Publication Date:
2013-10-19
Description:
Motivation: Data quality is a critical issue in the analyses of DNA copy number alterations obtained from microarrays. It is commonly assumed that copy number alteration data can be modeled as piecewise constant and the measurement errors of different probes are independent. However, these assumptions do not always hold in practice. In some published datasets, we find that measurement errors are highly correlated between probes that interrogate nearby genomic loci, and the piecewise-constant model does not fit the data well. The correlated errors cause problems in downstream analysis, leading to a large number of DNA segments falsely identified as having copy number gains and losses. Method: We developed a simple tool, called autocorrelation scanning profile, to assess the dependence of measurement error between neighboring probes. Results: Autocorrelation scanning profile can be used to check data quality and refine the analysis of DNA copy number data, which we demonstrate in some typical datasets. Contact: lzhangli@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.
Print ISSN:
1367-4803
Electronic ISSN:
1460-2059
Topics:
Biology
,
Computer Science
,
Medicine
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