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Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures

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Title: Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures
Authors: Chan, TE
Stumpf, MPH
Babtie, AC
Item Type: Journal Article
Abstract: While single-cell gene expression experiments present new challenges for data processing, the cellto-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher-order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks from three experimental single-cell datasets and illustrate how network context, choices made during analysis, and sources of variability affect network inference. PIDC tutorials and open-source software for estimating PID are available. PIDC should facilitate the identification of putative functional relationships and mechanistic hypotheses from single-cell transcriptomic data.
Issue Date: 27-Sep-2017
Date of Acceptance: 24-Aug-2017
URI: http://hdl.handle.net/10044/1/56882
DOI: https://dx.doi.org/10.1016/j.cels.2017.08.014
ISSN: 2405-4712
Publisher: Elsevier (Cell Press)
Start Page: 251
End Page: 267.e3
Journal / Book Title: Cell Systems
Volume: 5
Issue: 3
Copyright Statement: This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: Biotechnology and Biological Sciences Research Council (BBSRC)
Funder's Grant Number: BB/N011597/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Biochemistry & Molecular Biology
Cell Biology
RNA-SEQ EXPERIMENTS
MUTUAL INFORMATION
EXPRESSION ANALYSIS
BAYESIAN-APPROACH
FATE DECISIONS
ASSOCIATION NETWORKS
SEQUENCING DATA
STEM-CELLS
BLOOD STEM
DYNAMICS
gene regulation
mutual information
network reconstruction
single-cell PCR
single-cell RNA-seq
Publication Status: Published
Open Access location: http://www.cell.com/cell-systems/fulltext/S2405-4712(17)30386-1
Appears in Collections:Faculty of Natural Sciences



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