Overview_of_SCENIC_workflow
Last updated: 2021-08-20
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Knit directory: SCENIC_pipeline/
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一、SCENIC简介
SCENIC，Single-cell rEgulatory Network Inference and Clustering，专为单细胞数据开发的GRNs算法，旨在识别高可靠性的由转录因子主导的的GRNs。
1. 提出的生物学背景
组织内细胞异质性的基础是细胞转录状态的差异，转录状态的特异性又是由转录因子主导的基因调控网络GRNs决定并维持稳定的。
2. 提出的组学背景
单细胞转录组数据具有背景噪音高、基因检出率低和表达矩阵稀疏性的特点，给传统的统计学和生物信息学方法推断高质量的GRNs带来了挑战。
3. SCENIC优势
专为单细胞数据开发的GRNs算法，创新之处在于引入了转录因子motif序列验证统计学方法推断的基因共表达网络。旨在识别高可靠性的由转录因子主导的的GRNs。
二、SCENIC工作流程概览
| 步骤 | 包基础 | 输入数据 | 算法核心 | 输出数据 | 完成任务 | 涉及概念 | |
|---|---|---|---|---|---|---|---|
| 核心步骤——数据分析处理 | Step1 | GENIE3/GRNBoost | 表达矩阵 | 以TF为出发点，基于共表达情况鉴定每个TF的潜在靶点 |
module/network/ | ||
| 核心步骤——数据分析处理 | Step2 | RcisTarget | 基于DNA-motif分析选择潜在的直接结合靶点 |
regulon | |||
| 核心步骤——数据分析处理 | Step3 | AUCell | AUC | 通过打分评估细胞内不同转录因子的调控活性；或者说分析每个细胞的regulons活性 |
|||
| 数据探索和可视化 | Step4 | t-SNE/Hierarchical clustering/…… | 基于regulons的活性鉴定稳定的细胞状态并对结果进行探索 |
三、参考资料
想要深入了解分析原理和流程，可以参考以下两篇文献：
SCENIC : single-cell regulatory network inference and clustering （2007年首先发表于nature methods）
A scalable SCENIC workflow for single-cell gene regulatory network analysis (2020年将重新整理后的流程发表于nature protocles)