Abstract
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 language | English (US) |
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Pages (from-to) | 3797-3813 |
Number of pages | 17 |
Journal | Statistical Methods in Medical Research |
Volume | 27 |
Issue number | 12 |
DOIs | |
State | Published - Dec 1 2018 |
Keywords
- Gene expression data
- N-of-1
- RNA-seq
- affinity propagation clustering
- exemplar learning
- gene set
- inter-gene correlation
- precision medicine
- single-subject inference
- triple negative breast cancer
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability
- Health Information Management