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BnpC: Bayesian non-parametric clustering of single-cell mutation profiles.
Bioinformatics ( IF 5.8 ) Pub Date : 2020-06-27 , DOI: 10.1093/bioinformatics/btaa599
Nico Borgsmüller 1, 2 , Jose Bonet 3, 4 , Francesco Marass 1, 2 , Abel Gonzalez-Perez 3, 4 , Nuria Lopez-Bigas 3, 5 , Niko Beerenwinkel 1, 2
Affiliation  

The high resolution of single-cell DNA sequencing (scDNA-seq) offers great potential to resolve intratumor heterogeneity (ITH) by distinguishing clonal populations based on their mutation profiles. However, the increasing size of scDNA-seq datasets and technical limitations, such as high error rates and a large proportion of missing values, complicate this task and limit the applicability of existing methods.

中文翻译:

BnpC:单细胞突变谱的贝叶斯非参数聚类。

单细胞DNA测序(scDNA-seq)的高分辨率提供了巨大的潜力,可通过根据其突变谱区分克隆群体来解决肿瘤内异质性(ITH)。但是,scDNA-seq数据集的大小不断增加以及技术上的局限性(例如较高的错误率和大部分缺失值)使这项任务变得复杂,并限制了现有方法的适用性。
更新日期:2020-06-27
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