scRegulate:从基因表达的转录因子活性单细胞调节嵌入的变异推理
Mehrdad Zandigohar1, Jalees Rehman1,2, Yang Dai1
1Department of Biomedical Engineering, University of Illinois Chicago, Chicago, Illinois, United States.
scRegulate从单细胞RNA测序数据中推断转录因子活性,使用一种新的深度学习框架. 这种方法准确地预测基因调节网络,并确定关键的转录因子,推进计算生物学.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 从单细胞RNA测序 (scRNA-seq) 准确推断转录因子 (TF) 活性至关重要,但具有挑战性.
- 现有的方法通常依赖于静态数据库和决定性假设,限制了它们捕捉动态监管相互作用的能力.
研究的目的:
- 开发一个新的深度学习框架,scRegulate,从scRNA-seq数据中推断TF活动和基因调控网络 (GRNs).
- 将先前的生物学知识与数据驱动的推断相结合,以提高准确性和可解释性.
主要方法:
- scRegulate采用一种具有变化推理的生成深度学习框架.
- 它将基因调节网络 (GRN) 的先验和结构化的生物约束纳入了一个概率潜伏空间模型中.
- 该框架基于合成和实验数据集进行了基准测试,包括Perturb-seq和PBMC scRNA-seq数据.
主要成果:
- 与合成数据集的现有方法相比,scRegulate在推断TF活动和GRNs方面表现优越 (AUROC 0.71-0.86,AUPRC 0.80-0.95).
- 它准确地回顾了实验数据中TF的淘汰效应,并确定了ELK1,EGR1和CREB1等关键TF.
- 对PBMC数据的应用揭示了细胞类型特定的GRNs,并通过TF嵌入实现了通过TF嵌入准确的细胞类型聚类.
结论:
- scRegulate为TF活动和从scRNA-seq数据中推断GRN提供了一个可扩展,可解释和强大的框架.
- 该方法有效地将先前的生物学知识与数据驱动的方法相结合.
- scRegulate促进了转录调节和细胞异质性的分析.
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相关概念视频
Regulation of Expression at Multiple Steps
General Transcription Factors
Combinatorial Gene Control
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Constitutive and Regulated Gene Expression
Regulation of Expression Occurs at Multiple Steps
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Transcription Factors
