diff --git a/omics/week-4/data-processed/hspc_summary_gene.csv b/omics/week-4/data-processed/hspc_summary_gene.csv
new file mode 100644
index 0000000..f2a9d9e
--- /dev/null
+++ b/omics/week-4/data-processed/hspc_summary_gene.csv
@@ -0,0 +1,281 @@
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diff --git a/omics/week-4/data-processed/hspc_summary_samp.csv b/omics/week-4/data-processed/hspc_summary_samp.csv
new file mode 100644
index 0000000..e7d7f33
--- /dev/null
+++ b/omics/week-4/data-processed/hspc_summary_samp.csv
@@ -0,0 +1,702 @@
+cell,min,lowerq,mean,median,sd,upperq,max,n_zero
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diff --git a/omics/week-4/data-processed/prog_summary_gene.csv b/omics/week-4/data-processed/prog_summary_gene.csv
new file mode 100644
index 0000000..93c095c
--- /dev/null
+++ b/omics/week-4/data-processed/prog_summary_gene.csv
@@ -0,0 +1,281 @@
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diff --git a/omics/week-4/data-processed/prog_summary_samp.csv b/omics/week-4/data-processed/prog_summary_samp.csv
new file mode 100644
index 0000000..f5e501e
--- /dev/null
+++ b/omics/week-4/data-processed/prog_summary_samp.csv
@@ -0,0 +1,799 @@
+cell,min,lowerq,mean,median,sd,upperq,max,n_zero
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diff --git a/omics/week-4/data-processed/s20_filtered.csv b/omics/week-4/data-processed/s20_filtered.csv
new file mode 100644
index 0000000..3d4b39e
--- /dev/null
+++ b/omics/week-4/data-processed/s20_filtered.csv
@@ -0,0 +1,10132 @@
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diff --git a/omics/week-4/data-processed/s20_summary_gene.csv b/omics/week-4/data-processed/s20_summary_gene.csv
new file mode 100644
index 0000000..4e13faa
--- /dev/null
+++ b/omics/week-4/data-processed/s20_summary_gene.csv
@@ -0,0 +1,11894 @@
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diff --git a/omics/week-4/data-processed/s20_summary_gene_filtered.csv b/omics/week-4/data-processed/s20_summary_gene_filtered.csv
new file mode 100644
index 0000000..3acbfe7
--- /dev/null
+++ b/omics/week-4/data-processed/s20_summary_gene_filtered.csv
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diff --git a/omics/week-4/data-processed/s30_filtered.csv b/omics/week-4/data-processed/s30_filtered.csv
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diff --git a/omics/week-4/data-processed/s30_summary_gene.csv b/omics/week-4/data-processed/s30_summary_gene.csv
new file mode 100644
index 0000000..307dcec
--- /dev/null
+++ b/omics/week-4/data-processed/s30_summary_gene.csv
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diff --git a/omics/week-4/data-processed/s30_summary_gene_filtered.csv b/omics/week-4/data-processed/s30_summary_gene_filtered.csv
new file mode 100644
index 0000000..62bb0b6
--- /dev/null
+++ b/omics/week-4/data-processed/s30_summary_gene_filtered.csv
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diff --git a/omics/week-4/data-processed/s30_summary_samp.csv b/omics/week-4/data-processed/s30_summary_samp.csv
new file mode 100644
index 0000000..8042175
--- /dev/null
+++ b/omics/week-4/data-processed/s30_summary_samp.csv
@@ -0,0 +1,7 @@
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diff --git a/omics/week-4/data-raw/surfaceome_hspc.csv b/omics/week-4/data-raw/surfaceome_hspc.csv
new file mode 100644
index 0000000..080c765
--- /dev/null
+++ b/omics/week-4/data-raw/surfaceome_hspc.csv
@@ -0,0 +1,281 @@
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diff --git a/omics/week-4/data-raw/surfaceome_lthsc.csv b/omics/week-4/data-raw/surfaceome_lthsc.csv
new file mode 100644
index 0000000..460a05b
--- /dev/null
+++ b/omics/week-4/data-raw/surfaceome_lthsc.csv
@@ -0,0 +1,281 @@
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diff --git a/omics/week-4/data-raw/surfaceome_prog.csv b/omics/week-4/data-raw/surfaceome_prog.csv
new file mode 100644
index 0000000..1774a10
--- /dev/null
+++ b/omics/week-4/data-raw/surfaceome_prog.csv
@@ -0,0 +1,281 @@
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diff --git a/omics/week-4/data-raw/xlaevis_counts_S14.csv b/omics/week-4/data-raw/xlaevis_counts_S14.csv
new file mode 100644
index 0000000..65162c3
--- /dev/null
+++ b/omics/week-4/data-raw/xlaevis_counts_S14.csv
@@ -0,0 +1,11894 @@
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diff --git a/omics/week-4/data-raw/xlaevis_counts_S20.csv b/omics/week-4/data-raw/xlaevis_counts_S20.csv
new file mode 100644
index 0000000..17c1964
--- /dev/null
+++ b/omics/week-4/data-raw/xlaevis_counts_S20.csv
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diff --git a/omics/week-4/data-raw/xlaevis_counts_S30.csv b/omics/week-4/data-raw/xlaevis_counts_S30.csv
new file mode 100644
index 0000000..fe0770c
--- /dev/null
+++ b/omics/week-4/data-raw/xlaevis_counts_S30.csv
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+XB-GENE-999426,134,138,159,152,135,92
+XB-GENE-999443,29,29,80,38,34,30
+XB-GENE-999454,227,284,392,273,348,259
+XB-GENE-999469,226,305,363,308,331,261
+XB-GENE-999480,344,326,405,440,306,245
+XB-GENE-999491,252,286,424,259,315,252
+XB-GENE-999501,21,38,98,65,62,60
+XB-GENE-999511,55,51,126,65,85,91
+XB-GENE-999523,72,71,103,66,60,66
+XB-GENE-999533,3,6,7,9,5,8
+XB-GENE-999551,39,16,18,25,13,8
+XB-GENE-999572,41,35,68,38,33,25
+XB-GENE-999689,317,350,542,416,434,322
+XB-GENE-999702,110,117,161,158,122,74
+XB-GENE-999720,102,126,197,118,167,84
+XB-GENE-999725,8,8,6,8,11,3
+XB-GENE-999738,1473,1353,1553,1675,1340,1066
+XB-GENE-999750,37,69,73,66,71,36
+XB-GENE-999804,267,325,406,333,295,225
+XB-GENE-999823,283,282,368,324,302,231
+XB-GENE-999859,62,58,89,88,61,47
+XB-GENE-999870,32,42,87,78,68,60
+XB-GENE-999882,701,999,1406,876,1097,903
+XB-GENE-999891,60,55,67,87,41,46
+XB-GENE-999902,178,121,165,180,131,98
+XB-GENE-999915,54,52,96,106,73,85
+XB-GENE-999933,757,838,1183,1003,890,647
+XB-GENE-999960,780,735,902,1003,813,574
+XB-GENE-999979,193,193,275,339,210,278
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diff --git a/omics/week-4/overview.qmd b/omics/week-4/overview.qmd
new file mode 100644
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+++ b/omics/week-4/overview.qmd
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+---
+title: "Overview"
+subtitle: "Omics 1: 👋 Hello data!"
+toc: true
+toc-location: right
+---
+
+This week you will meet your data. The independent study will concisely cover how these data were generated and how they have been processed before being given to you. There will also be an overview of the analysis we will carry out over three workshops.
+In the workshop, you will learn what steps to take to get a good understanding of ’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.
+
+We suggest you sit together with your group in the workshop.
+
+### Learning objectives
+
+The successful student will be able to:
+
+-   explore 'omics data to find the number of rows and columns and know how these correspond to samples and variables
+-   explore the distribution of expression measures across whole data sets, across variables and across samples by summarising and plotting
+-   explain what distributions are expected and interpret the distributions they have
+-   explain on what basis we might filter out variables or samples
+-   import, explore and filter 'omics data reproducibly so they can understand and reuse their code in the future
+
+### Instructions
+
+1.  [Prepare](study_before_workshop.qmd)
+    
+    i.   📖 Read how the data were generated and how they have been processed so far, install 
+
+
+2.  [Workshop](workshop.qmd)
+
+    i.   💻 Set up a Project
+
+    ii.  💻 Import data
+
+    iii. 💻 Explore the distribution of values across samples/cells and across genes/species
+    
+    iv.  💻 Perform quality control
+    
+    v.   💻 Look after future you!
+
+3.  [Consolidate](study_after_workshop.qmd)
+
+    i.  💻 Use the work you completed in the workshop as a template to apply to a new case.
+
+
diff --git a/omics/week-4/study_after_workshop.qmd b/omics/week-4/study_after_workshop.qmd
new file mode 100644
index 0000000..d88995e
--- /dev/null
+++ b/omics/week-4/study_after_workshop.qmd
@@ -0,0 +1,30 @@
+---
+title: "Independent Study to consolidate this week"
+subtitle: "Core 1"
+toc: true
+toc-location: right
+format:
+  html:
+    code-fold: true
+    code-summary: "Answer - don't look until you have tried!"
+---
+
+
+You need only do the section for your own project data
+
+🐸 Frogs
+
+🎬 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.
+
+
+🐭 Mice
+
+🎬 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.
+
+
+🍂 xxxx
+
+🎬 Open your `xxxx-88H` Project. Make a new script and, using `xxxxxxx.R` as a template, repeat the analysis on xxxxxxxx
+
+
+
diff --git a/omics/week-4/study_before_workshop.qmd b/omics/week-4/study_before_workshop.qmd
new file mode 100644
index 0000000..89d226b
--- /dev/null
+++ b/omics/week-4/study_before_workshop.qmd
@@ -0,0 +1,271 @@
+---
+title: "Independent Study to prepare for workshop"
+subtitle: "Omics 1: 👋 Hello data!"
+author: "Emma Rand"
+format: 
+  revealjs:
+    footer: "🔗 [About Omics 1: Hello data!](https://3mmarand.github.io/BIO00088H-data/omics/week-3/overview.html)"
+    slide-number: true
+    chalkboard: true
+    code-link: true
+    code-line-numbers: false
+bibliography: ../../references.bib
+editor: 
+  markdown: 
+    wrap: 72
+---
+
+## Overview
+
+::: incremental
+-   Concise summary of the experimental design and aims
+
+-   What the raw data consist of
+
+-   What has been done to the data so far
+
+-   What steps we will take in the workshop
+:::
+
+## The Data
+
+There are three datasets
+
+-   🐸 transcriptomic data (bulk RNA-seq) from frog embryos.
+
+-   🐭 transcriptomic data (single cell RNA-seq) from stemcells
+
+-   🍂 ??????? Metabolomic / Metagenomic data from anaerobic digesters
+
+# Experimental design
+
+## 🐸 Experimental design {auto-animate="true"}
+
+![Schematic of frog development
+experiment](images/88H-exp-design-betsy.png){fig-align="center"
+width="700"}
+
+## 🐸 Experimental design {auto-animate="true"}
+
+![Schematic of frog development
+experiment](images/88H-exp-design-betsy.png){fig-align="left"
+width="200"}
+
+::: incremental
+-   3 fertilisations
+
+-   two siblings from each fertilisation one control, on FGF treated
+
+-   sequenced at three time points
+
+-   3 x 2 x 3 = 18 groups
+:::
+
+## 🐸 Experimental design {auto-animate="true"}
+
+![Schematic of frog development
+experiment](images/88H-exp-design-betsy.png){fig-align="left"
+width="200"}
+
+::: incremental
+-   3 fertilisations. [These are the replicates, `.5`, `.6`,
+    `A`]{style="color:#009900"}
+
+-   two siblings from each fertilisation one control, one FGF treated.
+    [The treatments are paired]{style="color:#009900"}
+
+-   sequenced at three time points. [S14, S20,
+    S30]{style="color:#009900"}
+
+-   3 x 2 x 3 = 18 groups
+:::
+
+## 🐸 Aim
+
+::: incremental
+-   find genes important in frog development
+
+-   Important means the genes that are differentially expressed between
+    the control-treated and the FGF-treated siblings
+
+-   Differentially expressed means the expression in one group is
+    significantly higher than in the other
+:::
+
+## 🐸 Guided analysis
+
+::: incremental
+-   The workshops will take you through comparing the control and FGF
+    treated sibling at S30
+
+-   This is the "least interesting" comparison
+
+-   You will be guided to carefully document your work so you can apply
+    the same methods to other comparisons
+:::
+
+## 🐭 Experimental design {auto-animate="true"}
+
+![Schematic of stem cell
+experiment](images/88H-exp-design-jillian.png){fig-align="center"
+width="700"}
+
+## 🐭 Experimental design {auto-animate="true"}
+
+![Schematic of stem cell
+experiment](images/88H-exp-design-jillian.png){fig-align="left"
+width="200"}
+
+::: incremental
+-   Cells were sorted using flow cytometry on the basis of cell surface
+    markers
+
+-   There are three cell types: LT-HSCs, HSPCs, Progs
+
+-   Many cells of each cell type were sequenced
+:::
+
+## 🐭 Experimental design {auto-animate="true"}
+
+![Schematic of stem cell
+experiment](images/88H-exp-design-jillian.png){fig-align="left"
+width="200"}
+
+::: incremental
+-   There are three cell types: LT-HSCs, HSPCs, Progs [These are the
+    "treaments"]{style="color:#009900"}
+
+-   Many cells of each type were sequenced: [These are the
+    replicates]{style="color:#009900"}
+
+-   [155 LT-HSCs, 701 HSPCs, 798 Progs]{style="color:#009900"}
+:::
+
+## 🐭 Aim
+
+::: incremental
+-   find genes for cell surface proteins that are important in stem cell
+    identity
+
+-   Important means genes that are differentially expressed between at
+    least two cell types
+
+-   Differentially expressed means the expression in one group is
+    significantly higher than in the other
+:::
+
+## 🐭 Guided analysis
+
+::: incremental
+-   The workshops will take you through comparing the HSPC and Prog
+    cells
+
+-   This is the "least interesting" comparison
+
+-   You will be guided to carefully document your work so you can apply
+    the same methods to other comparisons
+:::
+
+# The raw data
+
+## Raw Sequence data
+
+::: incremental
+-   The raw data are "reads" from a sequencing machine.
+
+-   A read is sequence of DNA or RNA shorter than the whole genome or
+    transcriptome
+
+-   The length of the reads depends on the type of sequencing machine
+
+    -   Short-read technologies e.g. Illumina have higher base accuracy
+        but are harder to align
+    -   Long-read technologies e.g. Nanopore have lower base accuracy
+        but are easier to align
+:::
+
+# The raw data
+
+## Raw Sequence data
+
+::: incremental
+-   Sequencing technology is constantly improving
+
+-   Optional: You can read more about Sequencing technologies in
+    [Statistically useful experimental
+    design](https://cloud-span.github.io/experimental_design00-overview/)
+    [@rand_statistically_2022]
+:::
+
+## Raw Sequence data
+
+::: incremental
+-   The RNA-seq data are from an Illumina machine 150-300bp; Metagenomic
+    data are often Nanopore 10,000 - 30000bp
+
+-   Reads are in FASTQ files
+
+-   FASTQ files contain the sequence of each read and a quality score
+    for each base
+:::
+
+# What has been done to the data so far
+
+## General steps
+
+::: incremental
+-   Reads are filtered and trimmed on the basis of the quality score
+
+-   They are then aligned/pseudo-aligned to a reference
+    genome/transcriptome or, in metagenomics, assembled de novo.
+
+-   Reads are then counted to quantify the expression or number of
+    genomes in metagenomics
+
+-   Counts are normalised to account for differences in sequencing depth
+    and gene/transcript/genome length before statistical analysis
+:::
+
+## 🐸 Data
+
+-   Unpublished (so far!)
+
+-   Expression for the whole transcriptome [*X. laevis* v10.1 genome
+    assembly](https://www.xenbase.org/xenbase/static-xenbase/ftpDatafiles.jsp)
+
+-   Values are raw counts
+
+-   The statistical analysis method we will use `DESeq2` [@DESeq2]
+    requires raw counts and performs the normalisation itself
+
+## 🐭 Data
+
+-   Published in @nestorowa2016
+
+-   Expression for a subset of genes, the surfaceome
+
+-   Values are log2 normalised values
+
+-   The statistical analysis method we will use `scran` [@scran]
+    requires normalised values
+
+# Workshops
+
+## Workshops
+
+-   Omics 1: Hello data Getting to know the data. Checking the
+    distributions of values overall, across samples and across genes to
+    check things are as we expect and detect genes/samples that need to
+    be removed
+
+-   Omics 2: Statistical Analysis Identifying which genes are
+    differentially expressed between treatments. This is the main
+    analysis step. We will use different methods for bulk and single
+    cell data.
+
+-   Omics 3: Visualising and Interpreting Production of volcano plots
+    and heatmaps to visualise the results of the statistical analysis.
+    We will also look at how to interpret the results and how to find
+    out more about the genes of interest.
+
+## References
diff --git a/omics/week-4/workshop.qmd b/omics/week-4/workshop.qmd
new file mode 100644
index 0000000..b4c51a2
--- /dev/null
+++ b/omics/week-4/workshop.qmd
@@ -0,0 +1,788 @@
+---
+title: "Workshop"
+subtitle: "Omics 1: Hello data!"
+author: "Emma Rand"
+toc: true
+toc-depth: 4
+toc-location: right
+execute:
+  echo: true
+  include: true
+  error: true
+bibliography: ../../references.bib
+editor: 
+  markdown: 
+    wrap: 72
+---
+
+# 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
+
+🎬 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`
+
+🎬 Make a new script called `workshop-1.R` to carry out the rest of the
+work.
+
+🎬 Record what you do and what you find out. All of it!
+
+🎬 Load `tidyverse` [@tidyverse] for importing, summarising, plotting
+and filtering.
+
+```{r}
+library(tidyverse)
+```
+
+## Examine the data in a spreadsheet
+
+These are the three datasets. Each set compromises several files.
+
+🐸 Frog development data:
+
+-   [xlaevis_counts_S14.csv](data-raw/xlaevis_counts_S14.csv)
+-   [xlaevis_counts_S20.csv](data-raw/xlaevis_counts_S20.csv)
+-   [xlaevis_counts_S30.csv](data-raw/xlaevis_counts_S30.csv)
+-   [meta_data.txt](data-raw/meta_data.txt)
+
+🐭 Stem cell data:
+
+-   [surfaceome_hspc.csv](data-raw/surfaceome_hspc.csv)
+-   [surfaceome_prog.csv](data-raw/surfaceome_prog.csv)
+-   [surfaceome_lthsc.csv](data-raw/surfaceome_lthsc.csv)
+
+🍂 xxxx data:
+
+-   xxx
+-   xxx
+
+🎬 Save the files to `data-raw` and open them in Excel
+
+🎬 Answer the following questions:
+
+-   Describe how the sets of data are similar and how they are
+    different.
+-   What is in the rows and columns of each file?
+-   How many rows and columns are there in each file? Are these the
+    same? In all cases or some cases? Why?
+-   Google an id. Where does your search take you? How much information
+    is available?
+
+🎬 Did you record all that??
+
+## Import
+
+Now let's get the data into R and visualise it.
+
+🎬 Import [xlaevis_counts_S30.csv](data-raw/xlaevis_counts_S30.csv),
+[surfaceome_hspc.csv](data-raw/surfaceome_hspc.csv) and xxxxxxxx
+
+```{r}
+# 🐸 import the s30 data
+s30 <- read_csv("data-raw/xlaevis_counts_S30.csv")
+```
+
+```{r}
+# 🐭 import the hspc data
+hspc <- read_csv("data-raw/surfaceome_hspc.csv")
+```
+
+```{r}
+# 🍂 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
+
+The first task is to get an overview. We want to know
+
+-   are there any missing values? If so, how many and how are they
+    distributed?
+-   how may zeros are there and how are they distributed
+-   does it look as tough all the samples/cells were equally
+    "successful"? Can we spot any problematic anomalies?
+-   what is the distribution of values?
+
+If our data collection has gone well we would hope to see approximately
+the same average expression in each sample or cell of the same type.
+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:
+
+-   The distribution of values across the whole dataset
+
+-   The distribution of values across the sample/cells (i.e., averaged
+    across genes). This allows us to see variation between
+    samples/cells:
+
+-   The distribution of values across the genes (i.e., averaged across
+    samples/cells). This allows us to see variation between genes.
+
+### 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](https://3mmarand.github.io/BIO00017C-Data-Analysis-in-R-2020/workshops/02TestingDataTypesReadingInData.html#Tidy_format)
+[@Wickham2014-nl] 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:
+
+```{r}
+s30 |>
+  pivot_longer(cols = -xenbase_gene_id,
+               names_to = "sample",
+               values_to = "count") |>
+  ggplot(aes(x = count)) +
+  geom_histogram()
+```
+
+This data is very skewed - there are so many low values that we can't
+see 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.
+
+```{r}
+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 genome[^1]. That small peak
+near the low end suggests that these lower counts might be anomalies.
+
+[^1]: This a result of the [Central limit
+    theorem](https://en.wikipedia.org/wiki/Central_limit_theorem),one
+    consequence of which is that adding together lots of distributions -
+    whatever distributions they are - will tend to a normal
+    distribution.
+
+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:
+
+```{r}
+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 distribution[^2]. 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.
+
+[^2]: This a result of the [Central limit
+    theorem](https://en.wikipedia.org/wiki/Central_limit_theorem),one
+    consequence of which is that adding together lots of distributions -
+    whatever distributions they are - will tend to a normal
+    distribution.
+
+### 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:
+
+```{r}
+# examine all the columns quickly
+# works well with smaller numbers of column
+summary(s30)
+```
+
+Notice that: - the minimum count is 0 and the maximums are very high in
+all the columns - the medians are quite a lot lower than the means so
+the data are skewed (hump to the left, tail to the right) - there must
+be quite a lot of zeros - the columns are roughly similar and it doesn't
+look like there is an anomalous replicate.
+
+To find out how may zeros there are in a column we can make use of the
+fact that `TRUE` evaluates to 1 and `FALSE` evaluates to 0. This means
+`sum(S30_C_5 == 0)` gives the number of 0 in the `S30_C_5` column
+
+🎬 Find the number of zeros in all six columns:
+
+```{r}
+
+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))
+```
+
+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:
+
+```{r}
+s30 |>
+  pivot_longer(cols = -xenbase_gene_id,
+               names_to = "sample",
+               values_to = "count") |>
+  group_by(sample) |>
+  summarise(n_zero = sum(count == 0))
+```
+
+You could expand to get all the summary information
+
+🎬 Summarise all the samples:
+
+```{r}
+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))
+```
+
+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:
+
+-   write to file
+-   use in `ggplot()`
+-   format in a Quarto report
+
+🎬 Save the summary as a dataframe, `s30_summary_samp`.
+
+```{r}
+#| echo: false
+s30_summary_samp <- 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))
+```
+
+We can write to file using `write_csv()`
+
+🎬 Write `s30_summary_samp` to a file called "s30_summary_samp.csv":
+
+```{r}
+write_csv(s30_summary_samp, 
+          file = "data-processed/s30_summary_samp.csv")
+```
+
+Plotting the distribution of values is perhaps the easiest way to
+understand the data. We could plot each column separately or we can pipe
+the tidy format of data into `ggplot()` and make use of `facet_wrap()`
+
+🎬 Pivot the data and pipe into `ggplot`:
+
+```{r}
+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 (`r length(s30$xenbase_gene_id)`) 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:
+
+```{r}
+s30 |>
+  pivot_longer(cols = -xenbase_gene_id,
+               names_to = "sample",
+               values_to = "count") |>
+  ggplot(aes(log10(count))) +
+  geom_density() +
+  facet_wrap(. ~ sample, nrow = 3)
+```
+
+The key information to take from these plots is:
+
+-   the distributions are roughly similar in width, height, location and
+    overall shape so it doesn't 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 '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.
+
+🎬 Get a quick overview of the columns:
+
+```{r}
+#| class: outputscroll
+summary(hspc)
+```
+
+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:
+
+```{r}
+#| class: outputscroll
+summary(hspc[1:20])
+```
+
+Notice that:
+
+-   the 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.
+-   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()`.
+
+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:
+
+```{r}
+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).
+
+All cells have quite a few zeros and the lower quartile is 0 for all
+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 +/-
+one s.d. Not unlike a boxplot, but when you need the boxes too be very
+narrow!
+
+🎬 Create a pointrange plot.
+
+```{r}
+hspc_summary_samp |> 
+  ggplot(aes(x = cell, y = mean)) +
+  geom_pointrange(aes(ymin = mean - sd, 
+                      ymax = mean + sd ),
+                  size = 0.1)
+```
+
+You will need to use the Zoom button to pop the plot window out so you
+can make it as wide as possible
+
+The things to notice are:
+
+-   the 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 the size of the mean.
+
+🎬 Order a pointrange plot with `reorder(variable_to_order, order_by)`.
+
+```{r}
+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`
+
+🎬 Write `hspc_summary_samp` to a file called "hspc_summary_samp.csv":
+
+```{r}
+#| echo: false
+write_csv(hspc_summary_samp, 
+          file = "data-processed/hspc_summary_samp.csv")
+```
+
+### 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:
+
+```{r}
+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.
+
+Notice that we have:
+
+-   a lot of genes with counts of zero in every sample
+-   a lot of genes with zero counts in several of the samples
+-   some very very low counts.
+
+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()`:
+
+```{r}
+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)
+```
+
+(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":
+
+```{r}
+#| echo: false
+write_csv(s30_summary_gene, 
+          file = "data-processed/s30_summary_gene.csv")
+```
+
+#### 🐭 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:
+
+```{r}
+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:
+
+-   no genes with 0 in every cell
+-   very few genes (9) with no zeros at all
+-   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()`:
+
+```{r}
+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":
+
+```{r}
+#| echo: false
+write_csv(hspc_summary_gene, 
+          file = "data-processed/hspc_summary_gene.csv")
+```
+
+## 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 at least 3
+samples[^3] and those with total counts above 20.
+
+[^3]: I chose three because that would keep \[0, 0, 0\] \[#,#,#\]. This
+    is difference we cannot test statistically, but which would matter
+    biologically.
+
+🎬 Filter the summary by gene dataframe:
+
+```{r}
+s30_summary_gene_filtered <- s30_summary_gene |> 
+  filter(total > 20) |> 
+  filter(n_zero < 4)
+```
+
+🎬 Write the filtered summary by gene to file:
+
+```{r}
+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:
+
+```{r}
+s30_filtered <- s30 |> 
+  filter(xenbase_gene_id %in%  s30_summary_gene_filtered$xenbase_gene_id)
+```
+
+🎬 Write the filtered original to file:
+
+```{r}
+write_csv(s30_filtered, 
+          file = "data-processed/s30_filtered.csv")
+```
+
+### 🐭 Mouse filtering
+
+We will 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!
+
+**You need only do the section for your own project data**
+
+### 🐸 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
+
+🎬 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.
+
+### 🍂 xxxx and future you
+
+🎬 Create a new Project, `xxxx-88H`, populated with folders and your
+data. Make a script file called `xxxx.R`. This will a be commented
+analysis of t xxxxxxxxxxxx.
+
+# 🥳 Finished
+
+Well Done!
+
+# Independent study following the workshop
+
+[Consolidate](study_after_workshop.qmd)
+
+# The Code file
+
+These contain all the code needed in the workshop even where it is not
+visible on the webpage.
+
+The [workshop.qmd](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](https://github.com/3mmaRand/). Coding and thinking answers are
+marked with `#---CODING ANSWER---` and `#---THINKING ANSWER---`
+
+Pages made with R [@R-core], Quarto [@allaire2022], `knitr` [@knitr],
+`kableExtra` [@kableExtra]
+
+# References