Instructor Notes

FIXME

Introduction to RNA-seqWhat are we measuring in an RNA-seq experiment?Experimental design considerationsRNA-seq quantification: from reads to count matrixFinding the reference sequencesWhere are we heading towards in this workshop?

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RStudio Project and Experimental Data

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Importing and annotating quantified data into R

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Exploratory analysis and quality control

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Differential expression analysis

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Instructor Note

The function to generate a DESeqDataSet needs to be adapted depending on the input type, e.g,

R

#From SummarizedExperiment object
ddsSE <- DESeqDataSet(se, design = ~ sex + time)

#From count matrix
dds <- DESeqDataSetFromMatrix(countData = assays(se)$counts,
                              colData = colData(se),
                              design = ~ sex + time)

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Instructor Note

DESeq2 uses the “Relative Log Expression” (RLE) method to calculate sample-wise size factors tĥat account for read depth and library composition. edgeR uses the “Trimmed Mean of M-Values” (TMM) method to account for library size differences and compositional effects. edgeR’s normalization factors and DESeq2’s size factors yield similar results, but are not equivalent theoretical parameters.

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Instructor Note

Both of the below ways of specifying the contrast are essentially equivalent. The name parameter can be accessed using resultsNames().

R

resTime <- results(dds, contrast = c("time", "Day8", "Day0"))
resTime <- results(dds, name = "time_Day8_vs_Day0")

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Extra exploration of design matrices

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Gene set enrichment analysis

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Next steps

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