spatial-condition

To perform differential expression on spatial transcriptomics data, this Skill aggregates counts per sample and annotated region into pseudobulk values. It then applies DESeq2-style statistics or Wilcoxon testing with multiple hypothesis correction to compare experimental conditions reliably.

Pseudoreplication occurs when spatial spots are treated as independent replicates. To avoid this, spatial transcriptomics condition comparison requires pseudobulk aggregation of counts per sample and region before applying statistical tests, ensuring robust multi-sample statistics.

Yes, spatial transcriptomics differential expression optionally supports pydeseq2 for advanced statistical modeling. The Skill leverages scanpy and scipy alongside pydeseq2 to apply DESeq2-style testing on pseudobulk values aggregated by sample and condition.

Comparing experimental conditions in spatial transcriptomics data requires AnnData objects containing condition and sample keys. These keys allow the pseudobulk aggregation and differential expression testing to properly group and compare samples across annotated tissue regions.

The best way to identify differentially expressed genes within annotated regions is using pseudobulk differential expression. This Skill aggregates spatial transcriptomics counts per sample and region, then applies DESeq2-style testing to compare healthy and diseased conditions accurately.

Yes, pseudobulk differential expression testing for spatial data supports both DESeq2-style statistics and Wilcoxon rank-sum tests. The Skill applies multiple hypothesis correction to both methods, allowing robust condition comparison across aggregated sample counts.