{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Schissler AG"],"funding":["NIEHS NIH HHS","NIAID NIH HHS"],"pagination":["3797-3813"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5554097"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(12)"],"pubmed_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"],"journal":["Statistical methods in medical research"],"pubmed_title":["Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation."],"pmcid":["PMC5554097"],"funding_grant_id":["R03 ES027394","U01 AI122275","P30 ES006694"],"pubmed_authors":["Lussier YA","Piegorsch WW","Schissler AG"],"additional_accession":[]},"is_claimable":false,"name":"Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation.","description":"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","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Dec","modification":"2026-05-03T14:51:15.156Z","creation":"2020-10-29T12:19:48Z"},"accession":"S-EPMC5554097","cross_references":{"pubmed":["28552011"],"doi":["10.1177/0962280217712271"]}}