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May 9, 2026Journal of the American Medical Informatics Association0 citationsOpen Access

Enhancing validation of case-control omics signatures through “minimalist” single-subject analysis (N-of-1 trials): proof of concept in sepsis

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LWLiam WilsonNPNima PouladiRNRachel F Nelson

Key Points

  • This research aims to assess whether a single-subject study can replicate transcriptomic signatures from small case-control studies in sepsis.
  • Generated a sepsis gene signature comprising 300 differentially expressed genes from a human case-control cohort using general linear models.
  • Assessed reproducibility through single-subject analyses (N-of-1-MixEnrich), paired-sample GLM analyses, and traditional case-control GLM analysis.
  • Analyzed independent datasets for validation across varying cohort sizes.
  • SGS reproducibility in GLM analyses was inconsistent, achieving ∼80% reproducibility in smaller cohorts (n = 5) but stabilizing at >6 subjects.
  • The single-subject-study approach reproduced SGS with 100% reproducibility in 18 individual subjects (n = 1).
  • Conventional GLMs were less effective for small cohorts due to reliance on larger sample sizes.

Abstract

OBJECTIVE: To evaluate if a single-subject study (S3) design, utilizing paired transcriptome samples from the same patient (eg, "sepsis" vs "recovered"), can replicate transcriptomic signatures from small case-control studies, addressing challenges in patient accrual for rare or sub-stratified diseases. METHODS: We generated a sepsis gene signature (SGS) comprising 300 differentially expressed genes (DEGs; FDR 6. Remarkably, the single-subject-study approach consistently reproduced SGS in each of the 18 subjects individually (100% reproducibility; n = 1). DISCUSSION: Conventional GLMs are not designed for single-subject or small cohort analyses due to their dependence on larger samples to mitigate variable dispersion and human heterogeneity. In contrast, S3 methods enhance statistical power by: reducing multiple testing through gene set aggregation, emphasizing concordant changes in pathway activity rather than exact molecular consistency, and exploiting paired samples from the same individual. CONCLUSION: This proof-of-concept demonstrates that S3 designs effectively validate gene expression signatures derived from case-control studies, highlighting their potential in research or clinical trials constrained by small sample sizes. However, further validation and computational simulation are needed to demonstrate scalability to other conditions and sensitivity to validation subject variations from the "average subject" of discovery cohorts.

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Cite This Study

Wilson et al. (2026) studied this question.

synapsesocial.com/papers/69fecfe9b9154b0b82876e18https://doi.org/10.1093/jamia/ocag061
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