Topic
Normalization choice
Which normalization is right for my question, and which ones must never be mixed?
Library-size normalization assumes most genes do not change; that assumption fails for global shifts, cell-type composition changes and spike-in designs. Median-of-ratios, TMM, CPM, TPM and SCTransform answer different questions and are not interchangeable inputs to downstream tools.
- How to Choose a Normalization Method in ATAC-seq
The method you pick can turn 33 significant regions into 24,450 on the same data, so decide it on purpose and check it with an MA plot.
- How to Choose a Normalization Method in Bulk RNA-seq
Median-of-ratios, TMM, CPM and TPM answer different questions, and the wrong one fails quietly when your treatment shifts the whole transcriptome.
- How to Choose a Normalization Method in ChIP-seq
RPKM, CPM, and median-of-ratios all assume nothing changed genome-wide, an assumption that quietly kills real histone-mark shifts before you ever see them.
- How to Choose a Normalization Method in CUT&RUN and CUT&Tag
Your treated sample has a fraction of the control's histone mark, and your bigWigs look identical: the normalization you picked erased the biology.
- How to Choose a Normalization Method in Single-Cell RNA-seq
LogNormalize, SCTransform, CPM, TPM and dsb answer different questions; picking the wrong one quietly rewrites your clusters and your DE calls.
- How to Choose a Normalization Method in Spatial Transcriptomics
Library-size normalization removes the exact tissue-architecture signal you're trying to map, and the scRNA-seq default you already know is often the wrong call here.