Tutorial: Copy Number Analysis in SVS 7

Check out the following tutorial for detailed instructions on performing CNV analysis in SVS 7. Workflows include: processing raw intensity data, performing quality assurance, identifying regions of copy number variation (CNV), visualizing copy number data, and performing association analysis on a variety of CNV covariates.

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Processing Intensity Data

SVS 7 offers direct import of log ratio data from a number of providers, including Affymetrix, Agilent, Illumina, and Nimblegen. For Affymetrix CEL files (500K, 5.0, 6.0, 2.7M Cyto, etc.) 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.

Copy Number Detection with Univariate and Multivariate Optimal Segmenting

SVS 7 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.

Detecting and Correcting for Plate/Batch Effects,
Genomic Waves, and other Quality Issues

Webcast: Quality Assurance in CNV and SNP Studies

The following webcast highlights a number of methodologies for quality assurance, the most important step in CNV and SNP GWAS.

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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 no only detect for these data quality problems but correct for them as well. These include:

• Derivative log ratio spread (DSLR) to measure signal-to-noise ratios in log ratio data.
• Extreme value distribution to detect samples with an excess of large positive and negative copy number regions.
• Gender concordance to detect misreported samples.
• Chromosomal means to detect cell line artifacts.
• SNP call rates to filter poorly called markers when SNP probes are available.
• Principal component analysis (PCA) designed to simultaneously correct for multiple quality issues (in addition to population stratification), while significantly improving signal-to-noise ratios.
• Genomic wave detection and correction from Diskin, et. al. to control for wave effects.

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. You can then perform numeric association tests or advanced linear and logistic regression with CNV covariates alone or in combination with other genetic markers and phenotypic variables.

Science Behind CNAM

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

» More about the Science behind CNAM