Interpreting personal transcriptomes: personalized mechanism-scale profiling of RNA-seq data.

ABSTRACT: Despite thousands of reported studies unveiling gene-level signatures for complex diseases, few of these techniques work at the single-sample level with explicit underpinning of biological mechanisms. This presents both a critical dilemma in the field of personalized medicine as well as a plethora of opportunities for analysis of RNA-seq data. In this study, we hypothesize that the "Functional Analysis of Individual Microarray Expression" (FAIME) method we developed could be smoothly extended to RNA-seq data and unveil intrinsic underlying mechanism signatures across different scales of biological data for the same complex disease. Using publicly available RNA-seq data for gastric cancer, we confirmed the effectiveness of this method (i) to translate each sample transcriptome to pathway-sc

SUBMITTER: Perez-Rathke A 

PROVIDER: S-EPMC3595401 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature