Addressing confounding artifacts in reconstruction of gene co-expression networks

Gene co-expression networks capture biological relationships between genes and are important tools in predicting gene function and understanding disease mechanisms. We show that technical and biological artifacts in gene expression data confound commonly used network reconstruction algorithms. We de...

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Bibliographic Details
Published in:Genome Biology Vol. 20; no. 1; p. 94
Main Authors: Parsana, Princy, Ruberman, Claire, Jaffe, Andrew E., Schatz, Michael C., Battle, Alexis, Leek, Jeffrey T.
Format: Journal Article
Language:English
Published: London BioMed Central 16.05.2019
Springer Nature B.V
BMC
Subjects:
algorithms
Animal Genetics and Genomics
Bias
Bioinformatics
Biomedical and Life Sciences
Evolutionary Biology
gene components
Gene expression
Gene Regulatory Networks
Genetic Techniques
Genomes
Genomics
Human Genetics
Humans
Life Sciences
measurement
Microbial Genetics and Genomics
Plant Genetics and Genomics
prediction
Short Report
Simulation
Teaching methods
tissues
ISSN:1474-760X, 1474-7596, 1474-760X
Online Access:Get full text
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Summary:Gene co-expression networks capture biological relationships between genes and are important tools in predicting gene function and understanding disease mechanisms. We show that technical and biological artifacts in gene expression data confound commonly used network reconstruction algorithms. We demonstrate theoretically, in simulation, and empirically, that principal component correction of gene expression measurements prior to network inference can reduce false discoveries. Using data from the GTEx project in multiple tissues, we show that this approach reduces false discoveries beyond correcting only for known confounders.
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ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-019-1700-9