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

Alan Perez-Rathke, L. Haiquan, Yves A. Lussier

Research output: Contribution to journalConference article

7 Scopus citations

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-scale scores, (ii) to predict deregulated pathways in gastric cancer against gold standards (FDR<5%, Precision=75%, Recall = 92%), and (iii) to predict pheno types in an independent dataset and expression platform (RNA-seq vs microarrays, Fisher Exact Test p<10 -6 ). Measuring at a single-sample level, FAIME could differentiate cancer samples from normal ones; furthermore, it achieved comparative performance in identifying differentially expressed pathways as compared to state-of-the-art cross-sample methods. These results motivate future work on mechanism-level biomarker discovery predictive of diagnoses, treatment, and therapy.

Original languageEnglish (US)
Pages (from-to)159-170
Number of pages12
JournalPacific Symposium on Biocomputing
StatePublished - Jan 1 2013
Externally publishedYes
Event18th Pacific Symposium on Biocomputing, PSB 2013 - Kohala Coast, United States
Duration: Jan 3 2013Jan 7 2013

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics

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