Testing for Differentially Expressed Genetic Pathways with Single-Subject N-Of-1 Data in the Presence of Inter-Gene Correlation

• Activity Number: 595 - Recent Methods Development on RNA-Seq Data Analysis
• Type: Contributed
• Date/Time: Wednesday, August 1, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Alfred Schissler* and Walter W Piegorsch and Yves A Lussier
• Companies: University of Nevada, Reno and University of Arizona and University of Arizona
• Keywords: single-subject inference; RNA-seq; gene set; clustering; inter-gene correlation; precision medicine

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.