Genomic regression analysis of coordinated expression

Ling Cai, Qiwei Li, Yi Du, Jonghyun Yun, Yang Xie, Ralph J. DeBerardinis () and Guanghua Xiao ()
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Ling Cai: Children’s Medical Center Research Institute at UT Southwestern Medical Center
Qiwei Li: Quantitative Biomedical Research Center at UT Southwestern Medical Center
Yi Du: Department of Bioinformatics at UT Southwestern Medical Center
Jonghyun Yun: Department of Mathematics at University of Texas at Arlington
Yang Xie: Quantitative Biomedical Research Center at UT Southwestern Medical Center
Ralph J. DeBerardinis: Children’s Medical Center Research Institute at UT Southwestern Medical Center
Guanghua Xiao: Quantitative Biomedical Research Center at UT Southwestern Medical Center

Nature Communications, 2017, vol. 8, issue 1, 1-10

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-02181-0

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