A general and flexible method for signal extraction from single-cell RNA-seq data

Davide Risso, Fanny Perraudeau, Svetlana Gribkova, Sandrine Dudoit () and Jean-Philippe Vert ()
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Davide Risso: Weill Cornell Medicine
Fanny Perraudeau: University of California
Svetlana Gribkova: Université Paris Diderot
Sandrine Dudoit: University of California
Jean-Philippe Vert: PSL Research University

Nature Communications, 2018, vol. 9, issue 1, 1-17

Abstract: Abstract Single-cell RNA-sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

Date: 2018
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DOI: 10.1038/s41467-017-02554-5

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