Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Single-cell RNA sequencing (scRNA-seq) has enabled decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types, performed through unsupervised clustering followed by manual annotation, or via “mapping” procedures to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data, and both are prone to batch effects. To overcome these issues we present CellAssign (www.github.com/irrationone/cellassign), a probabilistic model that leverages prior knowledge of cell type marker genes to annotate scRNA-seq data into pre-defined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high grade serous ovarian cancer and follicular lymphoma.