CNAM, in conjunction with other SVS 7 modules, offers a complete set of tools for processing raw intensity data, identifying regions of copy number variation (CNV), visualizing copy number data, and performing association analysis on a variety of copy number covariates.

CNV Data Processing

CNAM offers direct import of log ratio data from a number of providers, including Affymetrix, Agilent, and Illumina. For Affymetrix CEL files (500K, 5.0, and 6.0) a powerful processing tool enables you to run quantile normalization on the A and B probe intensities, including virtual array generation to merge CN and SNP probes or multiple arrays (e.g. NSP and STY). This process scales to thousands of samples, and can use any sample set as a reference. Learn more

CNV Quality Assurance

For both microarray and aCGH data, significant bias can be introduced by batch effects (plate, machine, and site variation), genomics waves, and population stratification. Other sources of variation include sample extraction and preparation procedures, cell types, temperature fluctuation, and even ambient ozone levels in a lab. These can lead to complications ranging from poorly defined segments to false and non-replicable findings. Utilizing a powerful PCA approach enables you to simultaneously correct for all experimental artifacts, while significantly improving signal-to-noise ratios. Learn more

CNV Association Testing

A number of covariate generation procedures enable you to perform association testing on raw or PCA corrected log ratios, CNV segment means, and discretized values based on three- and two-state models representing loss, neutral, and gain.  Perform numeric association tests or advanced linear and logistic regression with CNV covariates alone or in combination with other genetic markers and phenotypic variables. Learn more