HetFHMM：一种基于因子隐马尔可夫模型推断肿瘤异质性的新方法
HetFHMM: A novel approach to infer tumor heterogeneity using factorial Hidden Markov model
摘要：
癌症由连续轮的突变产生，这些突变产生具有不同基因组变异的肿瘤细胞，即克隆。准确地鉴定肿瘤样品中的克隆对于药物反应性和治疗是必要的。许多方法被开发来通过计算细胞患病率和肿瘤发生或预测突变的基因型来推断肿瘤异质性。所有的方法都存在一些问题，如克隆频率计算不准确，丢弃克隆特异性基因型等。在本文中，我们提出了一种方法，称为- HetFHMM来推断肿瘤异质性，通过预测克隆特异性基因型和细胞患病率。为了推断克隆特异性基因型，我们考虑在任何基因组位置存在多个突变。我们还在不同的模拟数据上测试了我们的模型。结果表明，HetFHMM优于目前推断肿瘤异质性的方法。因此，HetFHMM是肿瘤异质性研究领域的一种新方法。
英文摘要：
Cancer arises from successive rounds of mutations which generate tumor cells with different genomic variation i.e. clones. For drug responsiveness and therapeutics, it is necessary to identify the clones in tumor sample accurately. Many methods are developed to infer tumor heterogeneity by either computing cellular prevalence and tumor phylogeny or predicting genotype of mutations. All methods suffer some problems e.g. inaccurate computation of clonal frequencies, discarding clone specific genotypes etc. In the paper, we propose a method, called- HetFHMM to infer tumor heterogeneity by predicting clone specific genotypes and cellular prevalence. To infer clone specific genotype, we consider the presence of multiple mutations at any genomic location. We also tested our model on different simulated data. The results shows that HetFHMM outperforms recent methods which infer tumor heterogeneity. Therefore, HetFHMM is a novel approach in tumor heterogeneity research area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
