update lock file

.github/workflows/quarto-gh-pages-html.yaml

@@ -25,7 +25,7 @@ jobs:
           sudo apt-get update -y && sudo apt-get install -y libcurl4-openssl-dev
           sudo apt-get install -y libharfbuzz-dev 
           sudo apt-get install -y libfribidi-dev
-
+          sudo apt-get install -y libglpk40
 
 
       - uses: r-lib/actions/setup-renv@v2

omics/week-3/study_after_workshop.qmd

@@ -1,6 +1,6 @@
 ---
 title: "Independent Study to consolidate this week"
-subtitle: "Core 1"
+subtitle: "Omics 1: 👋 Hello data!"
 toc: true
 toc-location: right
 format:

omics/week-4/overview.qmd

@@ -5,39 +5,40 @@ toc: true
 toc-location: right
 ---
 
-This week you will .... The independent study will ...
-In the workshop, you will learn ...
+This week we cover differential expression analysis on raw counts or log normalised values. The independent study will allow you to check you have what you should have following the [Omics 1: Hello Data workshop](../week-3/workshop.html) and [Consolidation study](../week-3/study_after_workshop.html). It will also summarise the concepts and methods we will use in the workshop. 
+In the workshop, you will learn how to perform differential expression analysis on raw counts using **`DESeq2`** or on logged normalised expression values using **`scran`** or both.
 
 We suggest you sit together with your group in the workshop.
 
 ### Learning objectives
 
 The successful student will be able to:
 
--   xxx
--   xxx
--   xxx
--   xxx
+-   verify they have the required RStudio Project set up and the data and code files from the previous Workshop and Consolidation study
+-   explain the goal of differential expression analysis and the importance of normalisation
+-   explain why and how the nature of the input values determines the analysis package used
+-   describe the metadata needed to carry out differential expression analysis and the statistical models used by **`DESeq2`** and **`scran`**
+-   find genes that are unexpressed or expressed in a a single cell type or treatment group
+-   perform differential expression analysis on raw counts using **`DESeq2`** or on logged normalised expression values using **`scran`** or both.
+-   explain the output of differential expression: log fold change, p-value, adjusted p-value, 
 
 
 ### Instructions
 
 1.  [Prepare](study_before_workshop.qmd)
 
-    i.   📖 Read xxx 
+    i.   📖 Read what you should have so far and about concepts in differential expression analysis. 
 
 
 2.  [Workshop](workshop.qmd)
 
-    i.   💻 xx
+    i.   💻 Find unexpressed genes and those expressed in a single cell type or treatment group.
 
-    ii.  💻 xx
+    ii.  💻 Set up the metadata for differential expression analysis.
 
-    iii. 💻 xx
+    iii. 💻 Perform differential expression analysis on raw counts using **`DESeq2`** or on logged normalised expression values using **`scran`** or both.
 
-    iv.  💻 xx
-
-    v.   💻 Look after future you!
+    iv.  Look after future you!
 
 3.  [Consolidate](study_after_workshop.qmd)
 

omics/week-4/study_after_workshop.qmd

@@ -14,14 +14,14 @@ You need only do the section for your own project data
 
 🐸 Frogs
 
-🎬 Open your `frogs-88H` Project. xxx
+🎬 Open your `frogs-88H` Project and script you began in the Consolidation study last week. This is likely to be `cont-fgf-s20.R` or `cont-fgf-s14.R`. Use the differential expression analysis you did in the workshop (in `cont-fgf-s30.R`) as a template to continue your script. 
 
 🐭 Mice
 
-🎬 Open your `mice-88H` Project. xxx
+🎬 Open your `mice-88H` Project. Make a new script and, using `hspc-prog.R` as a template, repeat the analysis on a different comparisons. 
 
 🍂 xxxx
 
-🎬 Open your `xxxx-88H` Project. xxx
+🎬 Follow one of the other examples