PT  - JOURNAL ARTICLE
AU  - Weiguang Mao
AU  - Javad Rahimikollu
AU  - Ryan Hausler
AU  - Bernard Ng
AU  - Sara Mostafavi
AU  - Maria Chikina
TI  - DataRemix: a universal data transformation for optimal inference from gene expression datasets
AID  - 10.1101/357467
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 357467
4099  - http://biorxiv.org/content/early/2019/08/27/357467.short
4100  - http://biorxiv.org/content/early/2019/08/27/357467.full
AB  - RNAseq technology provides unprecedented power in the assessment 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. We 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 apply our method to the ROSMAP dataset and we report what to our knwoledge is the first replicable trans-eQTL effect in human brain.