DECA:利用可解释的变压器模型来实现染色体可访问性概况的细胞解卷
Shijie Luo1,2, Ming Zhu1, Liquan Lin1
1State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, No. 4221, Xiang'an South Road, Xiamen, Fujian 361102, China.
Briefings in bioinformatics
|February 23, 2025
概括
深度学习模型DECA从批量染色体可访问性数据中解构细胞类型. 该方法通过揭示细胞比例及其可访问性概况,增强对发育和疾病中的基因调节的理解.
科学领域:
- 基因组学就是基因组学.
- 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:
- 用测序 (ATAC-seq) 检测转化酶可访问的染色质对于绘制全基因组染色质可访问性至关重要,这是基因表达的关键调节者.
- 大量ATAC-seq掩盖了细胞异质性,而单细胞ATAC-seq带来了数据稀疏性和高成本等挑战.
- 从大量数据中解决细胞类型特定的染色质可访问性对于理解复杂的生物系统至关重要.
研究的目的:
- 引入DECA,一个使用视觉转换器的深度学习模型,用于从批量ATAC-seq数据中解构细胞类型信息.
- 利用单细胞ATAC-seq数据集作为提高细胞类型解卷的精度和分辨率的参考.
- 通过预测细胞比例及其染色体可访问性概况,使得在发育和疾病中基因调节程序的探索成为可能.
主要方法:
- 开发了基于视觉转换器的深度学习模型DECA,用于分析批量ATAC-seq配置文件.
- 在DECA中使用多头注意力机制来产生补丁注意力,与Hi-C.的染色体相互作用数据保持一致.
- 使用单细胞ATAC-seq数据集作为训练和验证解卷模型的参考.
主要成果:
- DECA成功地将细胞类型信息从批量染色质可访问性数据中以更高的精度分解.
- 该模型的补丁注意力机制与实验确定的染色体相互作用 (Hi-C) 显示了对齐.
- 在基因干扰后,DECA准确地预测了基因干扰后的特定细胞组成变化,并确定了染色体可访问性签名中的特定细胞类型的遗传变异.
- 将DECA应用于癌ATAC-seq数据集,揭示了具有显著临床意义的细胞类型比例.
结论:
- DECA有效地分解细胞比例,并从批量ATAC-seq数据中预测它们的染色质可访问性概况.
- 该模型提供了一个强大的工具,用于研究基因调节程序在各种生物背景下,包括发育和疾病.
- DECA集成批量和单细胞数据的能力为研究表观遗传学中的细胞异质性提供了一种具有成本效益和高分辨率的方法.
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