凸型ML:可从CRISPR/Cas9谱系追踪数据中进行可扩展和准确的单细胞时间表推断
Sebastian Prillo1, Akshay Ravoor1, Nir Yosef1,2
1Computer Science Division, University of California, Berkeley.
bioRxiv : the preprint server for biology
|December 11, 2023
概括
我们开发了ConvexML,一种新的方法,可以从CRISPR/Cas9数据中创建时间解析的细胞谱系树 (计时图). 这个工具准确地估计了分支长度,揭示了细胞进化动态和亚克隆适应性.
科学领域:
- 计算生物学 计算生物学
- 遗传学 是一个遗传学.
- 生物信息学是一种生物信息学.
背景情况:
- 克里斯普尔/卡斯9基因编辑促进了成千上万个细胞的*in vivo*血统追踪.
- 目前的分析方法主要重建细胞系谱树拓,缺乏时间信息.
- 时间解析树 (计时图) 对于研究细胞群动力学和进化轨迹至关重要.
研究的目的:
- 引入第一个可扩展和准确的方法,用于将单细胞树拓转换为时间解析的计时图.
- 为了能够在CRISPR/Cas9谱系追踪数据中估计分支长度.
- 为进化事件的时间和子克隆适应性提供洞察力.
主要方法:
- 开发了一种新的方法,ConvexML,利用CRISPR/Cas9切割缺少数据的统计模型.
- 采用保守的最大节方法来重建自信的祖先状态.
- 引入了处理缺失数据的新策略,特别是双重切除事件,以简化和加快分支长度估计.
- 以凸的最大概率估计 (MLE) 形式制定了这个问题,可以用标准的优化解决方案来解决.
- 在低信息场景中,纳入处罚的MLE具有最小分支长度和伪计数,用于稳定估计.
主要成果:
- ConvexML通过估计分支长度,准确地将树木拓改进为时间表.
- 该方法有效地处理丢失的数据,包括双重切除事件.
- 凸起的最大概率估计允许快速计算分支长度.
- 通过模拟进行基准测试表明,与天真方法相比,基准测试表现优越.
- ConvexML方法可以作为一个开源的Python包.
结论:
- ConvexML提供了一个可扩展和准确的解决方案,用于从CRISPR/Cas9数据中生成时间解析的细胞系谱树.
- 这一进步使得研究细胞进化动态,亚克隆适应性和表型变化的前所未有的解决方案成为可能.
- 通过Cassiopeia包,ConvexML的开源可用性促进了该领域的更广泛的采用和研究.
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