A general and flexible method for signal extraction from single-cell RNA-seq data
- Author(s): Risso, D
- Perraudeau, F
- Gribkova, S
- Dudoit, S
- Vert, JP
- et al.
Published Web Locationhttps://www.nature.com/articles/s41467-017-02554-5.pdf
© 2018 The Author(s). 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.