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A Novel HMM for Analyzing Chromosomal Aberrations in Heterogeneous Tumor Samples

机译:用于分析异质肿瘤样品中的染色体畸变的新型HMM

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Comprehensive detection and identification of copy number and LOH of chromosomal aberration is required to provide an accurate therapy of human cancer. As a cost-saving and high-throughput tool, SNP arrays facilitate analysis of chromosomal aberration throughout the whole genome. The performance of previous approaches has been limited to several critical issues such as normal cell contamination, aneuploidy and tumor heterogeneity. For these reasons we present a Hidden Markov Model (HMM) based approach called TH-HMM (Tumor Heterogeneity HMM), for simultaneous detection of copy number and LOH in heterogeneous tumor samples using data from Illumina SNP arrays. Through adopting an efficient EM algorithm, our method can correctly detect chromosomal aberration events in tumor subclones. Evaluation on simulated data series indicated that TH-HMM could accurately estimate both normal cell and subclone proportions, and finally recovery the aberration profiles for each clones.
机译:需要综合检测和鉴定拷贝数和染色体畸变的LOH,以提供人体癌症的准确治疗。作为一种成本节省和高通量的工具,SNP阵列促进了整个基因组的染色体像差。先前方法的性能仅限于几个关键问题,例如正常细胞污染,非综合性和肿瘤异质性。由于这些原因,我们介绍了一种称为TH-HMM(肿瘤异质性HMM)的隐马尔可夫模型(HMM)方法,用于使用来自Illumina SNP阵列的数据同时检测异质肿瘤样本中的拷贝数和LOH。通过采用高效的EM算法,我们的方法可以正确地检测肿瘤亚克隆的染色体像差事件。模拟数据系列的评估表明,TH-HMM可以准确估计正常的单元格和子筒式比例,最后恢复每个克隆的像差轮廓。

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