Chapter 7 Differential expression analysis with DESeq2

Here is a quick example of the use of DESeq2 for differential expression analysis.

7.1 Loading data for DESeq2

A DESeqDataSet object is required for all the following steps.

library(DESeq2)

rownames(metadata) <- metadata$SampleID

dds <- DESeqDataSetFromTximport(txi = txi.kallisto,
                                colData = metadata,
                                design = ~ treatment)

7.2 Filtering out underrepresented transcripts

Transcripts with too few counts are filtered out. The function relevel() is used to set up which group is the control

dds <- dds[rowSums(counts(dds)) >= 10,]
dds$treatment <- relevel(dds$treatment, ref = 'starved')

7.3 Running DESEq2

The main pipeline of DESeq2, including their own data normalization step is run using the DESeq() function.

dds <- DESeq(dds)

Exporting results

res <- results(dds)

summary(res)

## 
## out of 32104 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up)       : 472, 1.5%
## LFC < 0 (down)     : 585, 1.8%
## outliers [1]       : 276, 0.86%
## low counts [2]     : 9337, 29%
## (mean count < 6)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results

datatable(as.data.frame(res),
          caption = 'Differential expression results using DESeq2',
          options = list(scrollX = TRUE))

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html