PT  - JOURNAL ARTICLE
AU  - Davide Risso
AU  - Fanny Perraudeau
AU  - Svetlana Gribkova
AU  - Sandrine Dudoit
AU  - Jean-Philippe Vert
TI  - ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data
AID  - 10.1101/125112
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 125112
4099  - http://biorxiv.org/content/early/2017/09/26/125112.short
4100  - http://biorxiv.org/content/early/2017/09/26/125112.full
AB  - 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.