Chapter 9

Single-Cell and Spatial

• Cell cycle scoring

  A phase call for every cell that tells you whether your clustering split is biology or just who happened to be dividing.

• CITE-seq

  Your ADT counts are not RNA counts in disguise, and normalizing them like RNA is how CITE-seq experiments quietly go wrong.

• Clustering resolution

  The number you tweak until the UMAP "looks right" is quietly deciding which cell types exist in your paper.

• kNN graph (shared nearest neighbor)

  The graph you build before clustering decides what counts as similar, and no amount of resolution tweaking downstream fixes a k or PC count that's wrong upstream.

• LogNormalize (log1p CP10k)

  Seurat's default normalization looks like one function call, but the scale factor and the log1p transform each carry an assumption that decides whether your clusters reflect biology or sequencing depth.

• Marker gene

  The gene that supposedly proves your cluster is a real cell type, and the checks that keep FindAllMarkers from lying to you.

• Overclustering

  Your clustering algorithm will happily split pure noise into more pieces, so the resolution number tells you nothing about whether a cluster is real.

• Pseudobulk

  Collapsing cells into sample-level counts turns pseudoreplication into real replication, so a bulk RNA-seq model can actually be trusted on single-cell data.

• Regressing out variables

  Regressing out percent.mt, nCount or cell cycle looks like free cleanup, but it can delete the biology you came for.

• scATAC-seq

  The assay tells you where chromatin is open in each cell, not what any transcription factor is doing there, mixing up the two wrecks your interpretation before you've run a single test.

• Single-cell integration

  Integration erases the exact signal it was designed to erase, the trick is knowing when that's the goal and when it's the bug.

• Spatial transcriptomics

  A tissue section isn't a bag of cells, and treating a Visium spot like a single cell will quietly wreck your cell-type calls.

• Spot deconvolution

  Turning a Visium spot's blended signal into per-cell-type proportions, and why the method you pick changes the biology you report.

• Visium

  A Visium spot isn't a cell, it's a pool of one to ten, and treating it like single-cell data is the fastest way to misread your tissue.

• Xenium

  A padlock-probe imaging platform that gives you real single-cell spatial resolution, at the cost of a gene list you have to choose in advance.