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A general and flexible method for signal extraction from single-cell RNA-seq data
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A general and flexible method for signal extraction from single-cell RNA-seq data
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A general and flexible method for signal extraction from single-cell RNA-seq data
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Journal Article
A general and flexible method for signal extraction from single-cell RNA-seq data
2018
Overview
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.
Publisher
Nature Publishing Group,Nature Publishing Group UK,Nature Portfolio
Subject
/ Computational Biology - methods
/ Computational Biology - statistics & numerical data
/ Dropouts
/ Genes
/ Genomes
/ High-Throughput Nucleotide Sequencing - methods
/ High-Throughput Nucleotide Sequencing - statistics & numerical data
/ Humans
/ Male
/ Mice
/ Principal Component Analysis
/ Principal components analysis
/ RNA
/ Single-Cell Analysis - methods
