biostudies-literatureSchissler AGNIEHS NIH HHSNIAID NIH HHS3797-3813https://www.ebi.ac.uk/biostudies/studies/S-EPMC5554097biostudies-literatureUnknown27(12)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 exprStatistical methods in medical researchTesting for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation.PMC5554097R03 ES027394U01 AI122275P30 ES006694Lussier YAPiegorsch WWSchissler AGfalseTesting for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation.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 expr2018-01-01T00:00:00Z2018 Dec2026-05-03T14:51:15.156Z2020-10-29T12:19:48ZS-EPMC55540972855201110.1177/0962280217712271