Single-cell Hierarchical Poisson Factorization

Single-cell Hierarchical Poisson Factorization (scHPF) is a tool for de novo discovery of discrete and continuous expression patterns in single-cell RNA-sequencing (scRNA-seq).

We find that scHPF’s sparse low-dimensional representations, non-negativity, and explicit modeling of variable sparsity across genes and cells produces highly interpretable factors. The algorithm takes genome-wide molecular counts as input, avoids prior normalization, and has fast, memory-efficient inference on sparse scRNA-seq datasets.

Algorithmic details, benchmarking against alternative methods, and scHPF’s application to a spatially sampled high-grade glioma can be found in our paper at Molecular Systems Biology.

You can find the software on github.

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