Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation

A. Grant Schissler, Walter W Piegorsch, Yves A Lussier

Research output: Contribution to journalArticle

4 Scopus citations


Modern precision medicine increasingly relies on molecular data analytics, wherein development of interpretable single-subject (“N-of-1”) signals is a challenging goal. A previously developed global framework, N-of-1-pathways, employs single-subject gene expression data to identify differentially expressed gene set pathways in an individual patient. Unfortunately, the limited amount of data within the single-subject, N-of-1 setting makes construction of suitable statistical inferences for identifying differentially expressed gene set pathways difficult, especially when non-trivial inter-gene correlation is present. We propose a method that exploits external information on gene expression correlations to cluster positively co-expressed genes within pathways, then assesses differential expression across the clusters within a pathway. A simulation study illustrates that the cluster-based approach exhibits satisfactory false-positive error control and reasonable power to detect differentially expressed gene set pathways. An example with a single N-of-1 patient’s triple negative breast cancer data illustrates use of the methodology.

Original languageEnglish (US)
JournalStatistical Methods in Medical Research
Publication statusAccepted/In press - Jan 1 2017



  • affinity propagation clustering
  • exemplar learning
  • Gene expression data
  • gene set
  • inter-gene correlation
  • N-of-1
  • precision medicine
  • RNA-seq
  • single-subject inference
  • triple negative breast cancer

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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