Merge pull request #119 from ARTbio/update_IOC_singlecell

Update IOC-singlecell second part
ARTbio · May 11, 2024 · 6a904ac · 6a904ac
2 parents 7f7f36f + cbecfbb
commit 6a904ac
Show file tree

Hide file tree

Showing 7 changed files with 55 additions and 15 deletions.
diff --git a/docs/scRNAseq_basics/00_IOCsc_week0.md b/docs/scRNAseq_basics/00_IOCsc_week0.md
@@ -1,4 +1,4 @@
-## First Steps with Seurat
+# First Steps with Seurat
 
 Please go read the following pages : 
 

diff --git a/docs/scRNAseq_basics/01_IOCsc_week1.md b/docs/scRNAseq_basics/01_IOCsc_week1.md
@@ -1,4 +1,4 @@
-## Data Preprocessing
+# Data Preprocessing
 
 The preprocessing is the most important part of a single cell analysis because you
 can skew your result if you filter too much **or too little** and you must really

diff --git a/docs/scRNAseq_basics/02_IOCsc_week2.md b/docs/scRNAseq_basics/02_IOCsc_week2.md
@@ -1,11 +1,33 @@
 
+# Reduction of dimensions
 
+Now, you have a clean expression matrix and you want to identify cluster of populations.
+First, you'll need to explain as much variability in as few dimensions as possible. Because,
+at the moment, you can only describe your cells in 2000 dimensions (maybe a little complex
+for the poor human eye and also for a computer!). To learn more about different methods,
+you must read the following page: [Reduction of dimensionality](./redim.md).
 
 ---
 
 ![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!**
 
+# Render your RMD/QMD
 
+To complete this week you'll need to :
+
+- [x] 1. Compute the PCA
+- [x] 2. Perform the JackStraw analysis
+- [x] 3. Utilize JackStraw and Elbow visualization to choose the number of PCs to retain 
+     **explain your choice** 
+- [x] 4. Compute the UMAP and perform different visualizations with `DimPlot` and 
+     `FeaturePlot`, have fun!
+
+!!! warning "IMPORTANT"
+    Please note you **must** be explicative in your cutoff choices and
+    provide detailed explanations for each step of your thought process 
+    In general, try to explain in your own words for each step of your analysis!
+
+Add your RMD/QMD in your Trello card.
 
 
 **Thank you for your attention and see you next week :clap: :clap: :clap:**

diff --git a/docs/scRNAseq_basics/03_IOCsc_week3.md b/docs/scRNAseq_basics/03_IOCsc_week3.md
@@ -1,11 +1,29 @@
 
+# Clustering
 
+We have our cells in a dimensional space that can be easier to be interpreted. It's time to
+identify similar transcriptome to obtain clusters of cell populations! To do so, we use
+a non-hierarchical clustering method based on an SNN graph (Shared Nearest Neighbors). You
+can read the [clustering](./clustering.md) page to master the concept!
 
 ---
 
 ![](../R-IOC/images/toolbox-do-it-yourself.png){: style="width:75px"} **Do it yourself!**
 
+# Render your RMD/QMD
 
+To complete this week you'll need to :
+
+- [x] 1. Compute the clustering at several resolutions (ranging from 0.2 to 1.2 with a step size of 0.2)
+- [x] 2. Visualize result with `clustree`, select the resolution and **explain your choice** 
+- [x] 3. Visualize the clusters using the selected resolution and to explore results, have fun!
+
+!!! warning "IMPORTANT"
+    Please note you **must** be explicative in your cutoff choices and
+    provide detailed explanations for each step of your thought process 
+    In general, try to explain in your own words for each step of your analysis!
+
+Add your RMD/QMD in your Trello card. You can also add the html version of your rapport.
 
 
 **Thank you for your attention and see you next week :clap: :clap: :clap:**

diff --git a/docs/scRNAseq_basics/images/QCFilter-1.png b/docs/scRNAseq_basics/images/QCFilter-1.png
diff --git a/docs/scRNAseq_basics/preprocessing.md b/docs/scRNAseq_basics/preprocessing.md
@@ -137,10 +137,10 @@ pbmc_small <- subset(pbmc_small,
                      (nFeature_RNA > 300 & nFeature_RNA < 2300))
 
 ## Plot
-ggplot(pbmc_small@meta.data,
-               aes(x = nCount_RNA,
-                   y = nFeature_RNA,
-                   color = percent_mito)) +
+ggplot(pbmc_small@meta.data, 
+       aes(x = nFeature_RNA,
+           y = nCount_RNA,
+           color = percent_mito)) +
   geom_point() +
   scale_y_log10() +
   scale_color_gradient2(low = "green",

diff --git a/docs/scRNAseq_basics/program_sc.md b/docs/scRNAseq_basics/program_sc.md
@@ -1,15 +1,15 @@
 In this Interactive Online Companionship which will be held in **April 2024**.
 We will train to use the R programming language for single cell RNAseq data analysis.
 
-### Week 0 - 24/04/2024
+### [Week 0](./00_IOCsc_week0.md) - 24/04/2024
 
 1. Introduction of the Companions and Instructors (10 min)
 2. Presentation of the IOC general workflow (Scheme) (15 min)
 3. Work Program of the week 0  
     - Import data  
     - Create a genome annotation with Biomart  
 
-### Week 1 - 30/04/2024¶
+### [Week 1](./01_IOCsc_week1.md) - 30/04/2024
 
 1. Questions on Week 0 [Visioconference]
 2. Preprocessing
@@ -19,45 +19,45 @@ We will train to use the R programming language for single cell RNAseq data anal
 6. Program of Week 1  
     - Exercices with RStudio and R scripting  
 
-### Week 2 - 08/05/2024
+### [Week 2](./02_IOCsc_week2.md) - 08/05/2024
 
 1. Questions on Week 1 [Visioconference]
 2. Reduction of dimensionality (PCA & UMAP)
 3. Inspect PCA
 4. Program of Week 2  
     - Exercices with RStudio and R scripting
 
-### Week 3 - 15/05/2024¶
+### [Week 3](./03_IOCsc_week3.md) - 15/05/2024
 
 1. Questions on Week 2 [Visioconference]
 2. Clustering barcodes
 3. Choosing a meaningful partition
 4. Program of Week 3  
     - Exercices with RStudio and R scripting
 
-### Week 4 - 22/05/2024¶
+### [Week 4](./04_IOCsc_week4.md) - 22/05/2024
 
 1. Questions on Week 3 [Visioconference]
 2. Visualization of gene markers for each cluster
 3. Differential Gene Expression
 4. Program of Week 4  
     - Exercices with RStudio and R scripting
 
-### Week 5 - 29/05/2024¶
+### [Week 5](./05_IOCsc_week5.md) - 29/05/2024
 
 1. Questions on Week 4 [Visioconference]
 2. Over-representation Analysis with ClusterProfiler
 3. Program of Week 5  
     - Exercices with RStudio and R scripting
 
-### Week 6 - 05/06/2024¶
+### [Week 6](./06_IOCsc_week6.md) - 05/06/2024
 
 1. Questions on Week 5 [Visioconference]
 2. Gene Set Enrichment Analysis with ClusterProfiler
 3. Program of Week 6
     - Exercices with RStudio and R scripting
 
-### Week 7 - 12/06/2024¶
+### [Week 7](./07_IOCsc_week7.md) - 12/06/2024
 
 1. Questions on Week 6 [Visioconference]
 2. Assign cell type identity to clusters
@@ -66,7 +66,7 @@ We will train to use the R programming language for single cell RNAseq data anal
     - Exercices with RStudio and R scripting  
     - Small project with a specific objective
 
-### Week 8 - Presentations of the analyses by the companions¶ (september 2024)
+### Week 8 - Presentations of the analyses by the companions (september 2024)
 
 1. Questions on Week 7 (30 minutes max) [Visioconference]
 2. 20 min presentations by the attendees