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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.
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Details
1 Children’s Medical Center Research Institute at UT Southwestern Medical Center, Dallas, TX, USA; Quantitative Biomedical Research Center at UT Southwestern Medical Center, Dallas, TX, USA
2 Quantitative Biomedical Research Center at UT Southwestern Medical Center, Dallas, TX, USA
3 Department of Bioinformatics at UT Southwestern Medical Center, Dallas, TX, USA
4 Department of Mathematics at University of Texas at Arlington, Arlington, TX, USA
5 Children’s Medical Center Research Institute at UT Southwestern Medical Center, Dallas, TX, USA