@article {Risso125112, author = {Davide Risso and Fanny Perraudeau and Svetlana Gribkova and Sandrine Dudoit and Jean-Philippe Vert}, title = {ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data}, elocation-id = {125112}, year = {2017}, doi = {10.1101/125112}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Single-cell RNA sequencing (scRNA-seq) is a powerful technique that enables researchers to measure gene expression at the resolution of single cells. Because of the low amount of RNA present in a single cell, many genes 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 simulations 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.}, URL = {https://www.biorxiv.org/content/early/2017/04/06/125112}, eprint = {https://www.biorxiv.org/content/early/2017/04/06/125112.full.pdf}, journal = {bioRxiv} }