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 depend upon predefined structures like clustering or pseudo-temporal orderings, do not account for the sparsity of single cell data, or require prior normalization. We present single cell Hierarchical Poisson Factorization (scHPF), a Bayesian factorization method that adapts Hierarchical Poisson Factorization for de novo discovery of both continuous and discrete expression patterns in complex tissues. scHPF does not require prior normalization and outperforms other methods in benchmark datasets. Applied to single cell RNA-sequencing of the core and margin of a high-grade glioma, scHPF uncovers subtle regional expression biases within glioma subpopulations and an expression signature associated with inferior survival in glioblastoma.