DataRemix: a universal data transformation for optimal inference from gene expression datasets

MotivationRNAseq technology provides 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. ResultsWe describe a generalization of SVD-based reconstruction for which the common techniques of whitening, rank-k approximation, and removing the top k principle components are special cases. Our simple three-parameter transformation, DataRemix, can be tuned to reweight the contribution of hidden factors and reveal otherwise hidden biological signals. In particular, we demonstrate that the method can effectively prioritize biological signals over noise without leveraging external dataset-specific knowledge, and can outperform normalization methods that make explicit use of known technical factors. We also 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. AvailabilityDataRemix is an R package which is freely available at GitHub (https://github.com/wgmao/DataRemix). Contactmchikina@pitt.edu