De novo gene signature identification from single‐cell RNA ‐seq with hierarchical Poisson factorization

Common approaches to gene signature discovery in single‐cell RNA ‐sequencing (sc RNA ‐seq) depend upon predefined structures like clusters or pseudo‐temporal order, require prior normalization, or do not account for the sparsity of single‐cell data. We present single‐cell hierarchical Poisson factorization (sc HPF ), a Bayesian factorization method that adapts hierarchical Poisson factorization (Gopalan et al , [Gopalan P, 2015], Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence , 326) for de novo discovery of both continuous and discrete expression patterns from sc RNA ‐seq. sc HPF does not require prior normalization and captures statistical properties of single‐cell data better than other methods in benchmark datasets. Applied to sc RNA ‐seq of the core and margin of a high‐grade glioma, sc HPF uncovers marked differences in the abundance of glioma subpopulations across tumor regions and regionally associated expression biases within glioma subpopulations. sc HFP revealed an expression signature that was spatially biased toward the glioma‐infiltrated margins and associated with inferior survival in glioblastoma.