ContamDE: Differential Expression Analysis of RNA-Seq Data for Contaminated Tumor Samples

• Activity Number: 106
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Hong Zhang*
• Companies: Fudan University
• Keywords: next generation RNA sequencing ; differential gene expression ; cancer ; cell contamination

Abstract

Accurate detection of differentially expressed (DE) genes between tumor and normal samples is a primary approach of cancer-related biomarker identification. Due to the infiltration of tumor surrounding normal cells, the expression data derived from tumor samples would always be contaminated with normal cells. Ignoring such cellular contamination would deflate the power of detecting DE genes and further confound the biological interpretation of the analysis results. For the time being, there does not exist any differential expression analysis approach for RNA-seq data in literature that can properly account for the contamination of tumor samples. We develop a new method 'contamDE' based on a novel statistical model that associates RNA-seq expression levels with cell types. In our simulation studies, contamDE could be much more powerful than the benchmarks edgeR and DESeq2 that ignore the contamination when the cellular contamination was present. In the application to a Drosophila melanogaster study and two cancer studies, contamDE returned more meaningful results than edgeR and DESeq2.