title: 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-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.
authors: Department of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA. perezrat@uic.edu
authors: Alan
authors: A
authors: Perez-Rathke
authors: Haiquan
authors: H
authors: Li
authors: Yves A
authors: YA
authors: Lussier
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D057285:Precision Medicine / Q000706:statistics & numerical data*; D012334:RNA, Neoplasm / Q000235:genetics; D012372:ROC Curve; D017423:Sequence Analysis, RNA / Q000706:statistics & numerical data*; D013274:Stomach Neoplasms / Q000235:genetics; D059467:Transcriptome
chemical_list: D012334:RNA, Neoplasm