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Addressing confounding artifacts in reconstruction of gene co-expression networks

View ORCID ProfilePrincy Parsana, Claire Ruberman, Andrew E. Jaffe, Michael C. Schatz, Alexis Battle, Jeffery T. Leek
doi: https://doi.org/10.1101/202903
Princy Parsana
1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
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  • ORCID record for Princy Parsana
Claire Ruberman
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Andrew E. Jaffe
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
3Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
4Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
5McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
6Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
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Michael C. Schatz
1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
7Department of Biology, Johns Hopkins University, Baltimore, MD, USA
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Alexis Battle
1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
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  • For correspondence: ajbattle@cs.jhu.edu jtleek@gmail.com
Jeffery T. Leek
2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
6Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
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  • For correspondence: ajbattle@cs.jhu.edu jtleek@gmail.com
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Abstract

Gene co-expression networks can capture biological relationships between genes, and are important tools in predicting gene function and understanding disease mechanism. We show that artifacts such as batch effects in gene expression data confound commonly used network reconstruction algorithms. We then demonstrate, both theoretically and empirically, that principal component correction of gene expression measurements prior to network inference can reduce false discoveries. Using expression data from the GTEx project in multiple tissues and hundreds of individuals, this approach improves precision and recall in the networks reconstructed.

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Posted October 13, 2017.
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Addressing confounding artifacts in reconstruction of gene co-expression networks
Princy Parsana, Claire Ruberman, Andrew E. Jaffe, Michael C. Schatz, Alexis Battle, Jeffery T. Leek
bioRxiv 202903; doi: https://doi.org/10.1101/202903
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Addressing confounding artifacts in reconstruction of gene co-expression networks
Princy Parsana, Claire Ruberman, Andrew E. Jaffe, Michael C. Schatz, Alexis Battle, Jeffery T. Leek
bioRxiv 202903; doi: https://doi.org/10.1101/202903

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