在/08/07的Nature刊中，中科院景乃禾课题组发表了文章——Molecular architecture of lineage allocation and tissue organization in early mouse embryo，我在这篇文章中发现了一个被汤神组 （就是中开辟了单细胞转录组领域的人）反复用到的R工具-SCENIC，现在让我们来一起看看该工具有何妙用！
SCENIC简介
SCENIC是一种同时重建基因调控网络并从单细胞RNA-seq数据中鉴定stable cell states的工具。基于共表达和DNA模基序 （motif）分析推断基因调控网络 ，然后在每个细胞中分析网络活性以鉴定细胞状态。
SCENIC发表于的Nature method文章。具体见链接:
/articles/nmeth.4463
要求：当前版本的SCENIC支持人类，鼠和果蝇（Drosophila melanogaster）。
要将SCENIC应用于其他物种，需要手动调整第二步（例如使用新的RcisTarget数据库或使用不同的motif-enrichment-analysis工具）。
输入：SCENIC需要输入的是单细胞RNA-seq表达矩阵—— 每列对应于样品（细胞），每行对应一个基因。基因ID应该是gene-symbol并存储为rownames （尤其是基因名字部分是为了与RcisTarget数据库兼容）；表达数据是Gene的reads count。根据作者的测试，提供原始的或Normalized UMI count，无论是否log转换，或使用TPM值，结果相差不大。(Overall, SCENIC is quite robust to this choice, we have applied SCENIC to >SCENIC
先安装R，如果处理大样本数据，则建议使用Python版 (https://pyscenic.readthedocs.io/en/latest/);
SCENIC在R中实现基于三个R包：
GENIE3：推断基因共表达网络
RcisTarget：用于分析转录因子结合motif
AUCell：用于鉴定scRNA-seq数据中具有活性基因集（基因网络）的细胞
运行SCENIC需要安装这些软件包以及一些额外的依赖包：
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")BiocManager::version()# If your bioconductor version is previous to 3.9, see the section bellow## RequiredBiocManager::install(c("AUCell", "RcisTarget"))BiocManager::install(c("GENIE3")) # 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"))# For various visualizations and perform t-SNEs:BiocManager::install(c("DT", "NMF", "pheatmap", "R2HTML", "Rtsne"))# To support paralell execution (not available in Windows):BiocManager::install(c("doMC", "doRNG"))# To export/visualize in if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")devtools::install_github("aertslab/SCopeLoomR", build_vignettes = TRUE)
Version:AUCell >=1.4.1(minimum 1.2.4),RcisTarget>=1.2.0(minimum 1.0.2) andGENIE3>=1.4.0(minimum 1.2.1).
安装SCENIC:
if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")devtools::install_github("aertslab/SCENIC")packageVersion("SCENIC")
下载评分数据库
除了必要的R包之外，需要下载RcisTarget的物种特定数据库（/cistarget/；主题排名）。默认情况下，SCENIC使用在基因启动子（TSS上游500 bp）和TSS周围20 kb (+/- 10kb)中对模序进行评分的数据库。
Forhuman:
dbFiles <- c("/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-500bp-upstream-7species.mc9nr.feather","/cistarget/databases/homo_sapiens/hg19/refseq_r45/mc9nr/gene_based/hg19-tss-centered-10kb-7species.mc9nr.feather")# mc9nr: Motif collection version 9: 24k motifs
Formouse:
dbFiles <- c("/cistarget/databases/mus_musculus/mm9/refseq_r45/mc9nr/gene_based/mm9-500bp-upstream-7species.mc9nr.feather","/cistarget/databases/mus_musculus/mm9/refseq_r45/mc9nr/gene_based/mm9-tss-centered-10kb-7species.mc9nr.feather")# mc9nr: Motif collection version 9: 24k motifs
Forfly:
dbFiles <- c("/cistarget/databases/drosophila_melanogaster/dm6/flybase_r6.02/mc8nr/gene_based/dm6-5kb-upstream-full-tx-11species.mc8nr.feather")# mc8nr: Motif collection version 8: 20k motifs
注意：下载后最好确认下载的数据是否完整，可基于MD5值评估。（参考链接：/cistarget/databases/sha256sum.txt）
完成这些设置步骤后，SCENIC即可运行。
数据格式不同时如何读入？
最终读入的信息有两个，一个是前面说的表达矩阵，还有一个是样品分组信息。
a) From.loomfile
.loom文件可以通过SCopeLoomR包直接导入SCENIC。(loom格式是用于存储非常大的组学数据集的专属格式，具体见 /loompy/)
## Download:download.file("/clone/Previously%20Published/Cortex.loom", "Cortex.loom")loomPath <- "Cortex.loom"
b) From 10X/CellRanger output files
10X/CellRanger输出结果可用作SCENIC的输入文件 (需要先安装Seurat)。
# BiocManager::install("Seurat")# 测试数据也可以从Seurat官网下载，自己修改路径# 测试数据：/single-cell-gene-expression/software/pipelines/latest/output/matrices#r-load-mat>singleCellMatrix <- Seurat::Read10X(data.dir="data/pbmc3k/filtered_gene_bc_matrices/hg19/")cellInfo <- data.frame(seuratCluster=Idents(seuratObject))
c) From other R objects (e.g. Seurat, SingleCellExperiment)
sce <- load_as_sce(dataPath) # any SingleCellExperiment objectexprMat <- counts(sce)cellInfo <- colData(sce)
d) From GEO
从GEO下载和格式化数据集的示例（例如，GEOGSE60361：3005小鼠脑细胞数据集）
# 获取数据及数据标注# dir.create("SCENIC_MouseBrain"); setwd("SCENIC_MouseBrain") # if needed# (This may take a few minutes)if (!requireNamespace("GEOquery", quietly = TRUE)) BiocManager::install("GEOquery")library(GEOquery)geoFile <- getGEOSuppFiles("GSE60361", makeDirectory=FALSE)gzFile <- grep("Expression", basename(rownames(geoFile)), value=TRUE)txtFile <- gsub(".gz", "", gzFile)gunzip(gzFile, destname=txtFile, remove=TRUE)library(data.table)geoData <- fread(txtFile, sep="\t")geneNames <- unname(unlist(geoData[,1, with=FALSE]))exprMatrix <- as.matrix(geoData[,-1, with=FALSE])rm(geoData)dim(exprMatrix)rownames(exprMatrix) <- geneNamesexprMatrix <- exprMatrix[unique(rownames(exprMatrix)),]exprMatrix[1:5,1:4]# Remove file downloaded:file.remove(txtFile)
运行 SCENIC
SCENIC workflow:
建立基因调控网络（Gene Regulation Network，GRN）：
基于共表达识别每个转录因子TF的潜在靶标。过滤表达矩阵并运行GENIE3或者GRNBoost，它们是利用表达矩阵推断基因调控网络的一种算法，能得到转录因子和潜在靶标的相关性网络；将目标从GENIE3或者GRNBoost格式转为共表达模块。
根据DNA模序分析（motif）选择潜在的直接结合靶标（调节因子）（利用RcisTarget包：TF基序分析）
确定细胞状态及其调节因子：
3.分析每个细胞中的网络活性（AUCell）
在细胞中评分调节子（计算AUC）
SCENIC完整流程
### 导入数据loomPath<-system.file(package="SCENIC","examples/mouseBrain_toy.loom")library(SCopeLoomR)loom <- open_loom(loomPath, mode="r")exprMat <- get_dgem(loom)cellInfo <- get_cellAnnotation(loom)close_loom(loom)### Initialize settings 初始设置，导入评分数据库library(SCENIC)scenicOptions <- initializeScenic(org="mgi", dbDir="cisTarget_databases", nCores=10)# scenicOptions@inputDatasetInfo$cellInfo <- "int/cellInfo.Rds"saveRDS(scenicOptions, file="int/scenicOptions.Rds")### 共表达网络genesKept <- geneFiltering(exprMat, scenicOptions)exprMat_filtered <- exprMat[genesKept, ]runCorrelation(exprMat_filtered, scenicOptions)exprMat_filtered_log <- log2(exprMat_filtered+1)runGenie3(exprMat_filtered_log, scenicOptions)### Build and score the GRN 构建并给基因调控网络（GRN）打分exprMat_log <- log2(exprMat+1)scenicOptions@settings$dbs <- scenicOptions@settings$dbs["10kb"] # Toy run settingsrunSCENIC_1_coexNetwork2modules(scenicOptions)runSCENIC_2_createRegulons(scenicOptions, coexMethod=c("top5perTarget")) # Toy run settingsrunSCENIC_3_scoreCells(scenicOptions, exprMat_log)# Export:export2scope(scenicOptions, exprMat)# Binarize activity?# aucellApp <- plotTsne_AUCellApp(scenicOptions, exprMat_log)# savedSelections <- shiny::runApp(aucellApp)# newThresholds <- savedSelections$thresholds# scenicOptions@fileNames$int["aucell_thresholds",1] <- "int/newThresholds.Rds"# saveRDS(newThresholds, file=getIntName(scenicOptions, "aucell_thresholds"))# saveRDS(scenicOptions, file="int/scenicOptions.Rds")runSCENIC_4_aucell_binarize(scenicOptions)### Exploring output# Check files in folder "output"# .loom file @ # output/Step2_MotifEnrichment_preview.html in detail/subset:motifEnrichment_selfMotifs_wGenes <- loadInt(scenicOptions, "motifEnrichment_selfMotifs_wGenes")tableSubset <- motifEnrichment_selfMotifs_wGenes[highlightedTFs=="Sox8"]viewMotifs(tableSubset)# output/Step2_regulonTargetsInfo.tsv in detail:regulonTargetsInfo <- loadInt(scenicOptions, "regulonTargetsInfo")tableSubset <- regulonTargetsInfo[TF=="Stat6" & highConfAnnot==TRUE]viewMotifs(tableSubset)
举个栗子！输入表达矩阵
在本教程中，我们提供了一个示例，样本是小鼠大脑的200个细胞和862个基因：
loomPath <- system.file(package="SCENIC", "examples/mouseBrain_toy.loom")
打开loom文件并加载表达矩阵；
library(SCopeLoomR)loom <- open_loom(loomPath, mode="r")exprMat <- get_dgem(loom)cellInfo <- get_cellAnnotation(loom)close_loom(loom)dim(exprMat)
细胞信息/表型
# cellInfo$nGene <- colSums(exprMat>0)head(cellInfo)
cellInfo <- >cellTypeColumn <- "Class"colnames(cellInfo)[which(colnames(cellInfo)==cellTypeColumn)] <- "CellType"cbind(table(cellInfo$CellType))
# Color to assign to the variables (same format as for NMF::aheatmap)colVars <- list(CellType=c("microglia"="forestgreen", "endothelial-mural"="darkorange", "astrocytes_ependymal"="magenta4", "oligodendrocytes"="hotpink", "interneurons"="red3", "pyramidal CA1"="skyblue", "pyramidal SS"="darkblue"))colVars$CellType <- colVars$CellType[intersect(names(colVars$CellType), cellInfo$CellType)]saveRDS(colVars, file="int/colVars.Rds")plot.new(); legend(0,1, fill=colVars$CellType, legend=names(colVars$CellType))
初始化SCENIC设置
为了在SCENIC的多个步骤中保持设置一致，SCENIC包中的大多数函数使用一个公共对象，该对象存储当前运行的选项并代替大多数函数的“参数”。比如下面的org，dbDir等，可以在开始就将物种rog（mgi—— mouse，hgnc—— human，dmel—— fly）和RcisTarge数据库位置分别读给对象org，dbDir，之后统一用函数initializeScenic得到对象scenicOptions。具体参数设置可以用?initializeScenichelp一下。
library(SCENIC)org="mgi" # or hgnc, or dmeldbDir="cisTarget_>myDatasetTitle="SCENIC example on Mouse brain" # choose a name for your analysisdata(defaultDbNames)dbs <- defaultDbNames[[org]]scenicOptions <- initializeScenic(org=org, dbDir=dbDir, dbs=dbs, datasetTitle=myDatasetTitle, nCores=10)
# Modify if neededscenicOptions@inputDatasetInfo$cellInfo <- "int/cellInfo.Rds"scenicOptions@inputDatasetInfo$colVars <- "int/colVars.Rds"# Databases:# scenicOptions@settings$dbs <- c("mm9-5kb-mc8nr"="mm9-tss-centered-5kb-10species.mc8nr.feather")# scenicOptions@settings$db_mcVersion <- "v8"# Save to use at a later time...saveRDS(scenicOptions, file="int/scenicOptions.Rds")
共表达网络
SCENIC工作流程的第一步是根据表达数据推断潜在的转录因子靶标。为此，我们使用GENIE3或GRNBoost，输入文件是表达矩阵（过滤后的）和转录因子列表。GENIE3/GRBBoost的输出结果和相关矩阵将用于创建共表达模块（runSCENIC_1_coexNetwork2modules）。
基因过滤/选择
按每个基因的reads总数进行过滤。
该filter旨在去除最可能是噪音的基因。
默认情况下，它（minCountsPerGene）保留所有样品中至少带有6个UMI reads的基因（例如，如果在1％的细胞中以3的值表达，则基因将具有的总数）。
通过基因的细胞数来实现过滤（例如UMI > 0，或log 2（TPM）> 1 ）。
默认情况下(minSamples)，保留下来的基因能在至少1％的细胞中检测得到。
最后，只保留RcisTarget数据库中可用的基因。
# (Adjust minimum values according to your >genesKept <- geneFiltering(exprMat, scenicOptions=scenicOptions, minCountsPerGene=3*.01*ncol(exprMat), minSamples=ncol(exprMat)*.01)
在进行网络推断之前，检查是否有任何已知的相关基因被过滤掉（如果缺少任何相关基因，请仔细检查filter设置是否合适）：
interestingGenes <- c("Sox9", "Sox10", "Dlx5")# any missing?interestingGenes[which(!interestingGenes %in% genesKept)]
相关性
GENIE33或者GRNBoost可以检测正负关联。为了区分潜在的激活和抑制，我们将目标分为正相关和负相关目标（比如TF与潜在目标之间的Spearman相关性）。
runCorrelation(exprMat_filtered, scenicOptions)
运行GENIE3得到潜在转录因子TF
## If launched in a new session, you will need to reload...# setwd("...")# loomPath <- "..."# loom <- open_loom(loomPath, mode="r")# exprMat <- get_dgem(loom)# close_loom(loom)# genesKept <- loadInt(scenicOptions, "genesKept")# exprMat_filtered <- exprMat[genesKept,]# library(SCENIC)# scenicOptions <- readRDS("int/scenicOptions.Rds")# Optional: add log (if it is not logged/normalized already)exprMat_filtered <- log2(exprMat_filtered+1)# Run GENIE3runGenie3(exprMat_filtered, scenicOptions)
构建并评分GRN（runSCENIC_ …）
必要时重新加载表达式矩阵：
loom <- open_loom(loomPath, mode="r")exprMat <- get_dgem(loom)close_loom(loom)# Optional: log expression (for TF expression plot, it does not affect any other calculation)logMat <- log2(exprMat+1)dim(exprMat)
使用wrapper函数运行其余步骤：
library(SCENIC)scenicOptions <- readRDS("int/scenicOptions.Rds")scenicOptions@settings$verbose <- TRUEscenicOptions@settings$nCores <- 10scenicOptions@settings$seed <- 123# For a very quick run:# coexMethod=c("top5perTarget")scenicOptions@settings$dbs <- scenicOptions@settings$dbs["10kb"] # For toy run# save...runSCENIC_1_coexNetwork2modules(scenicOptions)runSCENIC_2_createRegulons(scenicOptions, coexMethod=c("top5perTarget")) #** Only for toy run!!runSCENIC_3_scoreCells(scenicOptions, logMat)
可选步骤
将network activity转换成ON/OFF（二进制）格式
nPcs <- c(5) # For toy ># nPcs <- c(5,15,50)
scenicOptions@settings$seed <- 123 # same seed for all of them# Run t-SNE with different settings:fileNames <- tsneAUC(scenicOptions, aucType="AUC", nPcs=nPcs, perpl=c(5,15,50))fileNames <- tsneAUC(scenicOptions, aucType="AUC", nPcs=nPcs, perpl=c(5,15,50), onlyHighConf=TRUE, filePrefix="int/tSNE_oHC")# Plot as pdf (individual files in int/):fileNames <- paste0("int/",grep(".Rds", grep("tSNE_", list.files("int"), value=T), value=T))
par(mfrow=c(length(nPcs), 3))fileNames <- paste0("int/",grep(".Rds", grep("tSNE_AUC", list.files("int"), value=T, perl = T), value=T))plotTsne_compareSettings(fileNames, scenicOptions, showLegend=FALSE, varName="CellType", cex=.5)
# Using only "high-confidence" regulons (normally similar)par(mfrow=c(3,3))fileNames <- paste0("int/",grep(".Rds", grep("tSNE_oHC_AUC", list.files("int"), value=T, perl = T), value=T))plotTsne_compareSettings(fileNames, scenicOptions, showLegend=FALSE, varName="CellType", cex=.5)
输出到 loom/SCope
SCENIC生成的结果既能在查看，还能用函数export2scope()（需要SCopeLoomR包）保存成.loom文件。
# DGEM (Digital gene expression matrix)# (non-normalized counts)# exprMat <- get_dgem(open_loom(loomPath))# dgem <- exprMat# head(colnames(dgem)) #should contain the Cell ID/name# Export:scenicOptions@fileNames$output["loomFile",] <- "output/mouseBrain_SCENIC.loom"export2scope(scenicOptions, exprMat)
加载.loom文件中的结果
SCopeLoomR中也有函数可以导入.loom文件中的内容，比如调节因子，AUC和封装内容（比如regulon activity的t-SNE和UMAP结果）。
library(SCopeLoomR)scenicLoomPath <- getOutName(scenicOptions, "loomFile")loom <- open_loom(scenicLoomPath)# Read information from loom file:regulons_incidMat <- get_regulons(loom)regulons <- regulonsToGeneLists(regulons_incidMat)regulonsAUC <- get_regulonsAuc(loom)regulonsAucThresholds <- get_regulonThresholds(loom)embeddings <- get_embeddings(loom)
解读结果1. 细胞状态
AUCell提供跨细胞的调节子的活性,AUCell使用“Area under Curve 曲线下面积”（AUC）来计算输入基因集的关键子集是否在每个细胞的表达基因中富集。通过该调节子活性（连续或二进制AUC矩阵）来聚类细胞，我们可以看出是否存在倾向于具有相同调节子活性的细胞群，并揭示在多个细胞中反复发生的网络状态。这些状态等同于网络的吸引子状态。将这些聚类与不同的可视化方法相结合，我们可以探索细胞状态与特定调节子的关联。
将AUC和TF表达投射到t-SNE上
logMat <- exprMat # Better if it is logged/normalizedaucellApp <- plotTsne_AUCellApp(scenicOptions, logMat) # default t-SNEsavedSelections <- shiny::runApp(aucellApp)
print(tsneFileName(scenicOptions))
tSNE_scenic <- readRDS(tsneFileName(scenicOptions))aucell_regulonAUC <- loadInt(scenicOptions, "aucell_regulonAUC")# Show TF expression:par(mfrow=c(2,3))AUCell::AUCell_plotTSNE(tSNE_scenic$Y, exprMat, aucell_regulonAUC[onlyNonDuplicatedExtended(rownames(aucell_regulonAUC))[c("Dlx5", "Sox10", "Sox9","Irf1", "Stat6")],], plots="Expression")
# Save AUC as PDF:Cairo::CairoPDF("output/Step4_BinaryRegulonActivity_tSNE_colByAUC.pdf", width=20, height=15)par(mfrow=c(4,6))AUCell::AUCell_plotTSNE(tSNE_scenic$Y, cellsAUC=aucell_regulonAUC, plots="AUC")dev.off()
library(KernSmooth)
library(RColorBrewer)dens2d <- bkde2D(tSNE_scenic$Y, 1)$fhatimage(dens2d, col=brewer.pal(9, "YlOrBr"), axes=FALSE)contour(dens2d, add=TRUE, nlevels=5, drawlabels=FALSE)
#par(bg = "black")par(mfrow=c(1,2))regulonNames <- c( "Dlx5","Sox10")cellCol <- plotTsne_rgb(scenicOptions, regulonNames, aucType="AUC", aucMaxContrast=0.6)text(0, 10, attr(cellCol,"red"), col="red", cex=.7, pos=4)text(-20,-10, attr(cellCol,"green"), col="green3", cex=.7, pos=4)regulonNames <- list(red=c("Sox10", "Sox8"), green=c("Irf1"), blue=c( "Tef"))cellCol <- plotTsne_rgb(scenicOptions, regulonNames, aucType="Binary")text(5, 15, attr(cellCol,"red"), col="red", cex=.7, pos=4)text(5, 15-4, attr(cellCol,"green"), col="green3", cex=.7, pos=4)text(5, 15-8, attr(cellCol,"blue"), col="blue", cex=.7, pos=4)
GRN：Regulon靶标和模序
regulons <- loadInt(scenicOptions, "regulons")regulons[c("Dlx5", "Irf1")]
regulons <- loadInt(scenicOptions, "aucell_regulons")head(cbind(onlyNonDuplicatedExtended(names(regulons))))
regulonTargetsInfo <- loadInt(scenicOptions, "regulonTargetsInfo")tableSubset <- regulonTargetsInfo[TF=="Stat6" & highConfAnnot==TRUE]viewMotifs(tableSubset)
2. 细胞群的调控因子
regulonAUC <- loadInt(scenicOptions, "aucell_regulonAUC")regulonAUC <- regulonAUC[onlyNonDuplicatedExtended(rownames(regulonAUC)),]regulonActivity_byCellType <- sapply(split(rownames(cellInfo), cellInfo$CellType), function(cells) rowMeans(getAUC(regulonAUC)[,cells]))regulonActivity_byCellType_Scaled <- t(scale(t(regulonActivity_byCellType), center = T, scale=T))pheatmap::pheatmap(regulonActivity_byCellType_Scaled, #fontsize_row=3, color=colorRampPalette(c("blue","white","red"))(100), breaks=seq(-3, 3, length.out = 100), treeheight_row=10, treeheight_col=10, border_color=NA)
# filename="regulonActivity_byCellType.pdf", width=10, height=20)topRegulators <- reshape2::melt(regulonActivity_byCellType_Scaled)colnames(topRegulators) <- c("Regulon", "CellType", "RelativeActivity")topRegulators <- topRegulators[which(topRegulators$RelativeActivity>0),]viewTable(topRegulators)
minPerc <- .7binaryRegulonActivity <- loadInt(scenicOptions, "aucell_binary_nonDupl")cellInfo_binarizedCells <- cellInfo[which(rownames(cellInfo)%in% colnames(binaryRegulonActivity)),, drop=FALSE]regulonActivity_byCellType_Binarized <- sapply(split(rownames(cellInfo_binarizedCells), cellInfo_binarizedCells$CellType), function(cells) rowMeans(binaryRegulonActivity[,cells, drop=FALSE]))binaryActPerc_subset <- regulonActivity_byCellType_Binarized[which(rowSums(regulonActivity_byCellType_Binarized>minPerc)>0),]pheatmap::pheatmap(binaryActPerc_subset, # fontsize_row=5, color = colorRampPalette(c("white","pink","red"))(100), breaks=seq(0, 1, length.out = 100), treeheight_row=10, treeheight_col=10, border_color=NA)
参考文献
Aibar, Sara, Carmen Bravo González-Blas, Thomas Moerman, Jasper Wouters, Vân Anh Huynh-Thu, Hana Imrichová, Zeynep Kalender Atak, et al. . “SCENIC: Single-Cell Regulatory Network Inference and Clustering.” Nature Methods 14 (october): 1083–6. doi:10.1038/nmeth.4463.
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