Assay
Spatial Transcriptomics: sanity checks and pitfalls
Sequencing-based platforms (Visium) give you a 55-micron spot containing 1-10 cells with whole-transcriptome coverage; imaging-based platforms (Xenium, MERFISH, CosMx) give single-cell resolution for a few hundred to a few thousand genes. The analysis inherits everything from scRNA-seq plus spatial neighborhood statistics, deconvolution of spot mixtures, and registration of the count data to the histology image. The most common errors are clustering spots and calling them cell types, comparing panel-based counts to whole-transcriptome references without accounting for the panel, and ignoring tissue-section batch effects that dominate the signal.
Who this is for: Pathologists and tumor biologists moving from H&E slides to Visium or Xenium data, and single-cell analysts asked to add a spatial axis. They often treat spots as cells (they are not) or treat imaging-based panels as if they were whole-transcriptome.
- How to Detect Batch Effects in Spatial Transcriptomics
A slide is not a replicate, learn to tell tissue-section artifact from real biology before you trust a single cluster.
- How to Detect Integration Over-Correction in Spatial Transcriptomics
The UMAP where every condition mixes perfectly isn't always good news, sometimes it means Harmony erased the exact difference you set out to measure.
- How to Sanity-Check Marker Genes and Cell Type Labels in Spatial Transcriptomics
FindMarkers and rank_genes_groups will happily hand you ribosomal genes and dissociation stress as your top "cell type marker", here is the checklist that catches it before it reaches a figure legend.
- 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.
- How to Tell If You Overclustered in Spatial Transcriptomics
Leiden and Louvain will happily cut your tissue into as many pieces as the resolution allows; the algorithm has no opinion on whether any of those pieces is a real cell type.
- How to Avoid Pseudoreplication in Spatial Transcriptomics
A wall of p-values near zero on pooled spots or cells almost always means you counted the wrong thing as your sample size.
- How to Choose Cell QC Thresholds in Spatial Transcriptomics
Copying the 10% mito cutoff from a PBMC tutorial into your tumor Visium slide will quietly delete every high-mitochondrial tumor spot you came to study.
- Why Your UMAP Is Misleading You in Spatial Transcriptomics
UMAP islands, cluster sizes and arcs are optical illusions in spatial data; the tissue coordinates and a permutation test are the only evidence that counts.