TY - JOUR T1 - DataRemix: a universal data transformation for optimal inference from gene expression datasets JF - bioRxiv DO - 10.1101/357467 SP - 357467 AU - Weiguang Mao AU - Ryan Hausler AU - Maria Chikina Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/08/27/357467.abstract N2 - RNAseq technology provides an unprecedented power in the assesment of the transcription abundance and can be used to perform a variety of downstream tasks such as inference of gene-correlation network and eQTL discovery. However, raw gene expression values have to be normalized for nuisance biological variation and technical covariates, and different normalization strategies can lead to dramatically different results in the downstream study. Here we present a simple three-parameter transformation, DataRemix, which can greatly improve the biological utility of gene expression datasets without any specific knowledge on the dataset. As we optimize the transformation with respect to the downstream biological objective, this parametric framework reweighs the contribution of each hidden factor and makes the biological signals visible. We demonstrate that DataRemix can outperform normalization methods which make explicit use of dataset specific technical factors. Also we show that DataRemix can be efficiently optimized via Thompson Sampling approach, which makes it feasible for computationally expensive objectives such as eQTL analysis. Finally we reanalyze the Depression Gene Networks (DGN) dataset, and we highlight new trans-eQTL networks which were not reported in the initial study. ER -