PT  - JOURNAL ARTICLE
AU  - Aaron M. Smith
AU  - Jonathan R. Walsh
AU  - John Long
AU  - Craig B. Davis
AU  - Peter Henstock
AU  - Martin R. Hodge
AU  - Mateusz Maciejewski
AU  - Xinmeng Jasmine Mu
AU  - Stephen Ra
AU  - Shanrong Zhao
AU  - Daniel Ziemek
AU  - Charles K. Fisher
TI  - Deep learning of representations for transcriptomics-based phenotype prediction
AID  - 10.1101/574723
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 574723
4099  - http://biorxiv.org/content/early/2019/03/15/574723.short
4100  - http://biorxiv.org/content/early/2019/03/15/574723.full
AB  - The ability to predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. This task is complicated because expression data are high dimensional whereas each experiment is usually small (e.g., ∼20,000 genes may be measured for ∼100 subjects). However, thousands of transcriptomics experiments with hundreds of thousands of samples are available in public repositories. Can representation learning techniques leverage these public data to improve predictive performance on other tasks? Here, we report a comprehensive analysis using different gene sets, normalization schemes, and machine learning methods on a set of 24 binary and multiclass prediction problems and 26 survival analysis tasks. Methods that combine large numbers of genes outperformed single gene methods, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses.