深度GRNCS:基于深度学习的框架,用于在细胞亚群中共同推断基因调节网络
Yahui Lei1, Xiao-Tai Huang1, Xingli Guo1
1School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
Briefings in bioinformatics
|July 9, 2024
概括
深度GRNCS通过将深度学习与单细胞RNA测序数据集成,在细胞亚群中推断基因调节网络 (GRNs). 这种方法通过在特定细胞类型中识别关键基因来增强对细胞功能和疾病的理解.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
背景情况:
- 基因调节网络 (GRNs) 对于理解细胞功能和疾病至关重要.
- 单细胞RNA测序 (scRNA-seq) 提高了GRN推断的准确性.
- 现有的方法往往忽略了scRNA-seq数据中的细胞间异质性和亚种群相似性.
研究的目的:
- 开发一个深度学习框架,DeepGRNCS,以共同推断跨不同细胞亚群的GRNs.
- 解决目前的GRN推断方法的局限性,这些方法忽视了细胞异质性.
- 为了提高GRN从scRNA-seq数据推断的准确性和范围.
主要方法:
- 提出了DeepGRNCS,这是一个深度学习框架,用于跨细胞亚群的联合GRN推断.
- 使用等宽离散方法处理scRNA-seq数据.
- 训练深度学习模型,从转录因子 (TF) 和推断的调节关系中预测目标基因表达.
主要成果:
- 与现有方法相比,DeepGRNCS在模拟和真实scRNA-seq数据集上表现出更高的性能.
- 该框架有效地预测细胞亚种群特定的GRNs.
- 应用于非小细胞肺癌数据,识别关键基因并分析它们的生物相关性.
结论:
- 深度GRNCS为推断特定于细胞亚群的基因调控网络提供了一种有效的方法.
- 该方法增强了对复杂生物系统中细胞机制的理解.
- 源代码是公开可用的,用于更广泛的研究应用.
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