How to use cghMCR

Copy number data (arrayCGH or SNP) can be used to identify genomic regions (Regions Of Interest or ROI) showing gains or losses that are common across samples. Existing algorithms, MCR (Aguirre et. al. 2004), GISTIC(), or GTS(), for the identification of ROI rely on the probe level data, which may be a concern when array density increases (to 1 million for example) or when data generated by arrays of different densities need to be analyzed together. The cghMCR initially implemented simplified version of MCR. An alternative approach (Segment Gain Or Loss or SGOL) were then added by applying a modified version of GISTIC algorithm so that the computations can be done based on segmented data. This vignette demonstrate how to use cghMCR to identify genomic alterations across samples profiled using copy number platforms using the two approaches (e. g. arrayCGH or SNP array). Since both approaches use the output of CBS (DNAcopy), the first section of the vignette shows how to generate the segmented data using DNAcopy based on three raw arrayCGH data files to reduce the length of time required for the calculation.