Gong, Ting and Szustakowski, Joseph (2013) DeconRNA-Seq: A Statistical Framework for Deconvolution of He-terogeneous Tissue Samples Based on mRNA-Seq data. Bioinformatics.

Abstract

Summary: For heterogeneous tissues, measurements of gene expression through mRNA-Seq data are confounded by relative proportions of cell types involved. In this note, we present a novel pipeline and methodology: DeconRNA-Seq, an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It adopts a globally optimized non-negative decomposition algorithm through quadratic programming for estimating the mixing proportions of distinctive tissue types in next generation sequencing data. We demonstrated the feasibility and validity of DeconRNA-Seq across a range of mixing levels and sources using mRNA-Seq data mixed in-silico at known concentrations. We validated our computational approach for various benchmark data with high correlation between our predicted cell proportions and the real fractions of tissues. Our study provides a rigorous, quantitative, and high-resolution tool as a prerequisite to utilize mRNA-Seq data. The modularity of package design allows an easy deployment of custom analytical pipelines for data from other high-throughput platforms.

Item Type:	Article
Additional Information:	This is for the submission to Bioinformatics journal for application notes. We have submitted the paper for the OAK before with the OAK ID: 6644. Here, we also wanted to publish the R code/package for the method we developed. Thereforefore, we chhose the "Application Notes" in Bioinformatics, which are short descriptions of novel software or new algorithm implementations. Software or data must be freely available to non-commercial users.
Date Deposited:	13 Oct 2015 13:14
Last Modified:	13 Oct 2015 13:14
URI:	https://oak.novartis.com/id/eprint/8099