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Mol Syst Biol. 2019 Feb 22;15(2):e8557. doi: 10.15252/msb.20188557.

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

Author information

1
Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
2
Department of Computer Science, Columbia University, New York, NY, USA.
3
Department of Engineering, University of Cambridge, Cambridge, UK.
4
Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA.
5
Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY, USA.
6
Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY, USA.
7
Institute for Cancer Genetics, Columbia University Irving Medical Center, New York, NY, USA.
8
Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
9
Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
10
Department of Computer Science, Columbia University, New York, NY, USA david.blei@columbia.edu pas2182@cumc.columbia.edu.
11
Department of Statistics, Columbia University, New York, NY, USA.
12
Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA david.blei@columbia.edu pas2182@cumc.columbia.edu.
13
Department of Biochemistry and Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.

Abstract

Common approaches to gene signature discovery in single-cell RNA-sequencing (scRNA-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 (scHPF), a Bayesian factorization method that adapts hierarchical Poisson factorization (Gopalan et al, 2015, Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, 326) for de novo discovery of both continuous and discrete expression patterns from scRNA-seq. scHPF does not require prior normalization and captures statistical properties of single-cell data better than other methods in benchmark datasets. Applied to scRNA-seq of the core and margin of a high-grade glioma, scHPF uncovers marked differences in the abundance of glioma subpopulations across tumor regions and regionally associated expression biases within glioma subpopulations. scHFP revealed an expression signature that was spatially biased toward the glioma-infiltrated margins and associated with inferior survival in glioblastoma.

KEYWORDS:

dimensionality reduction; gene signature discovery; glioma; single‐cell RNA‐seq

PMID:
30796088
PMCID:
PMC6386217
DOI:
10.15252/msb.20188557
[Indexed for MEDLINE]
Free PMC Article

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