Dataset Information

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

ABSTRACT:

Motivation

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.

Results

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.

Availability and implementation

R codes can be found at https://github.com/petraf01/TSNet.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Petralia F

PROVIDER: S-EPMC6022554 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Publications

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

Petralia Francesca F Wang Li L Peng Jie J Yan Arthur A Zhu Jun J Wang Pei P

Bioinformatics (Oxford, England) 20180701 13

<h4>Motivation</h4>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 Spe ...[more]

PMID: 29949994

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Dataset Information

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

Motivation

Results

Availability and implementation

Supplementary information

Publications

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

Similar Datasets

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