bio-de-deseq2-basics

Creating DESeqDataSet

Goal: Construct a DESeqDataSet object from various input formats for DE analysis.

Approach: Wrap count data and sample metadata into the DESeq2 container, specifying the experimental design formula.

"Load my RNA-seq counts into DESeq2" → Create a DESeqDataSet from a count matrix, SummarizedExperiment, or tximport object with sample metadata and a design formula.

Standard DESeq2 Workflow

Goal: Run the complete DESeq2 pipeline from raw counts to shrunken log fold change estimates.

Approach: Create dataset, pre-filter low-count genes, set reference level, run size factor estimation + dispersion estimation + Wald test, then apply LFC shrinkage.

"Find differentially expressed genes between treated and control" → Test for significant expression changes between conditions using negative binomial models with empirical Bayes shrinkage.

## Create DESeqDataSet

dds <- DESeqDataSetFromMatrix(countData = counts,
                               colData = coldata,
                               design = ~ condition)