Addressing confounding artifacts in reconstruction of gene co-expression networks

Abstract 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 algorith...

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Main Authors: Princy Parsana, Claire Ruberman, Andrew E. Jaffe, Michael C. Schatz, Alexis Battle, Jeffrey T. Leek
Format: Article
Language:English
Published: BMC 2019-05-01
Series:Genome Biology
Online Access:http://link.springer.com/article/10.1186/s13059-019-1700-9
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spelling doaj-40fe5e619182489ba2397e77666f3ae32020-11-25T03:27:20ZengBMCGenome Biology1474-760X2019-05-012011610.1186/s13059-019-1700-9Addressing confounding artifacts in reconstruction of gene co-expression networksPrincy Parsana0Claire Ruberman1Andrew E. Jaffe2Michael C. Schatz3Alexis Battle4Jeffrey T. Leek5Department of Computer Science, Johns Hopkins UniversityDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthDepartment of Computer Science, Johns Hopkins UniversityDepartment of Computer Science, Johns Hopkins UniversityDepartment of Biostatistics, Johns Hopkins Bloomberg School of Public HealthAbstract 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.http://link.springer.com/article/10.1186/s13059-019-1700-9
collection DOAJ
language English
format Article
sources DOAJ
author Princy Parsana
Claire Ruberman
Andrew E. Jaffe
Michael C. Schatz
Alexis Battle
Jeffrey T. Leek
spellingShingle Princy Parsana
Claire Ruberman
Andrew E. Jaffe
Michael C. Schatz
Alexis Battle
Jeffrey T. Leek
Addressing confounding artifacts in reconstruction of gene co-expression networks
Genome Biology
author_facet Princy Parsana
Claire Ruberman
Andrew E. Jaffe
Michael C. Schatz
Alexis Battle
Jeffrey T. Leek
author_sort Princy Parsana
title Addressing confounding artifacts in reconstruction of gene co-expression networks
title_short Addressing confounding artifacts in reconstruction of gene co-expression networks
title_full Addressing confounding artifacts in reconstruction of gene co-expression networks
title_fullStr Addressing confounding artifacts in reconstruction of gene co-expression networks
title_full_unstemmed Addressing confounding artifacts in reconstruction of gene co-expression networks
title_sort addressing confounding artifacts in reconstruction of gene co-expression networks
publisher BMC
series Genome Biology
issn 1474-760X
publishDate 2019-05-01
description Abstract 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.
url http://link.springer.com/article/10.1186/s13059-019-1700-9
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AT michaelcschatz addressingconfoundingartifactsinreconstructionofgenecoexpressionnetworks
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