单细胞转录因子分析之SCENIC流程 |
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去年我们在《生信技能树》公众号带领大家一起学习过:[SCENIC转录因子分析结果的解读](https://mp.weixin.qq.com/s/eAfkhX0SJu1lytZeXdsh0Q) ,提到了在做单细胞转录因子分析,首选的工具就是SCENIC流程,其工作流程两次发表在nature系列杂志足以说明它的优秀 : SCENIC : single-cell regulatory network inference and clustering(2017年的nature methods) https://www.nature.com/articles/nmeth.4463 A scalable SCENIC workflow for single-cell gene regulatory network analysis(2020年的nature protocls) https://www.nature.com/articles/s41596-020-0336-2SCENIC (Single-Cell rEgulatory Network Inference and Clustering) is a computational method to infer Gene Regulatory Networks and cell types from single-cell RNA-seq data. 官网教程非常清晰: Introduction and setup 安装 running SCENIC 使用提供了 (R / Python)两个版本的运行方式 ,SCENIC is implemented in R (this package and tutorial) and Python (pySCENIC). 安装SCENIC流程其中R语言版本的SCENIC流程依赖于3个R包: GENIE3 to infer the co-expression network (faster alternative: GRNBoost2) RcisTarget for the analysis of transcription factor binding motifs AUCell to identify cells with active gene sets (gene-network) in scRNA-seq data所以,实际上学习这个SCENIC流程,必须要先学习这3个R包的。 比如RcisTarget 包,基于DNA-motif 分析选择潜在的直接结合靶点,我也是写了两个教程: 基因集的转录因子富集分析 为什么是AUC值而不是GSEA来挑选转录因子呢再比如那个AUCell 包,我分享了:使用AUCell包的AUCell_calcAUC函数计算每个细胞的每个基因集的活性程度 安装它们的话基本上复制粘贴下面的代码即可: if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::version() # If your bioconductor version is previous to 4.0, see the section bellow ## Required BiocManager::install(c("AUCell", "RcisTarget"),ask = F,update = F) BiocManager::install(c("GENIE3"),ask = F,update = F) # Optional. Can be replaced by GRNBoost ## Optional (but highly recommended): # To score the network on cells (i.e. run AUCell): BiocManager::install(c("zoo", "mixtools", "rbokeh"),ask = F,update = F) # For various visualizations and perform t-SNEs: BiocManager::install(c("DT", "NMF", "ComplexHeatmap", "R2HTML", "Rtsne"),ask = F,update = F) # To support paralell execution (not available in Windows): BiocManager::install(c("doMC", "doRNG"),ask = F,update = F) # To export/visualize in http://scope.aertslab.org if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") devtools::install_github("aertslab/SCopeLoomR", build_vignettes = TRUE) if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") devtools::install_github("aertslab/SCENIC") packageVersion("SCENIC")Python我不怎么使用,所以 Python (pySCENIC). 先略过。虽然这次略过了,但其实是躲不过去的,因为R里面的计算速度真心很慢,后期我们会补上这个 Python (pySCENIC). 教程哈。 另外,运行单细胞转录因子分析之SCENIC流程还需要下载配套数据库,不同物种不一样, 在 https://resources.aertslab.org/cistarget/ 查看自己的物种,按需下载: # https://resources.aertslab.org/cistarget/ dbFiles |
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