Format

Send to

Choose Destination
Genome Biol. 2019 May 16;20(1):94. doi: 10.1186/s13059-019-1700-9.

Addressing confounding artifacts in reconstruction of gene co-expression networks.

Author information

1
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
2
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
3
Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
4
Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
5
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
6
Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
7
Department of Biology, Johns Hopkins University, Baltimore, MD, USA.
8
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
9
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA. ajbattle@jhu.edu.
10
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. jtleek@gmail.com.
11
Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA. jtleek@gmail.com.

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.

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center