SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
T cell subsets, highlighting the transition from the spectrum of active to quiescent differentiated states during a multi-day viral infection. We postulate that, although data integration methods aim to make samples more similar without distinguishing batch from biological signal, aggregating data into metacells on the per-sample level provides robust capture of true biological variation between samples.
Whereas gene scores, open regulatory elements and correlations between gene expression and chromatin accessibility cannot be determined robustly at the single-cell level, they can be computed for individual metacells.
The inputs to SEACells are raw count matrices ; a low-dimensional representation of the data derived using modality-appropriate pre-processing, such as principal component analysis for RNA; and the number of metacells to be identified. As output for downstream analyses, SEACells produces groupings of cells that represent metacells, aggregated metacells-by-feature raw counts matrices and soft assignments representing groups of highly related cells.
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