ArticleNucleic acids research2011
GPHMM: an integrated hidden Markov model for identification of copy number alteration and loss of heterozygosity in complex tumor samples using whole genome SNP arrays.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed, 87 citations in OpenAlex.
- HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data.Genome biology · 2024Article
- HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data.bioRxiv : the preprint server for biology · 2023Article
- A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies.Biostatistics (Oxford, England) · 2022Article
- Article
- A systematic evaluation of copy number alterations detection methods on real SNP array and deep sequencing data.BMC bioinformatics · 2019Article
- Machine Learning for Better Prognostic Stratification and Driver Gene Identification Using Somatic Copy Number Variations in Anaplastic Oligodendroglioma.The oncologist · 2018Article
- Detection of copy number variants and loss of heterozygosity from impure tumor samples using whole exome sequencing data.Oncology letters · 2018Article
- CLImAT-HET: detecting subclonal copy number alterations and loss of heterozygosity in heterogeneous tumor samples from whole-genome sequencing data.BMC medical genomics · 2017Article
- Multi-omics analysis of primary glioblastoma cell lines shows recapitulation of pivotal molecular features of parental tumors.Neuro-oncology · 2017Article
- aCNViewer: Comprehensive genome-wide visualization of absolute copy number and copy neutral variations.PloS one · 2017Article
- A Novel Role forFrontiers in genetics · 2017Article
- CloneCNA: detecting subclonal somatic copy number alterations in heterogeneous tumor samples from whole-exome sequencing data.BMC bioinformatics · 2016Article
- Abundant copy-number loss of CYCLOPS and STOP genes in gastric adenocarcinoma.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2016Article
- Overcoming Therapeutic Resistance in HER2-Positive Breast Cancers with CDK4/6 Inhibitors.Cancer cell · 2016Article
- Statistical Inference in Hidden Markov Models UsingJournal of the American Statistical Association · 2016Article
- A Method for Checking Genomic Integrity in Cultured Cell Lines from SNP Genotyping Data.PloS one · 2016Article
- Alterations of the spindle checkpoint pathway in clinicopathologically aggressive CpG island methylator phenotype clear cell renal cell carcinomas.International journal of cancer · 2015Article
- Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterations.BMC genomics · 2015Article
- A simplicial complex-based approach to unmixing tumor progression data.BMC bioinformatics · 2015Article
- DeAnnCNV: a tool for online detection and annotation of copy number variations from whole-exome sequencing data.Nucleic acids research · 2015Article
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10 authors at 1 institution in 1 country.
Funding
Abstract
There is an increasing interest in using single nucleotide polymorphism (SNP) genotyping arrays for profiling chromosomal rearrangements in tumors, as they allow simultaneous detection of copy number and loss of heterozygosity with high resolution. Critical issues such as signal baseline shift due to aneuploidy, normal cell contamination, and the presence of GC content bias have been reported to dramatically alter SNP array signals and complicate accurate identification of aberrations in cancer genomes. To address these issues, we propose a novel Global Parameter Hidden Markov Model (GPHMM) to unravel tangled genotyping data generated from tumor samples. In contrast to other HMM methods, a distinct feature of GPHMM is that the issues mentioned above are quantitatively modeled by global parameters and integrated within the statistical framework. We developed an efficient EM algorithm for parameter estimation. We evaluated performance on three data sets and show that GPHMM can correctly identify chromosomal aberrations in tumor samples containing as few as 10% cancer cells. Furthermore, we demonstrated that the estimation of global parameters in GPHMM provides information about the biological characteristics of tumor samples and the quality of genotyping signal from SNP array experiments, which is helpful for data quality control and outlier detection in cohort studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.