4.7 Article Proceedings Paper

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

期刊

BIOINFORMATICS
卷 37, 期 -, 页码 I67-I75

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btab290

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资金

  1. University of Arizona Health Sciences Center for Biomedical Informatics and Biostatistics
  2. BIO5 Institute
  3. National Institutes of Health [U01AI122275, 5R21AI152394]

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The study proposes a new method, Inter-N-of-1, to identify meaningful differences between very small cohorts by using the effect size of 'single-subject-study'-derived responsive biological mechanisms. In both simulations and a clinical proof-of-concept dataset, Inter-N-of-1 showed high precision and recall in cohorts of 3 versus 3 distinct subjects, outperforming conventional methods at smaller sample sizes but being outperformed at larger sample sizes.
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 (S-3) 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 S-3-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 (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 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.

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