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

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 expr

SUBMITTER: Schissler AG 

PROVIDER: S-EPMC5554097 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature