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Nat Commun. 2017 Dec 19;8(1):2187. doi: 10.1038/s41467-017-02181-0.

Genomic regression analysis of coordinated expression.

Author information

1
Children's Medical Center Research Institute at UT Southwestern Medical Center, 6000 Harry Hines Blvd, Dallas, TX, 75235, USA.
2
Quantitative Biomedical Research Center at UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
3
Department of Bioinformatics at UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
4
Department of Mathematics at University of Texas at Arlington, 411S. Nedderman Drive, 478 Pickard Hall, Arlington, TX, 76019, USA.
5
Children's Medical Center Research Institute at UT Southwestern Medical Center, 6000 Harry Hines Blvd, Dallas, TX, 75235, USA. Ralph.Deberardinis@UTSouthwestern.edu.
6
Quantitative Biomedical Research Center at UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA. Guanghua.Xiao@UTSouthwestern.edu.

Abstract

Co-expression analysis is widely used to predict gene function and to identify functionally related gene sets. However, co-expression analysis using human cancer transcriptomic data is confounded by somatic copy number alterations (SCNA), which produce co-expression signatures based on physical proximity rather than biological function. To better understand gene-gene co-expression based on biological regulation but not SCNA, we describe a method termed "Genomic Regression Analysis of Coordinated Expression" (GRACE) to adjust for the effect of SCNA in co-expression analysis. The results from analyses of TCGA, CCLE, and NCI60 data sets show that GRACE can improve our understanding of how a transcriptional network is re-wired in cancer. A user-friendly web database populated with data sets from The Cancer Genome Atlas (TCGA) is provided to allow customized query.

PMID:
29259170
PMCID:
PMC5736603
DOI:
10.1038/s41467-017-02181-0
[Indexed for MEDLINE]
Free PMC Article

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