Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation.
AffiliationUniv Arizona, Interdisciplinary Program Stat
Univ Arizona, Ctr Biomed Informat & Biostat CB2
Univ Arizona, Inst BIO5
Univ Arizona, Dept Med
Univ Arizona, Dept Math
KeywordsGene expression data
affinity propagation clustering
triple negative breast cancer
MetadataShow full item record
PublisherSAGE PUBLICATIONS LTD
CitationSchissler, A. G., Piegorsch, W. W., & Lussier, Y. A. (2018). Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation. Statistical Methods in Medical Research, 27(12), 3797–3813. https://doi.org/10.1177/0962280217712271
Rights© The Author(s) 2017; article reuse guidelines: sagepub.com/journals-permissions
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractModern 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.
VersionFinal accepted manuscript
SponsorsU.S. National Science Foundation ; U.S. National Institutes of Health [R03ES027394]