ArticleBioinformatics (Oxford, England)2013
Use of autocorrelation scanning in DNA copy number analysis.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A High-Fidelity and Ancestrally Inclusive Patient-Derived Organoid Platform Resolves Cancer Cell Plasticity in Uterine Carcinosarcoma.bioRxiv : the preprint server for biology · 2026Article
- A Single-Cell Atlas of Uterine Carcinosarcoma from Diverse Ancestries.bioRxiv : the preprint server for biology · 2026Article
- Transfer Learning for Error-Contaminated Poisson Regression Models.Statistics in medicine · 2025Article
- Deep whole-genome sequencing of 3 cancer cell lines on 2 sequencing platforms.Scientific reports · 2019Article
- CNARA: reliability assessment for genomic copy number profiles.BMC genomics · 2016Article
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Authors and funding
2 authors.
Funding
Abstract
motivationData 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.
methodWe developed a simple tool, called autocorrelation scanning profile, to assess the dependence of measurement error between neighboring probes.
resultsAutocorrelation 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.
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