继续跟着大咖们的步伐学习。（https://www.jianshu.com/p/c47332b880aa）
分析结果，用上面pbmc的输出结果。
=====第一步：还是获得loom文件=====
可以使用SCopeLoomR把上个帖子的前两步合成一步。get_count_from_seurat.R文件代码如下：
library(optparse)
op_list <- list(
make_option(c("-i", "--inrds"), type = "character", default = NULL, action = "store", help = "The input of Seurat RDS",metavar="rds"),
make_option(c("-d", "--ident"), type = "character", default = NULL, action = "store", help = "The sample Ident of Seurat object",metavar="idents"),
make_option(c("-s", "--size"), type = "integer", default = NULL, action = "store", help = "The sample size of Seurat object",metavar="size"),
make_option(c("-l", "--label"), type = "character", default = "out", action = "store", help = "The label of output file",metavar="label"),
make_option(c("-a", "--assay"), type = "character", default = "RNA", action = "store", help = "The assay of input file",metavar="assay")
)
parser <- OptionParser(option_list = op_list)
opt = parse_args(parser)
assay <- opt$assay
library(Seurat)
obj <- readRDS(optident)) {
Idents(obj) <- optsize
if (!is.null(size)) {
obj <- subset(x = obj, downsample = optlabel)) {
label1 <- 'out'
}else{
label1 <- opt$label
}
library(SCopeLoomR)
outloom <- paste0(label1,".loom")
build_loom(file.name = outloom,dgem = obj@assays[[assay]]@counts)
write.table(obj@meta.data,'metadata_subset.xls',sep='\t',quote=F)
运行如下：Rscript get_count_from_seurat.R -i pbmc.rds -s 20 -l out -a RNA
=== 第二步：运行pySCENIC===
代码和前面一样
./pyscenic_from_loom.sh -i out.loom -n 20
====第三步：计算RSS===
代码和前面一样
Rscript calcRSS_by_scenic.R -l aucell.loom -m metadata_subset.xls -c cell_type
=======第四步：可视化=====
加载一些必要的包
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)
做一些颜色上的基本设置
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))
输入文件的设置
inloom='aucell.loom'
incolor=incolor
inrss='cell_type_rss.rds'
inrds='subset.rds'
infun='median'
ct.col='cell_type'
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))
)
}))
dfsd.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 <- rssPlotdf
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(rssPlotcellType)
nlen2 <- length(lgroup)
wei <- nlen2*2
pdf(paste0('regulons_RSS_',ct.col,'_in_dotplot.pdf'))
print(rssPlot$plot)
dev.off()
sd top gene
anrow = data.frame( group = ntopgcluster))]
names(lcolor) <- unique(anrow$group)
annotation_colors <- list(group=lcolor)
pn1 = rss[ntopgregulon),]
rownames(pn1) <- make.unique(rownames(pn1))
rownames(anrow) <- rownames(pn1)
scale='row'
hei <- ceiling(length(ntopg$regulon)*0.4)
pdf(paste0('regulon_RSS_in_sd_topgene_',ct.col,'.pdf'))
print(
pheatmap(pn1,annotation_row = anrow,scale=scale,annotation_colors=annotation_colors,show_rownames = T,main='sd top regulons')
)
print(
pheatmap(pn2,scale=scale,show_rownames = T, main='sd top unique regulons')
)
dev.off()
plotRSS gene
pn2 = rss[unique(rp_dfTopic))*0.4)
pdf(paste0('regulon_RSS_in_plotRSS_',ct.col,'.pdf'))
print(
pheatmap(pn2,scale=scale,show_rownames = T, main='plotRSS unique regulons')
)
dev.off()
all regulons
hei <- ceiling(length(rownames(rss))*0.2)
pdf(paste0('all_regulons_RSS_in_',ct.col,'.pdf'))
print(
pheatmap(rss,scale=scale,show_rownames = T,main='all regulons RSS')
)
dev.off()
plot rss by all cells
if (is.null(inregulons)){
inregulons <- regulonsToPlot
}else{
inregulons <- intersect(inregulons,rownames(rss))
regulonsToPlot <- inregulons
}
pn3=as.matrix(regulonAUC@assays@data$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(ntopgcluster)
annotation_colors <- list(group1 =lcolor)
df1 <- ancol
df1group1),]
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'))
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$sub_celltype <- sce@meta.data[,ct.col]
sub_regulonAUC <- regulonAUC[,match(colnames(sce),colnames(regulonAUC))]
cellClusters <- data.frame(row.names = colnames(sce),
seurat_clusters = as.character(scesub_celltype)
sce@meta.data = cbind(sce@meta.data ,t(sub_regulonAUC@assays@data@listDatasub_celltype
nlen <- length(regulonsToPlot)
hei <- ceiling(nlen)0.4
blu<-colorRampPalette(brewer.pal(9,"Blues"))(100)
nlen2 <- length(unique(sce$sub_celltype))
wei <- nlen22
pdf('regulons_activity_in_dotplot.pdf')
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')
print(DotPlot(sce, features = regulonsToPlot))
print(RidgePlot(sce, features = regulonsToPlot , ncol = 4))
print(VlnPlot(sce, features = regulonsToPlot,pt.size = 0 ))
print(FeaturePlot(sce, features = regulonsToPlot))
dev.off()
会显示类似于下面的图，但是我的测试数据list太多了，就不放了。
grn <- read.table(ingrn,sep='\t',header=T,stringsAsFactors=F)
inregulons1=gsub('[(+)]','',inregulons)
c1 <- which(grn$TF %in% inregulons1)
grn <- grn[c1,]
edge1 <- data.frame()
node1 <- data.frame()
pdf(paste0(ntop2,'_regulon_netplot.pdf'))
for (tf in unique(grnimportance,decreasing=T),]
tmp <- tmp[1:ntop2,]
}
node2 <- data.frame(tmpnode.size=1.5
node2$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')
edge2edge.width <- scales::rescale(edge2$edge.width,to=torange)
graph_data <- tidygraph::tbl_graph(nodes = node2, edges = edge2, directed = T)
p1 <- ggraph(graph = graph_data, layout = "stress", circular = TRUE) + geom_edge_arc(aes(edge_colour = edge.colour, edge_width = edge.width)) +
scale_edge_width_continuous(range = c(1,0.2)) +geom_node_point(aes(colour = node.colour, size = node.size))+ theme_void() +
geom_node_label(aes(label = node,colour = node.colour),size = 3.5, repel = TRUE)
p1 <- p1 + scale_color_manual(values=c('#FFDA00','black'))+scale_edge_color_manual(values=c("#1B9E77"))
print(p1)
}
dev.off()
每个转录因子生成一个网络图
plot activity heatmap
meta <- sce@meta.data
celltype <- ct.col
cellsPerGroup <- split(rownames(meta),meta[,celltype])
sub_regulonAUC <- regulonAUC[onlyNonDuplicatedExtended(rownames(regulonAUC)),]
Calculate average expression:
regulonActivity_byGroup <- sapply(cellsPerGroup,
function(cells)
rowMeans(getAUC(sub_regulonAUC)[,cells]))
scale='row'
rss <- regulonActivity_byGroup
hei <- ceiling(length(regulonsToPlot)*0.4)
pn1 <- rss[regulonsToPlot,]
pdf(paste0('regulon_activity_in_',ct.col,'.pdf'))
print(
pheatmap(pn1,scale=scale,show_rownames = T, main='regulons activity')
)
dev.off()
pdf(paste0('all_regulons_activity_in_',ct.col,'.pdf'))
print(
pheatmap(rss,scale=scale,show_rownames = T,main='all regulons activity')
)
dev.off()
calculate fc
df = do.call(rbind,
lapply(1:ncol(rss), function(i){
dat= data.frame(
regulon = rownames(rss),
cluster = colnames(rss)[i],
sd.1 = rss[,i],
sd.2 = apply(rss[,-i], 1, get(infun))
)
}))
dfsd.1 - df$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 = ntopgcluster))]
names(lcolor) <- unique(anrowregulon,]
pn2 = rss[unique(ntopgregulon)*0.4)
pdf(paste0('regulon_activity_in_sd_topgene_',ct.col,'.pdf'))
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()
当然可以把整个过程给封装起来了一个函数，放在calcRSS_by_scenic.R函数里面。
