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A new method for constructing tumor specific gene co-expression networks based on samples with tumor purity heterogeneity

Author:
Petralia, Francesca, Wang, Li, Peng, Jie, Yan, Arthur, Zhu, Jun, Wang, Pei
Source:
Bioinformatics 2018 v.34 no.13 pp. i528
ISSN:
1460-2059
Subject:
animal ovaries, bioinformatics, genes, models, neoplasm cells, ovarian neoplasms, protein synthesis, stromal cells
Abstract:
Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample. Using extensive synthetic data experiments, we demonstrate that TSNet outperforms a standard graphical model which does not account for TPH. We then apply TSNet to estimate tumor specific gene co-expression networks based on TCGA ovarian cancer RNAseq data. We identify novel co-expression modules and hub structure specific to tumor cells. R codes can be found at https://github.com/petraf01/TSNet. Supplementary data are available at Bioinformatics online.
Agid:
6249559