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The CNV Package of SVS 7 offers direct import of log ratio data from a number of providers including Affymetrix, Agilent, NimbleGen, 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.
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.
The CNV package employs a powerful optimal segmenting algorithm called Copy Number Analysis Method (CNAM) 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.
Optimal segmenting incorporates a parallelized, unbiased randomization permutation procedure that uses all available cores on your computer. The 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.
SVS comes with an array of resources for learning and utilizing the software to get the most out of your data including tutorials, add-on scripts, example data and projects, and much more.
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. SVS 7 offers a number of tools to not only detect for these data quality problems but correct for them as well. These include:
"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 with our powerful genome browser. 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.