Assay
Single-Cell ATAC-seq: sanity checks and pitfalls
Each cell yields a few thousand Tn5 fragments, so the peak-by-cell matrix is far sparser than a gene-by-cell matrix and is usually modeled with TF-IDF plus singular value decomposition (LSI) rather than log-normalization plus PCA. The first LSI component tracks sequencing depth and must be dropped; peaks called on the pooled data miss rare cell types unless you call per cluster. Cell calling, doublet detection and gene activity scores all have assay-specific pitfalls that scRNA-seq experience does not cover.
Who this is for: Single-cell analysts extending an scRNA-seq project with the 10x ATAC or Multiome kit. They expect Seurat-style workflows and hit a sparser, binary-ish, peak-by-cell matrix where PCA, normalization and marker calling all need different tools and intuitions.
- How to Detect Batch Effects in Single-Cell ATAC-seq
Your LSI plot separates by processing date instead of cell type, here's the order of checks that tells you whether that's a fixable artifact or a confound no tool can undo.
- How to Tell If You Overclustered in Single-Cell ATAC-seq
Louvain and Leiden will keep splitting your point cloud for as long as you keep raising resolution, here's how to find out whether the clusters you kept are real.
- How to Call Peaks You Can Trust in Single-Cell ATAC-seq
Your top-scoring peaks might be centromeric repeats, not regulatory elements, and the pooled peak set your pipeline handed you is quietly erasing your rarest cell type.
- How to Avoid Pseudoreplication in Single-Cell ATAC-seq
Five thousand cells from two donors are not five thousand replicates, and your p-values are lying to you about it.