时间尺度的细胞系谱树的最大概率推理与混合类型的缺失数据
Uyen Mai1, Gillian Chu1, Benjamin J Raphael1
1Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.
bioRxiv : the preprint server for biology
|March 18, 2024
概括
这项研究引入了概率混合型缺失 (PMM) 模型和动态血统追踪的LAML算法,改进了从CRISPR突变推断细胞血统树的推断,并揭示了不同的转移时代.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 发展生物学 发展生物学
背景情况:
- 动态血统追踪使用CRISPR和单细胞测序来研究细胞分裂.
- 从CRISPR突变推断细胞谱系树提出了独特的计算挑战.
- 现有的遗传学模型不能充分解决CRISPR数据的不可修改突变和高缺失数据率.
研究的目的:
- 开发一种新的计算模型和算法,从动态谱系追踪数据中准确地推断细胞谱系树.
- 解决CRISPR诱导突变的特定特性,包括不可修改性和随着时间的推移而减少的突变率.
- 在复杂的生物系统 (如癌症转移) 的背景下,提高家族遗传树重建的准确性.
主要方法:
- 介绍了概率混合型失踪 (PMM) 模型,以捕捉CRISPR谱系追踪数据的独特特征.
- 开发LAML (通过最大概率进行血统分析) 算法,将预期最大化 (EM) 与启发式树搜索结合起来.
- 使用PMM和LAML,共同估计树木拓,树枝长度和缺失的数据参数.
主要成果:
- 在模拟数据上,LAML比现有的方法得出了更准确的树木拓和时间尺度的枝长.
- PMM模型和LAML算法优于标准的家族遗传模型,特别是在具有高遗传性缺失数据的情况下.
- 对肺腺癌数据的分析显示,LAML推断的基因距离与基因表达一致,这表明更合理的瘤进展动态.
结论:
- LAML提供了一个强大的框架,可以从动态谱系追踪数据中推断细胞谱系树.
- 该模型准确地捕捉了CRISPR诱导的突变和缺失数据的复杂性,从而改善了生物洞察力.
- 对肺癌数据的应用确定了不同的转移时代,为瘤进展和转移提供了新的视角.
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