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Journal Articles Bioinformatics Year : 2019

Exploring drivers of gene expression in the cancer genome atlas


Motivation: The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generaing, curating and publicly releasing deeply measured molecular data from thousands of tumor samples. In particular, gene expression measures, both within and across cancer types, have been used to determine the genes and proteins that are active in tumor cells. Results: To more thoroughly investigate the behavior of gene expression in TCGA tumor samples, we introduce a statistical framework for partitioning the variation in gene expression due to a var- iety of molecular variables including somatic mutations, transcription factors (TFs), microRNAs, copy number alternations, methylation and germ-line genetic variation. As proof-of-principle, we identify and validate specific TFs that influence the expression of PTPN14 in breast cancer cells. Availability and implementation: We provide a freely available, user-friendly, browseable inter- active web-based application for exploring the results of our transcriptome-wide analyses across 17 different cancers in TCGA at http://ls-shiny-prod.uwm.edu/edge_in_tcga. All TCGA Open Access tier data are available at the Broad Institute GDAC Firehose and were downloaded using the TCGA2STAT R package. TCGA Controlled Access tier data are available via controlled access through the Genomic Data Commons (GDC). R scripts used to download, format and analyze the data and produce the interactive R/Shiny web app have been made available on GitHub at https:// github.com/andreamrau/EDGE-in-TCGA

Dates and versions

hal-02625326 , version 1 (26-05-2020)



Andrea Rau, Michael Flister, Hallgeir Rui, Paul L. Auer. Exploring drivers of gene expression in the cancer genome atlas. Bioinformatics, 2019, 35 (1), pp.62-68. ⟨10.1093/bioinformatics/bty551⟩. ⟨hal-02625326⟩
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