pathway-go-enrichment

GO enrichment analysis on spatial transcriptomics data uses gseapy to compute Fisher's exact test and Benjamini-Hochberg FDR correction on a provided gene list. You need to store your gene list in adata.uns['go_genes'] and specify the species as either 'human' or 'mouse'.

The GO enrichment analysis uses the GO_Biological_Process_2023 library by default to identify enriched Gene Ontology terms. This library provides organism-wide mappings for human or mouse species to functionally interpret your gene sets.

Yes, this GO enrichment workflow supports cross-species analysis for both human and mouse spatial transcriptomics data. You simply select the appropriate species ('human' or 'mouse') when running the analysis to ensure correct organism-wide mappings are applied.

The enriched GO terms and their corresponding statistics are stored in adata.uns['go_results'] for downstream exploration. This output format allows researchers to easily access and interpret the significant biological processes driving observed spatial patterns.

Yes, this GO enrichment analysis applies to spatial transcriptomics workflows involving both single-slice and multi-slice experiments. It interprets candidate gene lists from these experiments to understand the biological processes driving observed spatial patterns.