keypoints for cell type annotation

episodes/cell_type_annotation.Rmd

@@ -488,5 +488,27 @@ TODO
 :::
 
 ::: keypoints
--   TODO
+-   The two main approaches for cell type annotation are 1) manual annotation
+    of clusters based on marker gene expression, and 2) computational annotation
+    based on annotation transfer from reference datasets or marker gene set enrichment testing.
+-   For manual annotation, cells are first clustered with unsupervised methods
+    such as graph-based clustering followed community detection algorithms such
+    as Louvain or Leiden.
+-   The `clusterCells` function from the
+    [scran](https://bioconductor.org/packages/scran) package provides different
+    algorithms that are commonly used for the clustering of scRNA-seq data.
+-   Once clusters have been obtained, cell type labels are then manually
+    assigned to cell clusters by matching cluster-specific upregulated marker
+    genes with prior knowledge of cell-type markers.
+-   The `findMarkers` function from the [scran](https://bioconductor.org/packages/scran) package
+    package can be used to find candidate marker genes for clusters of cells by
+    testing for differential expression between pairs of clusters.
+-   Computational annotation using published reference datasets or curated gene sets
+    provides a fast, automated, and reproducible alternative to the manual
+    annotation of cell clusters based on marker gene expression.
+-   The [SingleR](https://bioconductor.org/packages/SingleR)
+    package is a popular choice for reference-based annotation and assigns labels
+    to cells based on the reference samples with the highest Spearman rank correlations.
+-   The [AUCell](https://bioconductor.org/packages/AUCell) package provides an enrichment
+    test to identify curated marker sets that are highly expressed in each cell. 
 :::

episodes/multi-sample.Rmd

@@ -6,17 +6,17 @@ exercises: 15 # Minutes of exercises in the lesson
 
 :::::::::::::::::::::::::::::::::::::: questions 
 
-- How to remove technical variations (batch effects) for reliable scRNA-seq analysis?
-- How to identify genes differentially expressed between cell types in scRNA-seq?
-- How to track changes in cell type abundance across conditions in scRNA-seq, accounting for cell number bias?
+- How to integrate data from multiple batches, samples, and studies?
+- How to identify differentially expressed genes between experimental conditions for each cell type?
+- How to identify changes in cell type abundance between experimental conditions?
 
 ::::::::::::::::::::::::::::::::::::::::::::::::
 
 ::::::::::::::::::::::::::::::::::::: objectives
 
-- Correct batch effects (technical variations) in scRNA-seq data (use tools like correctExperiment).
-- Master scRNA-seq DEA workflow (grouping, normalization, testing with edgeR).
-- Analyze scRNA-seq DA (consider compositional effects) for robust cell type changes.
+- Correct batch effects and diagnose potential problems such as over-correction.
+- Perform differential expression comparisons between conditions based on pseudo-bulk samples.
+- Perform differential abundance comparisons between conditions.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::