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dc.contributor.authorLi, Cheng
dc.contributor.authorBeroukhim, Rameen
dc.contributor.authorWeir, Barbara Ann
dc.contributor.authorWinckler, Wendy
dc.contributor.authorGarraway, Levi Alexander
dc.contributor.authorSellers, William R
dc.contributor.authorMeyerson, Matthew Langer
dc.date.accessioned2011-05-13T01:10:50Z
dc.date.issued2008
dc.identifier.citationLi, Cheng, Rameen Beroukhim, Barbara A. Weir, Wendy Winckler, Levi A. Garraway, William R. Sellers, and Matthew Meyerson. 2008. Major copy proportion analysis of tumor samples using SNP arrays. BMC Bioinformatics 9: 204.en_US
dc.identifier.issn1471-2105en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4889440
dc.description.abstractBackground: Single nucleotide polymorphisms (SNPs) are the most common genetic variations in the human genome and are useful as genomic markers. Oligonucleotide SNP microarrays have been developed for high-throughput genotyping of up to 900,000 human SNPs and have been used widely in linkage and cancer genomics studies. We have previously used Hidden Markov Models (HMM) to analyze SNP array data for inferring copy numbers and loss-of-heterozygosity (LOH) from paired normal and tumor samples and unpaired tumor samples. Results: We proposed and implemented major copy proportion (MCP) analysis of oligonucleotide SNP array data. A HMM was constructed to infer unobserved MCP states from observed allele-specific signals through emission and transition distributions. We used 10 K, 100 K and 250 K SNP array datasets to compare MCP analysis with LOH and copy number analysis, and showed that MCP performs better than LOH analysis for allelic-imbalanced chromosome regions and normal contaminated samples. The major and minor copy alleles can also be inferred from allelic-imbalanced regions by MCP analysis. Conclusion: MCP extends tumor LOH analysis to allelic imbalance analysis and supplies complementary information to total copy numbers. MCP analysis of mixing normal and tumor samples suggests the utility of MCP analysis of normal-contaminated tumor samples. The described analysis and visualization methods are readily available in the user-friendly dChip software.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi:10.1186/1471-2105-9-204en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC2375907/pdf/en_US
dash.licenseLAA
dc.titleMajor Copy Proportion Analysis of Tumor Samples Using SNP Arraysen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalBMC Bioinformaticsen_US
dash.depositing.authorGarraway, Levi Alexander
dc.date.available2011-05-13T01:10:50Z
dash.affiliation.otherSPH^Biostatisticsen_US
dash.affiliation.otherHMS^Medicine-Brigham and Women's Hospitalen_US
dash.affiliation.otherHMS^Medicine-Brigham and Women's Hospitalen_US
dash.affiliation.otherHMS^Pathologyen_US
dc.identifier.doi10.1186/1471-2105-9-204*
dash.contributor.affiliatedWeir, Barbara Ann
dash.contributor.affiliatedMeyerson, Matthew
dash.contributor.affiliatedLi, Cheng
dash.contributor.affiliatedGarraway, Levi
dash.contributor.affiliatedBeroukhim, Rameen


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