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scGrapHiC:使用单细胞基因表达的Hi-C的基于深度学习的图形解卷

Ghulam Murtaza¹, Byron Butaney¹, Justin Wagner²

¹Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI, 02912, United States.

Bioinformatics (Oxford, England)

|June 28, 2024

概括

我们开发了scGrapHiC,这是一个深度学习工具,从scRNA-seq数据中预测单细胞Hi-C接触地图. 这种方法增强了对细胞类型特定的染色质相互作用的研究,克服了当前实验协议的局限性.

科学领域:

基因组学和生物信息学
计算生物学计算生物学
表观遗传学在表观遗传学中,表观遗传学是指表观遗传学.

背景情况:

单细胞Hi-C (scHi-C) 揭示了细胞类型特定的染色质相互作用,这对于理解细胞分化和疾病至关重要.
高成本和实验复杂性限制了scHi-C数据的广泛使用.
现有的方法往往不充分利用scHi-C数据集中的丰富信息.

研究的目的:

将 scGrapHiC 引入,这是一个深度学习框架,用于从伪批量 scRNA-seq 数据中预测伪批量 scHi-C 联系地图.
为了使细胞类型特定的scHi-C接触地图能够使用更容易获得的基因组信号生成.
促进对全基因组单细胞相互作用和染色体组织的研究.

主要方法:

scGrapHiC使用图形解卷方法推断单细胞相互作用.
它利用scRNA-seq数据作为指导信号,从批量Hi-C接触地图中提取信息.
该框架在七个细胞类型共同测试数据集上进行了培训和评估.

主要成果:

scGrapHiC在预测scHi-C接触地图方面优于传统的序列编码方法.
与基线方法相比,在恢复特定于细胞类型的拓学关联域方面取得了23.2%的改善.
在未见的胚胎和脑组织样本上展示了概括能力.

结论:

scGrapHiC提供了一种新且有效的方法,用于生成特定细胞类型的scHi-C接触地图.
该框架通过利用广泛可用的scRNA-seq数据,使染色质相互作用的研究民主化.
能够更深入地了解细胞类型特定的基因组组织及其在生物过程中的作用.

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