Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data

Lai, Weil; Johnson, Mark D.; Kucherlapati, Raju; Park, Peter J.

doi:10.1093/bioinformatics/bti611

Cited by 348 publications

(403 citation statements)

References 35 publications

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“…Different calling algorithms may give different results for an array experiment, and this has been comprehensively discussed [Lai et al, 2005;Pinto et al, 2011]. The sensitivity and specificity are influenced considerably by the choice of the log R-ratio-calling threshold cutoff values for discriminating deletions and duplications from normal diploid copy number.…”

Section: Determination Of Specificity and Sensitivitymentioning

confidence: 99%

“…Circular binary segmentation is one commonly used segmentation algorithm, which divides regions into segments of similar deviation from log 2 ratio of zero [Olshen et al, 2004;Venkatraman and Olshen, 2007]. Analysts should be aware that different algorithms may produce different array results [Lai et al, 2005;Pinto et al, 2011]. Using multiple different calling algorithms can increase confidence, and they may complement each other.…”

Section: Analysis and Interpretation Quality Criteriamentioning

confidence: 99%

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Genome-wide arrays: Quality criteria and platforms to be used in routine diagnostics

et al. 2012

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Whole-genome analysis using genome-wide arrays, also called "genomic arrays," "microarrays," or "arrays," has become the first-tier diagnostic test for patients with developmental abnormalities and/or intellectual disabilities. In addition to constitutional anomalies, genomic arrays are also used to diagnose acquired disorders. Despite the rapid implementation of these technologies in diagnostic laboratories, external quality control schemes (such as CEQA, EMQN, UK NEQAS, and the USA QA scheme CAP) and interlaboratory comparisons show that there are huge differences in quality, interpretation, and reporting among laboratories. We offer guidance to laboratories to help assure the quality of array experiments and to standardize minimum detection resolution, and we also provide guidelines to standardize interpretation and reporting.

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Section: Determination Of Specificity and Sensitivitymentioning

confidence: 99%

Section: Analysis and Interpretation Quality Criteriamentioning

confidence: 99%

Genome-wide arrays: Quality criteria and platforms to be used in routine diagnostics

et al. 2012

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“…Use of segmentation algorithms, e.g., CBS (Venkatraman and Olshen, 2007), to identify breakpoints delineating regions with a joint underlying genomic state was early adopted for aCGH data and has been repeatedly evaluated (Lai et al, 2005). Segmentation-based approaches can be applied with the overall aim to describe the studied genome as a series of segments ascribed specific BAF and LLR states.…”

Section: Delineating Regions Of Genomic Imbalancementioning

confidence: 99%

Genotyping Techniques to Address Diversity in Tumors

Lindgren¹,

Höglund²,

Vallon‐Christersson³

2011

Advances in Cancer Research

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Array based genotyping platforms have during recent years been established as a valuable tool for the characterization of genomic alterations in cancer. The analysis of tumor samples, however, presents challenges for data analysis and interpretation. For example, tumor samples are often admixed with nonaberrant cells that define the tumor microenvironment, such as infiltrating lymphocytes and fibroblasts, or vasculature. Furthermore, tumors often comprise subclones harboring divergent aberrations that are acquired subsequent to the tumorinitiating event. The combined analysis of both genotype and copy number status obtained by array based genotyping platforms provide opportunities to address these challenges. In this review, we present the basic principles for current array based genotyping platforms and how they can be used to infer genotype and copy number for acquired genomic alterations. We describe how these techniques can be used to resolve tumor ploidy, normal cell admixture, and subclonality. We also exemplify how genotyping techniques can be applied in tumor studies to elucidate the hierarchy among tumor clones, and thus, provide means to study clonal expansion and tumor evolution.

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“…The most ideal case, which is considered first, is equivalent to assuming B = 0 in Eq. (10). How to determine the parameters will be described in the next section.…”

Section: Heterogeneity In Ploidy Numbermentioning

confidence: 99%

Combfit: A Normalization Method for Array CGH Data

Oba

Tomioka

Ohira

et al. 2006

ipsjdc

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The recently developed array-based comparative genomic hybridization (array CGH) technique measures DNA copy number aberrations that occur as causes or consequences of cell diseases such as cancers. Conventional array CGH analysis classifies DNA copy number aberrations into three categories: no significant change, significant gain, and significant loss. However, recent improvements in microarray measurement precision enable more quantitative analysis of copy number aberrations. We propose a method, called comb fitting, that extracts a quantitative interpretation from array CGH data. We also propose modifications that allow us to apply comb fitting to cases featuring heterogeneity of local aberrations in DNA copy numbers. By using comb fitting, we can correct the baseline of the fluorescence ratio data measured by array CGH and simultaneously translate them into the amount of changed copy numbers for each small part of the chromosome, such as 0, ±1, ±2, · · ·. Comb fitting is applicable even when a considerable amount of contamination by normal cells exists and when heterogeneity in the ploidy number cannot be neglected.

show abstract

Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data

Cited by 348 publications

References 35 publications

Genome-wide arrays: Quality criteria and platforms to be used in routine diagnostics

Genome-wide arrays: Quality criteria and platforms to be used in routine diagnostics

Genotyping Techniques to Address Diversity in Tumors

Combfit: A Normalization Method for Array CGH Data

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