Interpreting Personal Transcriptomes: Personalized Mechanism-Scale Profiling of RNA-Seq Data

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, researchers at the University of Illinois at Chicago hypothesize that the “Functional Analysis of Individual Microarray Expression” (FAIME) method they 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, they 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 phenotypes 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.

Availability – a package allowing for high-throughput analyses of the five studied pathway scoring methods on individual samples is available at:

  • Perez-Rathke A, Li H, Lussier YA. Interpreting Personal Transcriptomes: Personalized Mechanism-Scale Profiling of RNA-Seq Data [article]
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