Tree inference for single-cell data.
Tree inference for single-cell data.
复制标题
单细胞数据的树推断。
DOI：
10.1186/s13059-016-0936-x
复制
发表时间：
2016-05-05
期刊：
影响因子：
12.3
通讯作者：
Beerenwinkel N
中科院分区：
文献类型：
--
作者：
Jahn K;Kuipers J;Beerenwinkel N
了解肿瘤内突变的异质性是开发有效的癌症治疗方法的关键。在这里，我们提出了一种随机搜索算法SCITE，它可以从噪声和不完全的单细胞突变图谱中识别肿瘤的进化历史。SCITE包括灵活的马尔可夫链蒙特卡罗抽样方案，允许用户计算最大似然突变历史，从后验概率分布进行抽样，并估计潜在测序实验的错误率。对真实癌症数据和模拟研究的评估表明，与现有方法相比，SCITE对目前的单细胞测序数据具有可扩展性，并提高了重建精度。本文的在线版本(doi：10.1186/s13059-0160936-x)包含补充材料，授权用户可以使用。
Understanding the mutational heterogeneity within tumors is a keystone for the development of efficient cancer therapies. Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumor from noisy and incomplete mutation profiles of single cells. SCITE comprises a flexible Markov chain Monte Carlo sampling scheme that allows the user to compute the maximum-likelihood mutation history, to sample from the posterior probability distribution, and to estimate the error rates of the underlying sequencing experiments. Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches. The online version of this article (doi:10.1186/s13059-016-0936-x) contains supplementary material, which is available to authorized users.