CNAM

CNAM

The Copy Number Analysis Module (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 analyses on a variety of copy number covariates.

CNAM Optimal Segmenting

CNAM employs a powerful optimal segmenting algorithm using dynamic programming to detect inherited and de novo CNVs on a per-sample (univariate) and multi-sample (multivariate) basis. Unlike Hidden Markov Models, which assume the means of different copy number states are consistent, optimal segmenting properly delineates CNV boundaries in the presence of mosaicism, even at a single probe level, and with controllable sensitivity and false discovery rate.

CNAM Optimal Segmenting now incorporates a new parallelized, unbiased randomization permutation procedure that uses all available cores on your computer. The new permutation procedure replaces a naïve, potentially biased randomization procedure with the unbiased Fisher and Yates method (also known as the Knuth shuffle). An added option allows you to further refine your segments by efficiently removing univariate outliers during the segmentation process.

CNV Data Visualization

"Seeing is believing" with richly interactive data visualization that provides unprecedented whole genome views and easy navigation of your data. Visually detect CNVs across many samples or confirm optimal segmenting results. Generate cluster plots of allele intensities to filter poor quality markers. Visualize CNV association p-values alongside SNP p-values. And when you finalize the views you want, you can save them to a number of publication quality formats, including scalable vector graphics.

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.

» More about Quantile Normalization

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.

» More about CNV Principal Component Analysis

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.

» More about CNV Association Testing

Science Behind CNAM

Learn more about the science behind copy number analysis in CNAM and SVS 7.

» More about the Science behind CNAM

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