RT Journal Article
SR Electronic
T1 MINT: A multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 070813
DO 10.1101/070813
A1 F. Rohart
A1 A. Eslami
A1 N. Matigian
A1 S. Bougeard
A1 K-A. Lê Cao
YR 2016
UL http://biorxiv.org/content/early/2016/08/22/070813.abstract
AB The solution to identify a reliable molecular signature in transcriptomics high-throughput experiments is to increase sample size by combining independent but related studies. However, those data sets are generated using different protocols and technological platforms, which results in unwanted systematic variation that strongly confounds the integrative analysis results. We introduce a Multi-variate INTegrative method, MINT, that identifies a highly reproducible, accurate and predictive gene signature to classify sample phenotypes while accounting for platform and study variation. MINT led to superior and unbiased classification performance compared to other existing methods, and identified highly relevant gene signatures when integrating two multi-transcriptomics studies.