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

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.

Community effort to unravel the complex human neuronal landscape exemplified for the striatum

“…Cell-specific enrichment scores for synapse-related gene sets 66 were computed as per the methodology outlined in Hediyeh-zadeh et al 67 . Differentially expressed gene sets between D1MSN and D2MSN were identified using a two-sided student's t-test, with selection and ranking of gene sets exhibiting an adjusted p-value below 0.001 based on t-statistic values.…”