STEAM is an algorithm for Spatial Trait Enrichment Analysis with perMutation testing, a robust computational approach to measure the enrichment of average gene expression across clusters in a dataset from a given gene list; it calculates a permutation p-value and performs multiple testing corrections based on the number of clusters. For ranked gene lists, STEAM enables interrogation of the topmost relevant sets of genes based on their combined average enrichment.
We applied STEAM to spatially resolved transcriptomics (SRT) datasets to elucidate the genetic basis of complex traits and diseases. We analyzed 31 SRT datasets from humans and mice spanning 8 organs and 32 complex traits. We also applied STEAM to single-cell and single-nuclei RNAseq datasets from the mouse and human brain, highlighting the complementary insights from the two data types.
Below we showcase analysis examples from the STEAM R-package, using one spatial dataset of a mouse brain coronal section (10X Genomics) and one single cell RNA-seq dataset of mouse cortex and hippocampus (Yao et al. Cell 2021).
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Developed by Linda Kvastad and Rasool Saghaleyni