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- Title
HCMMCNVs: hierarchical clustering mixture model of copy number variants detection using whole exome sequencing technology.
- Authors
Song, Chi; Su, Shih-Chi; Huo, Zhiguang; Vural, Suleyman; Galvin, James E; Chang, Lun-Ching
- Abstract
Summary In this article, we introduce a hierarchical clustering and Gaussian mixture model with expectation-maximization (EM) algorithm for detecting copy number variants (CNVs) using whole exome sequencing (WES) data. The R shiny package 'HCMMCNVs' is also developed for processing user-provided bam files, running CNVs detection algorithm and conducting visualization. Through applying our approach to 325 cancer cell lines in 22 tumor types from Cancer Cell Line Encyclopedia (CCLE), we show that our algorithm is competitive with other existing methods and feasible in using multiple cancer cell lines for CNVs estimation. In addition, by applying our approach to WES data of 120 oral squamous cell carcinoma (OSCC) samples, our algorithm, using the tumor sample only, exhibits more power in detecting CNVs as compared with the methods using both tumors and matched normal counterparts. Availability and implementation HCMMCNVs R shiny software is freely available at github repository https://github.com/lunching/HCMM%5fCNVs.and Zenodo https://doi.org/10.5281/zenodo.4593371. Supplementary information Supplementary data are available at Bioinformatics online.
- Subjects
DNA copy number variations; HIERARCHICAL clustering (Cluster analysis); GAUSSIAN mixture models; ALGORITHMS; SQUAMOUS cell carcinoma; CELL lines
- Publication
Bioinformatics, 2021, Vol 37, Issue 18, p3026
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btab183