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- Title
DCARS: differential correlation across ranked samples.
- Authors
Ghazanfar, Shila; Strbenac, Dario; Ormerod, John T; Yang, Jean Y H; Patrick, Ellis
- Abstract
Motivation Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena such as the aetiology of cancer. We have developed an approach for quantifying how changes in the association between pairs of genes may inform the outcome of interest called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the dichotomisation of samples into two distinct classes and can identify differences in gene correlation across early, mid or late stages of the outcome of interest. Results When we evaluated DCARS against the typical Fisher Z-transformation test for differential correlation, as well as a typical approach testing for interaction within a linear model, on real TCGA data, DCARS significantly ranked gene pairs containing known cancer genes more highly across several cancers. Similar results are found with our simulation study. DCARS was applied to 13 cancers datasets in TCGA, revealing several distinct relationships for which survival ranking was found to be associated with a change in correlation between genes. Furthermore, we demonstrated that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multi-layered and complex data. Availability and implementation DCARS R package and sample data are available at https://github.com/shazanfar/DCARS. Publicly available data from The Cancer Genome Atlas (TCGA) was used using the TCGABiolinks R package. Supplementary Files and DCARS R package is available at https://github.com/shazanfar/DCARS. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
GENOMES; GENE expression; MOLECULAR genetics; BIOINFORMATICS; GENETIC mutation
- Publication
Bioinformatics, 2019, Vol 35, Issue 5, p823
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/bty698