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bioRxiv · 10.1101/574574

Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model

Townes, F. W.·Hicks, S. C.·Aryee, M. J.·Irizarry, R. A.
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Abstract

Single cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow multinomial sampling with no zero-inflation. Current normalization pro-cedures such as log of counts per million and feature selection by highly variable genes produce false variability in dimension reduction. We pro-pose simple multinomial methods, including generalized principal component analysis (GLM-PCA) for non-normal distributions, and feature selection using deviance. These methods outperform current practice in a downstream clustering assessment using ground-truth datasets.

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Townes, F. W., Hicks, S. C., Aryee, M. J., Irizarry, R. A.. 2019-03-11. Feature Selection and Dimension Reduction for Single Cell RNA-Seq based on a Multinomial Model. https://doi.org/10.1101/574574

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