Accurate detection of aneuploidies in array CGH and gene expression microarray data

Myers, Chad L.; Dunham, Maitreya J.; Kung, Sun Yuan; Troyanskaya, Olga G.

doi:10.1093/bioinformatics/bth440

Cited by 103 publications

(66 citation statements)

References 37 publications

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“…The algorithms CBS, CLAC and ACE (Lingjaerde et al, 2005) detected two distinct regions instead of three. ChARM (Myers et al, 2004) grouped all the high log 2 intensity ratios into a single region. The HMM algorithm of Fridlyand et al (2004) did not detect the amplifications.…”

Section: Comparisons With Some Existing Methodsmentioning

confidence: 99%

Applications of MOSS for Discrete Multi-Way Data

Dey¹,

Ghosh²,

Mallick³

2010

Bayesian Modeling in Bioinformatics

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Genomic alterations have been linked to the development and progression of cancer. The technique of Comparative Genomic Hybridization (CGH) yields data consisting of fluorescence intensity ratios of test and reference DNA samples. The intensity ratios provide information about the number of copies in DNA. Practical issues such as the contamination of tumor cells in tissue specimens and normalization errors necessitate the use of statistics for learning about the genomic alterations from array-CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data.We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Since the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulation-based inference. Publicly available data on pancreatic adenocarcinoma, glioblastoma multiforme and breast cancer are analyzed, and comparisons are made with some widely-used algorithms to illustrate the reliability and success of the technique. and normalization errors necessitate the use of statistics for learning about the genomic alterations from array-CGH data. As increasing amounts of array CGH data become available, there is a growing need for automated algorithms for characterizing genomic profiles. Specifically, there is a need for algorithms that can identify gains and losses in the number of copies based on statistical considerations, rather than merely detect trends in the data.We adopt a Bayesian approach, relying on the hidden Markov model to account for the inherent dependence in the intensity ratios. Posterior inferences are made about gains and losses in copy number. Localized amplifications (associated with oncogene mutations) and deletions (associated with mutations of tumor suppressors) are identified using posterior probabilities. Global trends such as extended regions of altered copy number are detected. Since the posterior distribution is analytically intractable, we implement a Metropolis-within-Gibbs algorithm for efficient simulationbased inference. Publicly available data on pancreatic adenocarcinoma, glioblastoma multiforme and breast cancer are analyzed, and comparisons are made with some widely-used algorithms to illustrate the reliability and success of the technique.

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Section: Comparisons With Some Existing Methodsmentioning

confidence: 99%

Applications of MOSS for Discrete Multi-Way Data

Dey¹,

Ghosh²,

Mallick³

2010

Bayesian Modeling in Bioinformatics

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show abstract

“…A moving window smoother based on means is implemented in the CGH-Explorer program (Lingjaerde et al, 2005) and in the CGH-Miner based on Clustering Along Chromosomes method , while one based on medians is implemented in the ChARM package (Myers et al, 2004). where #(·) is the count function.…”

Section: Moving Window Approaches-mentioning

confidence: 99%

Statistical issues in the analysis of DNA Copy Number Variations

Kennedy

Erickson

Wojczynski

et al. 2008

IJCBDD

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Approaches to assess copy number variation have advanced rapidly and are being incorporated into genetic studies. While the technology exists for CNV genotyping, a further understanding and discussion of how to use the CNV data for association analyses is warranted. We present the options available for processing and analysing CNV data. We break these steps down into choice of genotyping platform, normalisation of the array data, calling algorithm, and statistical analysis.

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“…Whether the soft partition is more advantageous over the hard partition? One often used approach is computing the partition coefficient V PC given below [7]: (12) which measures the amount of overlap between clusters. In this definition, V PC is inversely proportional to the overall average overlap between pairs of fuzzy subsets.…”

Section: Validation Of Clusteringmentioning

confidence: 99%

A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

Wang

Gunampally

Chen³

et al. 2007

J Sign Process Syst Sign Image

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Despite the widespread application of microarray imaging for biomedical imaging research, barriers still exist regarding its reliability for clinical use. A critical major problem lies in accurate spot segmentation and the quantification of gene expression level (mRNA) from the microarray images. A variety of commercial and research freeware packages are available, but most cannot handle array spots with complex shapes such as donuts and scratches. Clustering approaches such as k-means and mixture models were introduced to overcome this difficulty, which use the hard labeling of each pixel. In this paper, we apply fuzzy clustering approaches for spot segmentation, which provides soft labeling of the pixel. We compare several fuzzy clustering approaches for microarray analysis and provide a comprehensive study of these approaches for spot segmentation. We show that possiblistic c-means clustering (PCM) provides the best performance in terms of stability criterion when testing on both a variety of simulated and real microarray images. In addition, we compared three statistical criteria in measuring gene expression levels and show that a new asymptotically unbiased statistic is able to quantify the gene expression level more accurately.

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Accurate detection of aneuploidies in array CGH and gene expression microarray data

Abstract: Code available by request from the authors and on Web supplement at http://function.cs.princeton.edu/ChARM/

Cited by 103 publications

References 37 publications

Applications of MOSS for Discrete Multi-Way Data

Applications of MOSS for Discrete Multi-Way Data

Statistical issues in the analysis of DNA Copy Number Variations

A Comparison of Fuzzy Clustering Approaches for Quantification of Microarray Gene Expression

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