3rd Qu.: 3.731
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+ Max. :9.821 Max. :10.8128 Max. :10.568 Max. :11.256
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+ Max. :11.429 Max. :11.1776 Max. :11.064 Max. :11.5475
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+ Max. :11.795 Max. :12.071 Max. :11.732 Max. :10.672
+ HSPC_477 HSPC_478 HSPC_479 HSPC_480
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+ Max. :11.1171 Max. :12.113 Max. :11.277 Max. :10.641
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+ HSPC_579 HSPC_580 HSPC_582 HSPC_584
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+ HSPC_596 HSPC_597 HSPC_598 HSPC_599
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+ Max. :10.899 Max. :11.002 Max. :11.117 Max. :11.248
+ HSPC_600 HSPC_601 HSPC_602 HSPC_603
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+ HSPC_604 HSPC_606 HSPC_607 HSPC_608
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+ 3rd Qu.: 2.516 3rd Qu.: 4.726 3rd Qu.: 3.187 3rd Qu.: 2.885
+ Max. :11.743 Max. :11.210 Max. :10.319 Max. :10.802
+ HSPC_610 HSPC_612 HSPC_613 HSPC_614
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+ Max. :10.860 Max. :11.564 Max. :10.560 Max. :11.824
+ HSPC_615 HSPC_617 HSPC_618 HSPC_620
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+ Max. :11.058 Max. :10.9434 Max. :11.210 Max. :11.388
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+ Max. :10.954 Max. :10.9459 Max. :11.226 Max. :11.770
+ HSPC_627 HSPC_628 HSPC_629 HSPC_630
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+ HSPC_631 HSPC_633 HSPC_634 HSPC_635
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+ Max. :10.996 Max. :12.011 Max. :11.406 Max. :11.178
+ HSPC_636 HSPC_637 HSPC_638 HSPC_639
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+ Max. :11.057 Max. :11.1881 Max. :10.8837 Max. :10.9561
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+ Max. :11.173 Max. :11.056 Max. :12.016 Max. :11.0232
+ HSPC_645 HSPC_646 HSPC_648 HSPC_649
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+ Max. :10.9922 Max. :11.6070 Max. :11.243 Max. :10.7707
+ HSPC_651 HSPC_652 HSPC_654 HSPC_656
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+ Max. :11.202 Max. :10.897 Max. :10.367 Max. :9.673
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+ Max. :12.132 Max. :11.264 Max. :9.852 Max. :11.647
+ HSPC_662 HSPC_663 HSPC_664 HSPC_665
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+ Max. :11.277 Max. :12.152 Max. :10.9423 Max. :12.0111
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+ 3rd Qu.: 6.359 3rd Qu.: 2.549 3rd Qu.: 2.407 3rd Qu.: 2.426
+ Max. :11.052 Max. :11.406 Max. :11.061 Max. :11.752
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+ 3rd Qu.: 3.188 3rd Qu.: 3.777 3rd Qu.: 2.057 3rd Qu.: 2.723
+ Max. :11.021 Max. :10.846 Max. :11.212 Max. :11.579
+ HSPC_674 HSPC_676 HSPC_678 HSPC_679
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+ 3rd Qu.: 1.892 3rd Qu.: 3.097 3rd Qu.: 3.089 3rd Qu.: 2.737
+ Max. :10.875 Max. :10.496 Max. :12.125 Max. :11.583
+ HSPC_680 HSPC_681 HSPC_682 HSPC_683
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+ Median : 0.000 Median : 1.037 Median : 1.043 Median : 1.180
+ Mean : 2.573 Mean : 2.277 Mean : 2.586 Mean : 2.498
+ 3rd Qu.: 4.165 3rd Qu.: 4.210 3rd Qu.: 5.432 3rd Qu.: 3.929
+ Max. :11.100 Max. :10.154 Max. :11.095 Max. :10.859
+ HSPC_687 HSPC_689 HSPC_690 HSPC_692
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 2.091 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.136 Mean : 2.489 Mean : 2.686 Mean : 2.285
+ 3rd Qu.: 2.911 3rd Qu.: 4.106 3rd Qu.: 5.055 3rd Qu.: 3.427
+ Max. :11.380 Max. :10.693 Max. :10.408 Max. :12.242
+ HSPC_695 HSPC_696 HSPC_697 HSPC_698
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.2681 Median : 1.538 Median : 1.271 Median : 0.000
+ Mean : 1.6151 Mean : 2.688 Mean : 2.529 Mean : 2.531
+ 3rd Qu.: 0.6895 3rd Qu.: 5.560 3rd Qu.: 4.779 3rd Qu.: 4.387
+ Max. :12.4139 Max. :10.880 Max. :10.292 Max. :12.146
+ HSPC_699 HSPC_700 HSPC_701 HSPC_702
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 1.157 Median : 0.000
+ Mean : 2.586 Mean : 2.402 Mean : 2.401 Mean : 2.723
+ 3rd Qu.: 4.595 3rd Qu.: 4.797 3rd Qu.: 3.889 3rd Qu.: 4.822
+ Max. :11.389 Max. :10.630 Max. :11.750 Max. :11.805
+ HSPC_703 HSPC_704 HSPC_705 HSPC_706
+ Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 2.193 Median : 0.000 Median : 0.9795 Median : 1.273
+ Mean : 2.543 Mean : 2.598 Mean : 2.5048 Mean : 2.364
+ 3rd Qu.: 3.935 3rd Qu.: 4.335 3rd Qu.: 5.0680 3rd Qu.: 3.492
+ Max. :11.710 Max. :11.488 Max. :11.3580 Max. :10.447
+ HSPC_707 HSPC_708 HSPC_709 HSPC_714
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.361 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.371 Mean : 2.509 Mean : 2.601 Mean : 2.326
+ 3rd Qu.: 3.626 3rd Qu.: 3.832 3rd Qu.: 5.060 3rd Qu.: 3.324
+ Max. :11.796 Max. :10.865 Max. :10.145 Max. :11.126
+ HSPC_716 HSPC_717 HSPC_719 HSPC_720
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 0.000 Median : 1.154 Median : 1.855 Median : 0.8206
+ Mean : 2.325 Mean : 2.302 Mean : 2.519 Mean : 2.5768
+ 3rd Qu.: 3.356 3rd Qu.: 2.833 3rd Qu.: 4.115 3rd Qu.: 5.5594
+ Max. :11.812 Max. :11.047 Max. :12.237 Max. :10.5895
+ HSPC_721 HSPC_722 HSPC_723 HSPC_724
+ Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000
+ Median : 2.113 Median : 1.185 Median : 0.8421 Median : 0.6485
+ Mean : 2.205 Mean : 1.814 Mean : 2.6174 Mean : 1.9644
+ 3rd Qu.: 3.456 3rd Qu.: 2.269 3rd Qu.: 4.9545 3rd Qu.: 1.9402
+ Max. :10.706 Max. :10.709 Max. :11.5956 Max. :11.3505
+ HSPC_725 HSPC_727 HSPC_729 HSPC_730
+ Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 1.577 Median : 1.576 Median : 0.9579 Median : 1.135
+ Mean : 2.483 Mean : 2.436 Mean : 2.2448 Mean : 2.445
+ 3rd Qu.: 3.741 3rd Qu.: 3.447 3rd Qu.: 2.7343 3rd Qu.: 3.475
+ Max. :10.647 Max. :11.512 Max. :10.9657 Max. :11.121
+ HSPC_731 HSPC_732 HSPC_733 HSPC_734
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 1.130 Median : 0.6937 Median : 1.436 Median : 0.7333
+ Mean : 2.854 Mean : 2.1051 Mean : 2.489 Mean : 2.5404
+ 3rd Qu.: 6.019 3rd Qu.: 2.0311 3rd Qu.: 3.738 3rd Qu.: 5.6282
+ Max. :10.471 Max. :11.0494 Max. :10.929 Max. :10.4547
+ HSPC_735 HSPC_736 HSPC_737 HSPC_738
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.033 Median : 0.6789 Median : 1.185 Median : 1.514
+ Mean : 2.389 Mean : 2.0224 Mean : 2.722 Mean : 2.503
+ 3rd Qu.: 3.056 3rd Qu.: 2.0017 3rd Qu.: 5.669 3rd Qu.: 3.602
+ Max. :10.866 Max. :11.8100 Max. :11.076 Max. :10.473
+ HSPC_740 HSPC_742 HSPC_743 HSPC_744
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.8437 Median : 1.122 Median : 1.213
+ Mean : 2.506 Mean : 1.8949 Mean : 2.028 Mean : 2.048
+ 3rd Qu.: 3.794 3rd Qu.: 1.7586 3rd Qu.: 2.840 3rd Qu.: 2.309
+ Max. :10.618 Max. :11.6327 Max. :10.449 Max. :10.598
+ HSPC_745 HSPC_746 HSPC_747 HSPC_748
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 2.403 Median : 2.184 Median : 0.000 Median : 1.181
+ Mean : 2.309 Mean : 2.153 Mean : 2.543 Mean : 2.017
+ 3rd Qu.: 3.793 3rd Qu.: 3.016 3rd Qu.: 4.751 3rd Qu.: 2.264
+ Max. :10.882 Max. :10.988 Max. :10.860 Max. :12.153
+ HSPC_749 HSPC_750 HSPC_751 HSPC_752
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.444 Median : 1.030 Median : 1.567 Median : 2.228
+ Mean : 2.477 Mean : 2.370 Mean : 2.416 Mean : 2.529
+ 3rd Qu.: 3.501 3rd Qu.: 3.052 3rd Qu.: 3.435 3rd Qu.: 3.976
+ Max. :11.391 Max. :11.167 Max. :10.239 Max. :10.586
+ HSPC_753 HSPC_755 HSPC_756 HSPC_757
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.062 Median : 0.740 Median : 1.731 Median : 1.395
+ Mean : 2.313 Mean : 2.102 Mean : 2.592 Mean : 2.477
+ 3rd Qu.: 2.961 3rd Qu.: 2.509 3rd Qu.: 4.107 3rd Qu.: 3.253
+ Max. :11.202 Max. :10.559 Max. :10.783 Max. :10.973
+ HSPC_758 HSPC_759 HSPC_760 HSPC_761
+ Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 0.8648 Median : 0.9415 Median : 1.052 Median : 0.6917
+ Mean : 2.6819 Mean : 2.1274 Mean : 2.288 Mean : 2.2992
+ 3rd Qu.: 4.7233 3rd Qu.: 2.2271 3rd Qu.: 2.404 3rd Qu.: 2.6015
+ Max. :11.1096 Max. :11.2534 Max. :11.008 Max. :11.7228
+ HSPC_762 HSPC_764 HSPC_765 HSPC_766
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 1.271 Median : 1.784 Median : 2.116 Median : 0.9828
+ Mean : 2.242 Mean : 2.068 Mean : 2.100 Mean : 2.1721
+ 3rd Qu.: 2.734 3rd Qu.: 3.059 3rd Qu.: 2.939 3rd Qu.: 2.6115
+ Max. :12.043 Max. :11.003 Max. :12.757 Max. :10.2002
+ HSPC_767 HSPC_768 HSPC_769 HSPC_770
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.6646 Median : 1.703 Median : 0.000 Median : 1.760
+ Mean : 1.9552 Mean : 2.365 Mean : 2.080 Mean : 2.343
+ 3rd Qu.: 1.9730 3rd Qu.: 3.325 3rd Qu.: 3.289 3rd Qu.: 3.122
+ Max. :11.3033 Max. :10.958 Max. :11.176 Max. :10.497
+ HSPC_771 HSPC_772 HSPC_773 HSPC_774
+ Min. : 0.000 Min. : 0.000 Min. : 0.0000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0000
+ Median : 1.178 Median : 1.601 Median : 0.9901 Median : 0.9736
+ Mean : 2.527 Mean : 2.283 Mean : 1.8628 Mean : 2.5263
+ 3rd Qu.: 3.342 3rd Qu.: 2.828 3rd Qu.: 1.9851 3rd Qu.: 5.4694
+ Max. :11.156 Max. :10.625 Max. :10.7274 Max. :10.7701
+ HSPC_776 HSPC_777 HSPC_778 HSPC_780
+ Min. : 0.000 Min. : 0.000 Min. :0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 1.053 Median :1.435 Median : 1.178
+ Mean : 2.315 Mean : 2.110 Mean :2.130 Mean : 2.476
+ 3rd Qu.: 3.788 3rd Qu.: 2.673 3rd Qu.:3.488 3rd Qu.: 3.769
+ Max. :11.105 Max. :11.646 Max. :9.535 Max. :11.265
+ HSPC_781 HSPC_782 HSPC_783 HSPC_784
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.050
+ Mean : 1.911 Mean : 2.416 Mean : 2.254 Mean : 2.175
+ 3rd Qu.: 2.884 3rd Qu.: 3.872 3rd Qu.: 2.548 3rd Qu.: 2.468
+ Max. :11.445 Max. :10.161 Max. :10.970 Max. :10.958
+ HSPC_785 HSPC_786 HSPC_787 HSPC_788
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 0.000 Median : 1.148 Median : 0.000 Median : 0.9386
+ Mean : 2.230 Mean : 2.467 Mean : 2.100 Mean : 1.9749
+ 3rd Qu.: 2.466 3rd Qu.: 3.899 3rd Qu.: 2.991 3rd Qu.: 2.6662
+ Max. :11.041 Max. :11.080 Max. :10.690 Max. :11.1078
+ HSPC_789 HSPC_790 HSPC_791 HSPC_794
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.181 Median : 1.353 Median : 1.790 Median : 1.113
+ Mean : 2.225 Mean : 2.255 Mean : 2.699 Mean : 2.225
+ 3rd Qu.: 2.876 3rd Qu.: 2.852 3rd Qu.: 4.931 3rd Qu.: 2.768
+ Max. :11.245 Max. :11.558 Max. :11.104 Max. :11.118
+ HSPC_795 HSPC_796 HSPC_797 HSPC_798
+ Min. : 0.0000 Min. : 0.0000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.8317 Median : 0.7001 Median : 0.8722 Median : 1.531
+ Mean : 2.3985 Mean : 2.6865 Mean : 2.6172 Mean : 2.485
+ 3rd Qu.: 3.4461 3rd Qu.: 5.6688 3rd Qu.: 5.3078 3rd Qu.: 3.098
+ Max. :11.0956 Max. :11.0829 Max. :11.4339 Max. :10.933
+ HSPC_799 HSPC_800 HSPC_801 HSPC_802
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 1.033
+ Mean : 2.179 Mean : 2.173 Mean : 2.427 Mean : 2.613
+ 3rd Qu.: 3.517 3rd Qu.: 2.865 3rd Qu.: 4.665 3rd Qu.: 3.780
+ Max. :11.666 Max. :11.263 Max. :10.905 Max. :10.864
+ HSPC_803 HSPC_804 HSPC_806 HSPC_807
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 0.000 Median : 0.000 Median : 2.103 Median : 0.7501
+ Mean : 2.395 Mean : 2.301 Mean : 2.222 Mean : 2.2476
+ 3rd Qu.: 3.883 3rd Qu.: 3.167 3rd Qu.: 3.445 3rd Qu.: 2.3481
+ Max. :10.766 Max. :11.298 Max. :10.326 Max. :11.2700
+ HSPC_808 HSPC_809 HSPC_810 HSPC_812
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Median : 0.6619 Median : 1.788 Median : 1.651 Median : 0.8459
+ Mean : 2.1677 Mean : 2.544 Mean : 2.471 Mean : 2.2960
+ 3rd Qu.: 2.5355 3rd Qu.: 3.730 3rd Qu.: 3.662 3rd Qu.: 2.6906
+ Max. :10.9302 Max. :11.791 Max. :10.829 Max. :11.5500
+ HSPC_813 HSPC_814 HSPC_815 HSPC_816
+ Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.6278 Median : 1.110 Median : 0.9631 Median : 1.346
+ Mean : 2.2448 Mean : 2.762 Mean : 2.4587 Mean : 2.341
+ 3rd Qu.: 2.3066 3rd Qu.: 5.996 3rd Qu.: 3.4228 3rd Qu.: 2.842
+ Max. :12.0043 Max. :10.406 Max. :11.4527 Max. :11.151
+ HSPC_818 HSPC_819 HSPC_820 HSPC_821
+ Min. : 0.0000 Min. : 0.000 Min. : 0.0000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000
+ Median : 0.9967 Median : 1.365 Median : 0.8099 Median : 1.382
+ Mean : 2.3081 Mean : 2.426 Mean : 2.1063 Mean : 2.532
+ 3rd Qu.: 2.9942 3rd Qu.: 3.632 3rd Qu.: 2.4643 3rd Qu.: 3.462
+ Max. :11.9931 Max. :10.672 Max. :11.2412 Max. :12.126
+ HSPC_822 HSPC_824 HSPC_825 HSPC_826
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.387 Median : 0.000 Median : 1.386 Median : 1.324
+ Mean : 2.503 Mean : 2.084 Mean : 2.162 Mean : 2.398
+ 3rd Qu.: 3.799 3rd Qu.: 2.342 3rd Qu.: 2.897 3rd Qu.: 3.150
+ Max. :11.892 Max. :11.365 Max. :11.498 Max. :11.198
+ HSPC_827 HSPC_828 HSPC_831 HSPC_832
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.746 Median : 1.003 Median : 1.304 Median : 1.035
+ Mean : 2.239 Mean : 2.145 Mean : 2.589 Mean : 2.384
+ 3rd Qu.: 2.638 3rd Qu.: 2.326 3rd Qu.: 3.866 3rd Qu.: 3.450
+ Max. :12.101 Max. :10.710 Max. :10.839 Max. :10.686
+ HSPC_833 HSPC_834 HSPC_835 HSPC_836
+ Min. : 0.0000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.7166 Median : 0.9245 Median : 1.006 Median : 0.000
+ Mean : 2.3553 Mean : 2.0872 Mean : 2.552 Mean : 2.471
+ 3rd Qu.: 3.9364 3rd Qu.: 2.4568 3rd Qu.: 4.034 3rd Qu.: 3.994
+ Max. :11.1695 Max. :11.1803 Max. :11.779 Max. :11.316
+ HSPC_837 HSPC_838 HSPC_839 HSPC_840
+ Min. : 0.000 Min. : 0.0000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.312 Median : 0.9838 Median : 1.083 Median : 1.867
+ Mean : 2.590 Mean : 2.5281 Mean : 2.380 Mean : 2.548
+ 3rd Qu.: 4.443 3rd Qu.: 3.5551 3rd Qu.: 3.743 3rd Qu.: 3.609
+ Max. :10.672 Max. :11.2707 Max. :10.966 Max. :10.867
+ HSPC_841 HSPC_842 HSPC_843 HSPC_844
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.439 Median : 1.774 Median : 1.257 Median : 1.584
+ Mean : 2.408 Mean : 2.380 Mean : 2.845 Mean : 2.627
+ 3rd Qu.: 3.494 3rd Qu.: 3.490 3rd Qu.: 6.768 3rd Qu.: 3.951
+ Max. :10.930 Max. :11.137 Max. :11.933 Max. :11.446
+ HSPC_845 HSPC_846 HSPC_848 HSPC_849
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 1.227 Median : 1.401 Median : 0.000 Median : 1.602
+ Mean : 2.464 Mean : 2.240 Mean : 2.152 Mean : 2.402
+ 3rd Qu.: 3.377 3rd Qu.: 2.920 3rd Qu.: 2.554 3rd Qu.: 2.920
+ Max. :10.535 Max. :11.519 Max. :11.266 Max. :11.678
+ HSPC_851 HSPC_852
+ Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000
+ Mean : 2.319 Mean : 2.143
+ 3rd Qu.: 3.373 3rd Qu.: 2.901
+ Max. :11.602 Max. :11.469
+
+
+
Hmmmm, did you get all that? Nope, me neither! We have 701 cells but we only have 6 samples for the frogs. Letβs take a look at the first few columns
ensembl_gene_id HSPC_001 HSPC_002 HSPC_003
+ Length:280 Min. : 0.000 Min. : 0.000 Min. : 0.0000
+ Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.0000
+ Mode :character Median : 0.000 Median : 0.000 Median : 0.9929
+ Mean : 2.143 Mean : 1.673 Mean : 2.5964
+ 3rd Qu.: 2.120 3rd Qu.: 2.239 3rd Qu.: 6.1559
+ Max. :12.567 Max. :11.976 Max. :11.1138
+ HSPC_004 HSPC_006 HSPC_008 HSPC_009
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. :0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000
+ Median : 0.000 Median : 1.276 Median : 0.000 Median :0.000
+ Mean : 1.851 Mean : 2.338 Mean : 2.375 Mean :2.220
+ 3rd Qu.: 2.466 3rd Qu.: 3.536 3rd Qu.: 3.851 3rd Qu.:3.594
+ Max. :11.133 Max. :10.014 Max. :11.574 Max. :9.997
+ HSPC_011 HSPC_012 HSPC_014 HSPC_015
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 1.750 Median : 0.000 Median : 0.000
+ Mean : 2.285 Mean : 2.431 Mean : 2.295 Mean : 2.515
+ 3rd Qu.: 3.193 3rd Qu.: 3.741 3rd Qu.: 3.150 3rd Qu.: 3.789
+ Max. :11.260 Max. :10.905 Max. :11.051 Max. :10.751
+ HSPC_016 HSPC_017 HSPC_018 HSPC_020
+ Min. : 0.0000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.9488 Median : 0.000 Median : 1.248 Median : 0.000
+ Mean : 2.6115 Mean : 2.146 Mean : 2.710 Mean : 2.509
+ 3rd Qu.: 5.9412 3rd Qu.: 2.357 3rd Qu.: 6.006 3rd Qu.: 4.470
+ Max. :11.3082 Max. :12.058 Max. :11.894 Max. :11.281
+ HSPC_021 HSPC_022 HSPC_023 HSPC_024
+ Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
+ 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.000
+ Median : 0.000 Median : 0.000 Median : 0.000 Median : 0.000
+ Mean : 2.170 Mean : 2.287 Mean : 2.314 Mean : 2.195
+ 3rd Qu.: 2.996 3rd Qu.: 3.351 3rd Qu.: 2.749 3rd Qu.: 2.944
+ Max. :10.709 Max. :11.814 Max. :12.113 Max. :11.279
+
+
+
Notice that:
+
the maximum value is much less high and has decimals. That is because the mouse data are logged (to base 2) normalised counts, not raw counts as they are in the frog data set.
+
the minimum count is 0
+
at least some of the medians are zeros 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 distribution of values in cell cell using facet_wrap().
+
In this data set, there is even more of an advantage of using the pivot_longer(), group_by() and summarise() approach. We will be to open the dataframe in the Viewer and make plots to examine whether the distributions are similar across cells.
Notice that I have used cell as the column name rather than sample and expr (expression) rather than count. Iβve also added the standard deviation.
+
π¬ View the hspc_summary dataframe
+
All cells have quite a few zeros and the lower quartile is 0 for al cells, i.e., every cell has many genes with zero expression.
+
To get a better understanding of the distribution of expressions in cells we can create a ggplot using the pointrange geom. Pointrange puts a dot at the mean and a line between a minimum and a maximum such as +/-s.d. Not unlike a boxplot but when you need the boxes too be very narrow!
You will need to use the Zoom button to pop the plot window out so you can make it as wide as possible
+
The things to notice are:
+
+
the 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.
+
the distributions are roughly similar in width too
+
+
The default order of cell is alphabetical. It can be easier to see these (non) effects if we order the lines by size
+
π¬ Order a pointrange plot with reorder(variable_to_order, order_by).
reorder() arranges cell in increasing size of mean
+
It is more important to remove odd samples/cells when you have relatively few of them.
+
π¬ Write hspc_summary to a file called βhspc_summary.csvβ:
+
Distribution of values across the genes
+
πΈ Frog genes
+
π Mouse genes
+
Filtering for QC
+
πΈ Frog filtering
+
all samples are fine; some genes with low counts should be removed
+
+
low counts overall
+
low counts in βseveralβ of the samples
+
additional filtering on results of DE
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π Mouse filtering
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Look after future you
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You need only do the section for your own project data
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πΈ Frogs and future you
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π¬ 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.
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π Mice and future you
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π¬ 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. 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.
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NOTES - to be checked removed once drafted
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π¬ Make a note of the cut off value that seems appropriate. Iβm going to use 10 (i.e., 1 on the graph). I think 3 (0.5 on the graph) would be a reasonable choice too. Donβt attempt to be over precise. - what kind of values are they, how many missing - qc plots - removing the useless, writing to files
These contain all the code needed in the workshop even where it is not visible on the webpage.
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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---
+R Core Team. 2023. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
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