In:
Nature Communications, Springer Science and Business Media LLC, Vol. 9, No. 1 ( 2018-01-18)
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.
Type of Medium:
Online Resource
ISSN:
2041-1723
DOI:
10.1038/s41467-017-02554-5
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2018
detail.hit.zdb_id:
2553671-0
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