A test for detecting differential indirect trans effects between two groups of samples
-
Nimisha Chaturvedi
,
Renée X. de Menezes
Abstract
Integrative analysis of copy number and gene expression data can help in understanding the cis and trans effect of copy number aberrations on transcription levels of genes involved in a pathway. To analyse how these copy number mediated gene-gene interactions differ between groups of samples we propose a new method, named dNET. Our method uses ridge regression to model the network topology involving one gene’s expression level, its gene dosage and the expression levels of other genes in the network. The interaction parameters are estimated by fitting the model per gene for all samples together. However, instead of testing for differential network topology per gene, dNET tests for an overall difference in estimated parameters between two groups of samples and produces a single p-value. With the help of several simulation studies, we show that dNET can detect differential network nodes with high accuracy and low rate of false positives even in the presence of differential cis effects. We also apply dNET to publicly available TCGA cancer datasets and identify pathways where copy number mediated gene-gene interactions differ between samples with cancer stage lower than stage 3 and samples with cancer stage 3 or above.
Acknowledgement
This work has been supported and funded by Netherlands Bioinformatics Centre.
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Articles in the same Issue
- Research Articles
- Assessing genome-wide significance for the detection of differentially methylated regions
- A test for detecting differential indirect trans effects between two groups of samples
- A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data
Articles in the same Issue
- Research Articles
- Assessing genome-wide significance for the detection of differentially methylated regions
- A test for detecting differential indirect trans effects between two groups of samples
- A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data
