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

• Published in: Bioinformatics (Oxford, England) Vol. 37; no. Supplement_1; pp. i67 - i75
• Main Authors: Aberasturi, Dillon, Pouladi, Nima, Zaim, Samir Rachid, Kenost, Colleen, Berghout, Joanne, Piegorsch, Walter W, Lussier, Yves A
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01-07-2021; Oxford Publishing Limited (England)
• Subjects: Availability; Biological activity; Biological effects; Biomedical Informatics; Control methods; Datasets; Parameters; Patients; Rare diseases; Recall; Transcriptomes
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btab290

Abstract
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, single-subject studies (S3) can compare paired transcriptome samples drawn from the same patient under two conditions (e.g. treated versus 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 differences between very small cohorts by using the effect size of ‘single-subject-study’-derived responsive biological mechanisms.
Results
In each subject, Inter-N-of-1 requires applying previously published S3-type N-of-1-pathways MixEnrich to two paired samples (e.g. diseased versus unaffected tissues) for determining patient-specific enriched genes sets: Odds Ratios (S3-OR) and S3-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 versus 20 to 2 versus 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 versus 3 distinct subjects (regardless of the parameter values), whereas conventional methods outperform Inter-N-of-1 at sample sizes 9 versus 9 and larger. Similar results were obtained in the clinical proof-of-concept dataset.
Availability and implementation
R software is available at Lussierlab.net/BSSD.