“Single-subject studies”-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases

Motivation Identifying altered transcripts between very small human cohorts is particularly challenging and is compounded by the low accrual rate of human subjects in rare diseases or sub-stratified common disorders. Yet, s ingle- s ubject s tudies (S 3 ) can compare paired transcriptome samples drawn from the same patient under two conditions (e.g., treated vs pre-treatment) and suggest patient-specific responsive biomechanisms based on the overrepresentation of functionally defined gene sets. These improve statistical power by: (i) reducing the total features tested and (ii) relaxing the requirement of within-cohort uniformity at the transcript level. We propose Inter-N-of-1 , a novel method, to identify meaningful biomechanism differences between very small cohorts by using the effect size of “single-subject-study”-derived responsive biomechanisms. Results In each subject, Inter-N-of-1 requires applying previously published S 3 -type N-of-1-pathways MixEnrich to two paired samples (e.g., diseased vs unaffected tissues) for determining patient-specific e nriched g enes s ets: Odds Ratios (S 3 -OR) and S 3 -variance using Gene Ontology Biological Processes. To evaluate small cohorts, we calculated the precision and recall of Inter-N-of-1 and that of a control method (GLM+EGS) when comparing two cohorts of decreasing sizes (from 20 vs 20 to 2 vs 2) in a comprehensive six-parameter simulation and in a proof-of-concept clinical dataset. In simulations, the Inter-N-of-1 median precision and recall are > 90% and >75% in cohorts of 3 vs 3 distinct subjects (regardless of the parameter values), whereas conventional methods outperform Inter-N-of-1 at sample sizes 9 vs 9 and larger. Similar results were obtained in the clinical proof-of-concept dataset. Availability R software is available at Lussierlab.net/BSSD. Contact [email protected] , [email protected]