Single subject transcriptome analysis to identify functionally signed gene set or pathway activity

Joanne Berghout, Qike Li, Nima Pouladi, Jianrong Li, Yves A. Lussier

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have proposed a method for single subject analysis that uses a mixture model for transcript fold-change clustering from isogenically paired samples, followed by integration of these distributions with Gene Ontology Biological Processes (GO-BP) to reduce dimension and identify functional attributes. We then extended these methods to develop functional signing metrics for gene set process regulation by incorporating biological repressor relationships encoded in GO-BP as negatively_regulates edges. Results revealed reproducible and biologically meaningful signals from analysis of a single subject’s response, opening the door to future transcriptomic studies where subject and resource availability are currently limiting. We used inbred mouse strains fed different diets to provide isogenic biological replicates, permitting rigorous validation of our method. We compared significant genotype-specific GO-BP term results for overlap and rank order across three replicate pairs per genotype, and cross-methods to reference standards (limma+FET, SAM+FET, and GSEA). All single-subject analytics findings were robust and highly reproducible (median area under the ROC curve=0.96, n=24 genotypes x 3 replicates), providing confidence and validation of this approach for analyses in single subjects. R code is available online at

Original languageEnglish (US)
Pages (from-to)400-411
Number of pages12
JournalPacific Symposium on Biocomputing
StatePublished - 2018
Event23rd Pacific Symposium on Biocomputing, PSB 2018 - Kohala Coast, United States
Duration: Jan 3 2018Jan 7 2018


  • Atherosclerosis
  • High fat
  • N-of-1
  • Ontology
  • Reproducibility
  • Transcriptome

ASJC Scopus subject areas

  • General Medicine


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