Reference-free cell type deconvolution of pixel-resolution spatially resolved transcriptomics data

Brendan F Miller, Feiyang Huang, Lyla Atta, Arpan Sahoo, Jean Fan^

Abstract: Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type spatial co-localization patterns. We developed STdeconvolve as an unsupervised approach to deconvolve underlying cell-types comprising such multi-cellular pixel resolution spatially resolved transcriptomics datasets. We show that STdeconvolve effectively recovers the putative transcriptomic profiles of cell-types and their proportional representation within spatially resolved pixels without reliance on external single-cell transcriptomics references.

Paper: Nature Communications. April 29, 2022. | Pubmed | PDF

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