Chapter 8

RNA-seq

• Batch correction (ComBat, limma removeBatchEffect)

  Batch correction can rescue a PCA plot and ruin a p-value in the same script, so the real decision is where in the analysis you apply it.

• Contrast (differential expression)

  The model fits coefficients; the contrast is the question you ask of them, and a wrong reference level flips the sign of your biology without any warning.

• Count matrix

  The raw, unnormalized table every RNA-seq analysis starts from, and the single most common place a downstream result quietly goes wrong.

• Log fold change (log2FC)

  The number that decides which genes count as "changed", and the one number in your results table that people misread most often.

• Log fold change shrinkage

  Why your DESeq2 volcano plot has a spike of huge fold changes at low expression, and what apeglm or ashr does to fix it before you rank genes.

• Negative binomial model

  The two-parameter distribution that lets DESeq2 and edgeR tell real fold changes apart from ordinary replicate noise.

• Transcript-to-gene mapping (tx2gene)

  A two-column table decides whether your Salmon counts become gene-level data or a silent mess, and most failures come from version suffixes.