A Hidden Markov Modeling Approach for Identifying Tumor Subclones in Next-Generation Sequencing Studies.
Allele-specific copy number alteration (ASCNA) analysis is for identifying copy number abnormalities in tumor cells. Unlike normal cells, tumor cells are heterogeneous with a combination of dominant and minor subclones with distinct copy number profiles. Estimating the clonal proportion and identifying main and subclone genotypes across the genome is important for understanding tumor progression. Several ASCNA tools have recently been developed, but they have been limited to the identification of subclone regions, and not the genotype of the subclones. This package uses a hidden Markov model-based approach that estimates both sub-clone region as well as region-specific subclone genotype and clonal proportion. A hidden state variable is specified which represents the conglomeration of clonal genotypes and subclone status. A two-step algorithm for parameter estimation is implemented, where in the first step, a standard hidden Markov model with this conglomerated state space is fit. Then, in the second step, region-specific estimates of the clonal proportions are obtained by maximizing region-specific pseudo-likelihoods.
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- Unix O/S: subHMM_0.1.3.tar.gz
- Windows O/S: subHMM_0.1.3.zip
Download Source Code
Choo-Wosoba H., Albert P.S., Zhu B. A hidden Markov modeling approach for identifying tumor subclones in next-generation sequencing studies. Biostatistics 2020: kxaa013
Questions, please contact Dr. Bin Zhu (email@example.com)