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Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays.

Lin S, Carvalho B, Cutler DJ, Arking DE, Chakravarti A, Irizarry RA - Genome Biol. (2008)

Bottom Line: We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed).Also, we tie our call confidence metric to percent accuracy.We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms.

View Article: PubMed Central - HTML - PubMed

Affiliation: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, N. Broadway, Baltimore, MD 21205, USA.

ABSTRACT
Multiple algorithms have been developed for the purpose of calling single nucleotide polymorphisms (SNPs) from Affymetrix microarrays. We extend and validate the algorithm CRLMM, which incorporates HapMap information within an empirical Bayes framework. We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed). Also, we tie our call confidence metric to percent accuracy. We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms.

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Related in: MedlinePlus

Robustness to bad quality chips. Accuracy is plotted against confidence thresholds for various datasets. In other words, the data in Figure 1 are plotted again except that the confidence measures used previously to achieve specified drop rates are now placed on the x-axis. Results of all HapMap datasets are shown from (a) BRLMM and (b) CRLMM. (c) Accuracy versus confidence plots (ACPs) are made for BRLMM (purple) and CRLMM (orange). The points are further stratified by call type according to the HapMap gold standard. The STY and NSP are the array types described in the text. Hmz and Htz are abbreviations for homozygous and heterozygous, respectively.
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Figure 3: Robustness to bad quality chips. Accuracy is plotted against confidence thresholds for various datasets. In other words, the data in Figure 1 are plotted again except that the confidence measures used previously to achieve specified drop rates are now placed on the x-axis. Results of all HapMap datasets are shown from (a) BRLMM and (b) CRLMM. (c) Accuracy versus confidence plots (ACPs) are made for BRLMM (purple) and CRLMM (orange). The points are further stratified by call type according to the HapMap gold standard. The STY and NSP are the array types described in the text. Hmz and Htz are abbreviations for homozygous and heterozygous, respectively.

Mentions: Not only is the BRLMM confidence metric invalid for poor quality chips, it corresponds to different accuracies from dataset to dataset. Figure 3a,b show plots similar to the ADPs as before for datasets of different quality and from different labs; however, the drop rate is replaced with confidence metric thresholds on the abscissa. The plot for BRLMM (Figure 3a) shows a wide variation in accuracy across different datasets for any given confidence threshold. The implication of this finding is that a BRLMM confidence threshold found to give an acceptable accuracy rate in distal analyses for one dataset may not apply to another set. In contrast, the plot for CRLMM (Figure 3b) demonstrates that its confidence measure has greater robustness to laboratory and chip quality effects.


Validation and extension of an empirical Bayes method for SNP calling on Affymetrix microarrays.

Lin S, Carvalho B, Cutler DJ, Arking DE, Chakravarti A, Irizarry RA - Genome Biol. (2008)

Robustness to bad quality chips. Accuracy is plotted against confidence thresholds for various datasets. In other words, the data in Figure 1 are plotted again except that the confidence measures used previously to achieve specified drop rates are now placed on the x-axis. Results of all HapMap datasets are shown from (a) BRLMM and (b) CRLMM. (c) Accuracy versus confidence plots (ACPs) are made for BRLMM (purple) and CRLMM (orange). The points are further stratified by call type according to the HapMap gold standard. The STY and NSP are the array types described in the text. Hmz and Htz are abbreviations for homozygous and heterozygous, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2643934&req=5

Figure 3: Robustness to bad quality chips. Accuracy is plotted against confidence thresholds for various datasets. In other words, the data in Figure 1 are plotted again except that the confidence measures used previously to achieve specified drop rates are now placed on the x-axis. Results of all HapMap datasets are shown from (a) BRLMM and (b) CRLMM. (c) Accuracy versus confidence plots (ACPs) are made for BRLMM (purple) and CRLMM (orange). The points are further stratified by call type according to the HapMap gold standard. The STY and NSP are the array types described in the text. Hmz and Htz are abbreviations for homozygous and heterozygous, respectively.
Mentions: Not only is the BRLMM confidence metric invalid for poor quality chips, it corresponds to different accuracies from dataset to dataset. Figure 3a,b show plots similar to the ADPs as before for datasets of different quality and from different labs; however, the drop rate is replaced with confidence metric thresholds on the abscissa. The plot for BRLMM (Figure 3a) shows a wide variation in accuracy across different datasets for any given confidence threshold. The implication of this finding is that a BRLMM confidence threshold found to give an acceptable accuracy rate in distal analyses for one dataset may not apply to another set. In contrast, the plot for CRLMM (Figure 3b) demonstrates that its confidence measure has greater robustness to laboratory and chip quality effects.

Bottom Line: We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed).Also, we tie our call confidence metric to percent accuracy.We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms.

View Article: PubMed Central - HTML - PubMed

Affiliation: McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, N. Broadway, Baltimore, MD 21205, USA.

ABSTRACT
Multiple algorithms have been developed for the purpose of calling single nucleotide polymorphisms (SNPs) from Affymetrix microarrays. We extend and validate the algorithm CRLMM, which incorporates HapMap information within an empirical Bayes framework. We find CRLMM to be more accurate than the Affymetrix default programs (BRLMM and Birdseed). Also, we tie our call confidence metric to percent accuracy. We intend that our validation datasets and methods, refered to as SNPaffycomp, serve as standard benchmarks for future SNP calling algorithms.

Show MeSH
Related in: MedlinePlus