适用背景

之前写了一篇文章介绍 四步完成单细胞数据调控网络流程分析 ，但生成的结果没有进行可视化，所以本篇文章将介绍可视化的内容。
本篇可视化主要分成三个部分：

SCENIC/pySCENIC结果具体展示什么呢？
据我初步了解，主要展示regulons的活性值，所谓regulons其实应该就是TF，另外可以展示这些regulons调控的基因网络图。
另外，SCENIC提供了一个plotRSS函数能根据RSS得分筛选细胞类型特异的regulons，而且可以对RSS得分进行可视化。注意RSS分值和活性值不能搞混，RSS分值衡量的是regulons对某种细胞类型的特异性。

可视化举例

做热图主要分为两种，一种是把细胞分组求regulons的活性平均值/中位数，另一种是展示所有细胞regulons的活性平均值。

1、细胞分组求regulons的活性平均值/中位数

这个数据集已做好降维聚类和注释，注释列为seurat_annotations：

> head(sce)
               orig.ident nCount_RNA nFeature_RNA seurat_annotations percent.mt
AAACATACAACCAC     pbmc3k       2419          779       Memory CD4 T  3.0177759
AAACATTGAGCTAC     pbmc3k       4903         1352                  B  3.7935958
AAACATTGATCAGC     pbmc3k       3147         1129       Memory CD4 T  0.8897363
AAACCGTGTATGCG     pbmc3k        980          521                 NK  1.2244898
AAACGCACTGGTAC     pbmc3k       2163          781       Memory CD4 T  1.6643551
AAACGCTGACCAGT     pbmc3k       2175          782              CD8 T  3.8160920
AAACGCTGGTTCTT     pbmc3k       2260          790              CD8 T  3.0973451
AAACGCTGTAGCCA     pbmc3k       1275          532        Naive CD4 T  1.1764706
AAACGCTGTTTCTG     pbmc3k       1103          550       FCGR3A+ Mono  2.9011786
AAACTTGAAAAACG     pbmc3k       3914         1112                  B  2.6315789
               RNA_snn_res.0.5 seurat_clusters       group1 sub_celltype
AAACATACAACCAC               1               1 Memory CD4 T Memory CD4 T
AAACATTGAGCTAC               3               3            B            B
AAACATTGATCAGC               1               1 Memory CD4 T Memory CD4 T
AAACCGTGTATGCG               6               6           NK           NK
AAACGCACTGGTAC               1               1 Memory CD4 T Memory CD4 T
AAACGCTGACCAGT               4               4        CD8 T        CD8 T
AAACGCTGGTTCTT               4               4        CD8 T        CD8 T
AAACGCTGTAGCCA               0               0  Naive CD4 T  Naive CD4 T
AAACGCTGTTTCTG               5               5 FCGR3A+ Mono FCGR3A+ Mono
AAACTTGAAAAACG               3               3            B            B
然后按照之前的文章跑一下流程：
Rscript get_count_from_seurat.R -i pbmc3k.rds -d seurat_annotations -s 200 -l out
python create_loom_file_from_scanpy.py -i out.csv
sh pyscenic_from_loom.sh -i out.loom -n 10
Rscript calcRSS_by_scenic.R -l aucell.loom -m metadata_subset.xls -c seurat_annotations
生成文件有：
aucell.loom
ctx.csv
grn.tsv
metadata_subset.xls
out.csv
out.loom
regulon_RSS.Rdata
seurat_annotations_rss.rds
subset.rds
加载需要的包：
library(Seurat)
library(SCopeLoomR)
library(AUCell)
library(SCENIC)
library(dplyr)
library(KernSmooth)
library(RColorBrewer)
library(plotly)
library(BiocParallel)
library(pheatmap)
library(cowplot)
library(ggpubr)
library(ggsci)
library(ggplot2)
library(tidygraph)
library(ggraph)
library(stringr)
colpalettes<-unique(c(pal_npg("nrc")(10),pal_aaas("default")(10),pal_nejm("default")(8),pal_lancet("lanonc")(9),
                      pal_jama("default")(7),pal_jco("default")(10),pal_ucscgb("default")(26),pal_d3("category10")(10),
                      pal_locuszoom("default")(7),pal_igv("default")(51),
                      pal_uchicago("default")(9),pal_startrek("uniform")(7),
                      pal_tron("legacy")(7),pal_futurama("planetexpress")(12),pal_rickandmorty("schwifty")(12),
                      pal_simpsons("springfield")(16),pal_gsea("default")(12)))
len <- 100
incolor<-c(brewer.pal(8, "Dark2"),brewer.pal(12, "Paired"),brewer.pal(8, "Set2"),brewer.pal(9, "Set1"),colpalettes,rainbow(len))
主函数plot_pyscenic
plot_pyscenic函数参数介绍：
inloom='aucell.loom'，pyscenic得到的aucell.loom文件，可更改。
incolor=incolor，调色板，可自定义
inrss='seurat_annotations_rss.rds'，计算细胞类型特异性regulons得到的_rss.rds后缀文件
inrds='subset.rds'，用于分析pyscenic的rds，这里分组随机取了200个细胞
infun='median'，展示的热图用中位数还是平均值，默认用中位数，平均值改成mean。
ct.col='seurat_annotations'，分组的列，这里用seurat_annotations
inregulons=NULL，可以指定感兴趣的regulons，如果没有指定，默认使用前5的
ingrn='grn.tsv'
ntop1=5，热图展示的活性值top regulon，默认为前5.
ntop2=50，网络图展示regulons调控的权重top基因，默认为前50。
plot_pyscenic <- function(inloom='aucell.loom',incolor=incolor,inrss='seurat_annotations_rss.rds',inrds='subset.rds',infun='median', ct.col='seurat_annotations',inregulons=NULL,ingrn='grn.tsv',ntop1=5,ntop2=50){
###load data
loom <- open_loom(inloom)
regulons_incidMat <- get_regulons(loom, column.attr.name="Regulons")
regulons <- regulonsToGeneLists(regulons_incidMat)
regulonAUC <- get_regulons_AUC(loom,column.attr.name='RegulonsAUC')
regulonAucThresholds <- get_regulon_thresholds(loom)
embeddings <- get_embeddings(loom)
close_loom(loom)
rss <- readRDS(inrss)
sce <- readRDS(inrds)
##calculate  RSS fc
df = do.call(rbind,
             lapply(1:ncol(rss), function(i){
                dat= data.frame(
                regulon  = rownames(rss),
                cluster =  colnames(rss)[i],
                sd.1 = rss[,i],
                sd.2 = apply(rss[,-i], 1, get(infun))
df$fc = df$sd.1 - df$sd.2
#select top regulon
ntopg <- df %>% group_by(cluster) %>% top_n(ntop1, fc)
ntopgene <- unique(ntopg$regulon)
write.table(ntopgene,'sd_regulon_RSS.list',sep='\t',quote=F,row.names=F,col.names=F)
#plot rss by cluster
#using plotRSS
rssPlot <- plotRSS(rss)
regulonsToPlot <- rssPlot$rowOrder
rp_df <- rssPlot$df
write.table(regulonsToPlot,'rss_regulon.list',sep='\t',quote=F,row.names=F,col.names=F)
write.table(rp_df,'rssPlot_data.xls',sep='\t',quote=F)
nlen <- length(regulonsToPlot)
hei <- ceiling(nlen)*0.4
blu<-colorRampPalette(brewer.pal(9,"Blues"))(100)
lgroup <- levels(rssPlot$df$cellType)
nlen2 <- length(lgroup)
wei <- nlen2*2
pdf(paste0('regulons_RSS_',ct.col,'_in_dotplot.pdf'),wei,hei)
print(rssPlot$plot)
dev.off()
# sd top gene
anrow = data.frame( group = ntopg$cluster)
lcolor <- incolor[1:length(unique(ntopg$cluster))]
names(lcolor) <- unique(anrow$group)
annotation_colors <- list(group=lcolor)
pn1 = rss[ntopg$regulon,]
pn2 = rss[unique(ntopg$regulon),]
rownames(pn1) <-  make.unique(rownames(pn1))
rownames(anrow) <- rownames(pn1)
scale='row'
hei <- ceiling(length(ntopg$regulon)*0.4)
pdf(paste0('regulon_RSS_in_sd_topgene_',ct.col,'.pdf'),wei,hei)
print(
pheatmap(pn1,annotation_row = anrow,scale=scale,annotation_colors=annotation_colors,show_rownames = T,main='sd top regulons')
print(
pheatmap(pn2,scale=scale,show_rownames = T, main='sd top unique regulons')
dev.off()
#plotRSS gene
pn2 = rss[unique(rp_df$Topic),]
scale='row'
hei <- ceiling(length(unique(rp_df$Topic))*0.4)
pdf(paste0('regulon_RSS_in_plotRSS_',ct.col,'.pdf'),wei,hei)
print(
pheatmap(pn2,scale=scale,show_rownames = T, main='plotRSS unique regulons')
dev.off()
#all regulons
hei <- ceiling(length(rownames(rss))*0.2)
pdf(paste0('all_regulons_RSS_in_',ct.col,'.pdf'),wei,hei)
print(
pheatmap(rss,scale=scale,show_rownames = T,main='all regulons RSS')
dev.off()
#plot rss by all cells
if (is.null(inregulons)){
inregulons <- regulonsToPlot
}else{
inregulons <- intersect(inregulons,rownames(rss))
regulonsToPlot <- inregulons
pn3=as.matrix(regulonAUC@assays@data$AUC)
regulon <- rownames(pn3)
#regulon <- inregulons
pn3 <- pn3[regulon,]
#pn3 <- pn3[,sample(1:dim(pn3)[2],500)]
sce$group1=sce@meta.data[,ct.col]
meta <- sce@meta.data
meta <- meta[order(meta$group1),]
#meta <- meta[colnames(pn3),]
ancol = data.frame(meta[,c('group1')])
colnames(ancol) <- c('group1')
rownames(ancol) <- rownames(meta)
lcolor <- incolor[1:length(unique(ntopg$cluster))]
names(lcolor) <- unique(ntopg$cluster)
annotation_colors <- list(group1 =lcolor)
df1 <- ancol
df1$cell <- rownames(df1)
df1 <- df1[order(df1$group1),]
pn3 <- pn3[,rownames(df1)]
torange=c(-2,2)
pn3 <- scales::rescale(pn3,to=torange)
pn3 <- pn3[,rownames(ancol)]
scale='none'
hei <- ceiling(length(unique(regulon))*0.2)
pdf(paste0('all_regulon_activity_in_allcells.pdf'),10,hei)
print(
pheatmap(pn3,annotation_col = ancol,scale=scale,annotation_colors=annotation_colors,show_rownames = T,show_colnames = F,cluster_cols=F)
#pheatmap(pn3,scale=scale,show_rownames = T, show_colnames = F,cluster_cols=F)
dev.off()
#plot in seurat
regulonsToPlot = inregulons
sce$sub_celltype <- sce@meta.data[,ct.col]
sub_regulonAUC <- regulonAUC[,match(colnames(sce),colnames(regulonAUC))]
cellClusters <- data.frame(row.names = colnames(sce),
                           seurat_clusters = as.character(sce$seurat_clusters))
cellTypes <- data.frame(row.names = colnames(sce),
                        celltype = sce$sub_celltype)
sce@meta.data = cbind(sce@meta.data ,t(sub_regulonAUC@assays@data@listData$AUC[regulonsToPlot,]))
Idents(sce) <- sce$sub_celltype
nlen <- length(regulonsToPlot)
hei <- ceiling(nlen)*0.4
blu<-colorRampPalette(brewer.pal(9,"Blues"))(100)
nlen2 <- length(unique(sce$sub_celltype))
wei <- nlen2*2
pdf('regulons_activity_in_dotplot.pdf',wei,hei)
print(DotPlot(sce, features = unique(regulonsToPlot)) + coord_flip()+
      theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = .5))+
      scale_color_gradientn(colours = blu)
dev.off()
hei=ceiling(nlen/4)*4
pdf('regulons_activity_in_umap.pdf',16,hei)
print(RidgePlot(sce, features = regulonsToPlot , ncol = 4))
print(VlnPlot(sce, features = regulonsToPlot,pt.size = 0 ))
print(FeaturePlot(sce, features = regulonsToPlot))
dev.off()
grn <- read.table(ingrn,sep='\t',header=T,stringsAsFactors=F)
inregulons1=gsub('[(+)]','',inregulons)
c1 <- which(grn$TF %in% inregulons1)
grn <- grn[c1,]
#edge1 <- data.frame()
#node1 <- data.frame()
pdf(paste0(ntop2,'_regulon_netplot.pdf'),10,10)
for (tf in unique(grn$TF)) {
tmp <- subset(grn,TF==tf)
if (dim(tmp)[1] > ntop2) {
tmp <- tmp[order(tmp$importance,decreasing=T),]
tmp <- tmp[1:ntop2,]
node2 <- data.frame(tmp$target)
node2$node.size=1.5
node2$node.colour <- 'black'
colnames(node2) <- c('node','node.size','node.colour')
df1 <- data.frame(node=tf,node.size=2,node.colour='#FFDA00')
node2 <- rbind(df1,node2)
edge2 <- tmp
colnames(edge2) <- c('from','to','edge.width')
edge2$edge.colour <- "#1B9E77"
torange=c(0.1,1)
edge2$edge.width <- scales::rescale(edge2$edge.width,to=torange)
graph_data <- tidygraph::tbl_graph(nodes = node2, edges = edge2, directed = T)
p1 <- ggraph(graph = graph_data, layout = "stress", circular = TRUE) + geom_edge_arc(aes(edge_colour = edge.colour, edge_width = edge.width)) +
  scale_edge_width_continuous(range = c(1,0.2)) +geom_node_point(aes(colour = node.colour, size = node.size))+ theme_void() +
      geom_node_label(aes(label = node,colour = node.colour),size = 3.5, repel = TRUE)
p1 <- p1 + scale_color_manual(values=c('#FFDA00','black'))+scale_edge_color_manual(values=c("#1B9E77"))
print(p1)
dev.off()
#plot activity heatmap
meta <- sce@meta.data
celltype <- ct.col
cellsPerGroup <- split(rownames(meta),meta[,celltype])
sub_regulonAUC <- regulonAUC[onlyNonDuplicatedExtended(rownames(regulonAUC)),]
# Calculate average expression:
regulonActivity_byGroup <- sapply(cellsPerGroup,
                                  function(cells)
                                    rowMeans(getAUC(sub_regulonAUC)[,cells]))
scale='row'
rss <- regulonActivity_byGroup
hei <- ceiling(length(regulonsToPlot)*0.4)
pn1 <- rss[regulonsToPlot,]
pdf(paste0('regulon_activity_in_',ct.col,'.pdf'),wei,hei)
print(
pheatmap(pn1,scale=scale,show_rownames = T, main='regulons activity')
dev.off()
hei <- ceiling(length(rownames(rss))*0.2)
pdf(paste0('all_regulons_activity_in_',ct.col,'.pdf'),wei,hei)
print(
pheatmap(rss,scale=scale,show_rownames = T,main='all regulons activity')
dev.off()
##calculate fc
df = do.call(rbind,
             lapply(1:ncol(rss), function(i){
                dat= data.frame(
                regulon  = rownames(rss),
                cluster =  colnames(rss)[i],
                sd.1 = rss[,i],
                sd.2 = apply(rss[,-i], 1, get(infun))
df$fc = df$sd.1 - df$sd.2
#select top regulon
ntopg <- df %>% group_by(cluster) %>% top_n(ntop1, fc)
ntopgene <- unique(ntopg$regulon)
write.table(ntopgene,'sd_regulon_activity.list',sep='\t',quote=F,row.names=F,col.names=F)
anrow = data.frame( group = ntopg$cluster)
lcolor <- incolor[1:length(unique(ntopg$cluster))]
names(lcolor) <- unique(anrow$group)
annotation_colors <- list(group=lcolor)
pn1 = rss[ntopg$regulon,]
pn2 = rss[unique(ntopg$regulon),]
rownames(pn1) <-  make.unique(rownames(pn1))
rownames(anrow) <- rownames(pn1)
scale='row'
hei <- ceiling(length(ntopg$regulon)*0.4)
pdf(paste0('regulon_activity_in_sd_topgene_',ct.col,'.pdf'),wei,hei)
print(
pheatmap(pn1,annotation_row = anrow,scale=scale,annotation_colors=annotation_colors,show_rownames = T,main='sd top regulons')
print(
pheatmap(pn2,scale=scale,show_rownames = T, main='sd top unique regulons ')
dev.off()
使用方法及结果
使用示例：
plot_pyscenic(inloom='aucell.loom',incolor=incolor,inrss='seurat_annotations_rss.rds',inrds='subset.rds',infun='median', ct.col='seurat_annotations',inregulons=NULL,ingrn='grn.tsv',ntop1=5,ntop2=50)
生成文件如下：
所有细胞中找到的所有regulons的活性热图文件all_regulon_activity_in_allcells.pdf