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netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.

Author(s): Elyanow, Rebecca; Dumitrascu, Bianca; Engelhardt, Barbara E; Raphael, Benjamin J

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Abstract: Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA expression in single cells. However, because of technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells in a lower-dimensional space, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized non-negative matrix factorization. The network regularization takes advantage of prior knowledge of gene-gene interactions, encouraging pairs of genes with known interactions to be nearby each other in the low-dimensional representation. The resulting matrix factorization imputes gene abundance for both zero and nonzero counts and can be used to cluster cells into meaningful subpopulations. We show that netNMF-sc outperforms existing methods at clustering cells and estimating gene-gene covariance using both simulated and real scRNA-seq data, with increasing advantages at higher dropout rates (e.g., >60%). We also show that the results from netNMF-sc are robust to variation in the input network, with more representative networks leading to greater performance gains.
Publication Date: Feb-2020
Citation: Elyanow, Rebecca, Dumitrascu, Bianca, Engelhardt, Barbara E, Raphael, Benjamin J. (2020). netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis.. Genome research, 30 (2), 195 - 204. doi:10.1101/gr.251603.119
DOI: doi:10.1101/gr.251603.119
ISSN: 1088-9051
EISSN: 1549-5469
Pages: 195 - 204
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: Genome research
Version: Author's manuscript

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