RNA-Sequencing data offers an opportunity to enable precision medicine, but most methods rely on gene expression alone. To date, no methodology exists to identify and interpret alternative splicing patterns within pathways for an individual patient. This study develops methodology and conducts computational experiments to test the hypothesis that pathway aggregation of subject-specific alternatively spliced genes (ASGs) can inform upon disease mechanisms and predict survival. We propose the N-of-1-pathways Alternatively Spliced (N1PAS) method that takes an individual patient's paired-sample RNA-Seq isoform expression data (e.g., tumor vs. non-tumor, before-treatment vs. during-therapy) and pathway annotations as inputs. N1PAS quantifies the degree of alternative splicing via Hellinger distances followed by two-stage clustering to determine pathway enrichment. We provide a clinically relevant "odds ratio" along with statistical significance to quantify pathway enrichment. We validate our method in clinical samples and find that our method selects relevant pathways (p < 0.05 in 4/6 data sets). Extensive Monte Carlo studies show N1PAS powerfully detects pathway enrichment of ASGs while adequately controlling false discovery rates. Importantly, our studies also unveil highly heterogeneous single-subject alternative splicing patterns that cohort-based approaches overlook. Finally, we apply our patient-specific results to predict cancer survival (FDR < 20%) while providing diagnostics in pursuit of translating transcriptome data into clinically actionable information. Software available at https://github.com/grizant/n1pas/tree/master.
Keywords: Hellinger distance; RNA-Seq; alternative splicing; isoform; local false discovery rate; pathways; precision medicine; systems biology.