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

Authors: Schissler, A Grant; Piegorsch, Walter W; Lussier, Yves A

Published In Stat Methods Med Res, (2018 12)

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.

PubMed ID: 28552011

MeSH Terms: Algorithms; Computer Simulation; Female; Gene Expression Profiling/statistics & numerical data*; Humans; Models, Statistical*; Monte Carlo Method; Precision Medicine; Sequence Analysis, RNA; Triple Negative Breast Neoplasms/genetics*