key points for multi-sample analysis

episodes/multi-sample.Rmd

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-- TODO
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+-   Batch effects are systematic technical differences in the observed expression
+    in cells measured in different experimental batches.
+-   Computational removal of batch-to-batch variation with the `correctExperiment`
+    function from the `r Biocpkg("batchelor")` package allows us to combine data
+    across multiple batches for a consolidated downstream analysis.
+-   Differential expression (DE) analysis of replicated multi-condition scRNA-seq experiments
+    is typically based on pseudo-bulk expression profiles, generated by summing
+    counts for all cells with the same combination of label and sample.
+-   The `aggregateAcrossCells` function from the `r Biocpkg("scater")` package
+    facilitates the creation of pseudo-bulk samples.   
+-   The `pseudoBulkDGE` function from the `r Biocpkg("scran")` package can be used
+    to detect significant changes in expression between conditions for pseudo-bulk samples
+    consisting of cells of the same type.
+-   Differential abundance (DA) analysis aims at identifying significant changes in
+    cell type abundance across conditions.
+-   DA analysis uses bulk DE methods such as `r Biocpkg("edgeR")` and `r Biocpkg("DESeq2")`,
+    which provide suitable statistical models for count data in the presence of
+    limited replication - except that the counts are not of reads per gene, but
+    of cells per label.
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