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bioRxiv · 10.1101/2023.09.08.556842

Identification of cell types, states and programs by learning gene set representations

Hediyeh-zadeh, S.·Whitfield, H. J.·Kharbanda, M.·Curion, F.·Bhuva, D. D.·Theis, F. J.·Davis, M. J.
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Abstract

As single cell molecular data expand, there is an increasing need for algorithms that efficiently query and prioritize gene programs, cell types and states in single-cell sequencing data, particularly in cell atlases. Here we present scDECAF, a statistical learning algorithm to identify cell types, states and programs in single-cell gene expression data using vector representation of gene sets, which improves biological interpretation by selecting a subset of most biologically relevant programs. We applied scDECAF to scRNAseq data from PBMC, Lung, Pancreas, Brain and slide-tags snRNA of human prefrontal cortex for automatic cell type annotation. We demonstrate that scDECAF can recover perturbed gene programs in Lupus PBMC cells stimulated with IFNbeta and TGFBeta-induced cells undergoing epithelial-to-mesenchymal transition. scDECAF delineates patient-specific heterogeneity in cellular programs in Ovarian Cancer data. Using a healthy PBMC reference, we apply scDECAF to a mapped query PBMC COVID-19 case-control dataset and identify multicellular programs associated with severe COVID-19. scDECAF can improve biological interpretation and complement reference mapping analysis, and provides a method for gene set and pathway analysis in single cell gene expression data.

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Hediyeh-zadeh, S., Whitfield, H. J., Kharbanda, M., Curion, F., Bhuva, D. D., Theis, F. J., Davis, M. J.. 2023-09-12. Identification of cell types, states and programs by learning gene set representations. https://doi.org/10.1101/2023.09.08.556842

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