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.

UNLABELLED: Although a large collection of classification software packages exist in R, a new generic framework for linking custom classification functions with classification performance measures is needed. A generic classification framework has been designed and implemented as an R package in an object oriented style. Its design places emphasis on parallel processing, reproducibility and extensibility. Finally, a comprehensive set of performance measures are available to ease post-processing. Taken together, these important characteristics enable rapid and reproducible benchmarking of alternative classifiers. AVAILABILITY AND IMPLEMENTATION: ClassifyR is implemented in R and can be obtained from the Bioconductor project: http://bioconductor.org/packages/release/bioc/html/ClassifyR.html.

SUMMARY: Epigenetics, the study of heritable somatic phenotypic changes not related to DNA sequence, has emerged as a critical component of the landscape of gene regulation. The epigenetic layers, such as DNA methylation, histone modifications and nuclear architecture are now being extensively studied in many cell types and disease settings. Few software tools exist to summarize and interpret these datasets. We have created a toolbox of procedures to interrogate and visualize epigenomic data (both array- and sequencing-based) and make available a software package for the cross-platform R language. AVAILABILITY: The package is freely available under LGPL from the R-Forge web site (http://repitools.r-forge.r-project.org/) CONTACT: [email protected].