Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data.
Biophysically Interpretable Inference of Cell Types from Multimodal Sequencing Data.
复制标题
从多模式测序数据中对细胞类型进行生物物理解释的推断。
DOI：
10.1101/2023.09.17.558131
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Pachter,Lior
中科院分区：
文献类型：
--
作者：
Chari,Tara;Gorin,Gennady;Pachter,Lior
多模式、单细胞基因组学技术能够同时测量细胞中 DNA 和 RNA 加工的多个方面。这为异质细胞群中细胞加工的全转录组机制研究创造了机会，例如通过转录随机性调节细胞命运或通过异常剪接动力学调节肿瘤增殖。然而，当前确定多模式数据中的细胞类型或“簇”的方法通常依赖于平衡或整合测量的临时方法，以及忽略数据固有属性的假设。为了实现可解释和一致的细胞簇确定，我们提出了 meK-means（机械 K-means），它通过统一的转录模型整合模式，以学习潜在的、共享的生物物理状态。通过 meK-means，我们可以利用这些模式之间的因果物理关系，对具有新生和成熟 mRNA 测量值的细胞进行聚类。这识别了细胞间共享的转录动态，从而诱导观察到的分子计数，并通过细胞过程的控制参数为“簇”提供了替代定义。
Multimodal, single-cell genomics technologies enable simultaneous measurement of multiple facets of DNA and RNA processing in the cell. This creates opportunities for transcriptome-wide, mechanistic studies of cellular processing in heterogeneous cell populations, such as regulation of cell fate by transcriptional stochasticity or tumor proliferation through aberrant splicing dynamics. However, current methods for determining cell types or ‘clusters’ in multimodal data often rely on ad hoc approaches to balance or integrate measurements, and assumptions ignoring inherent properties of the data. To enable interpretable and consistent cell cluster determination, we present meK-means (mechanistic K-means) which integrates modalities through a unifying model of transcription to learn underlying, shared biophysical states. With meK-means we can cluster cells with nascent and mature mRNA measurements, utilizing the causal, physical relationships between these modalities. This identifies shared transcription dynamics across cells, which induce the observed molecule counts, and provides an alternative definition for ‘clusters’ through the governing parameters of cellular processes.