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maSigPro_control_2.R
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maSigPro_control_2.R
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library(Seurat)
library(ggplot2)
library(SCENIC)
require("RColorBrewer")
library(maSigPro)
seurat.chon <- readRDS('JingMA_NEW/res/Harmony/ALL/RDS/seurat_celltype_Chond.Rdata')
seurat.chon <- subset(seurat.chon, subset = batch %in% c('C4', 'C6', 'M1', 'M2', 'M3'))
path.change='/local/yzj/JingMA_NEW/res/maSigPro/'
file.tstep <- paste0(path.change, 'tstep.Rdata')
tstep <- readRDS(file.tstep)
sigs = get.siggenes(tstep, rsq = 0.25, vars = "groups")
# plot single gene
Harmony2 <- seurat.chon@reductions$harmony@cell.embeddings[, 'harmony_2']
mat.gene <- seurat.chon@assays$RNA@data
# AUC
regulonAUC <- readRDS(file='/local/yzj/JingMA_NEW/res/SCENIC_main/int/3.4_regulonAUC.Rds')
regulonAUC <- regulonAUC[onlyNonDuplicatedExtended(rownames(regulonAUC)), colnames(mat.gene)]
mat.auc <- as.matrix(regulonAUC@assays@data@listData$AUC)
# mat.gene.TF <- rbind(as.matrix(mat.gene), mat.auc)
df.pc.gene <- data.frame(t(rbind(as.matrix(mat.gene), mat.auc)), check.names = F)
df.pc.gene$Harmony2 <- Harmony2
df.pc.gene$celltype <- seurat.chon$celltype
df.pc.gene$status <- seurat.chon$type
df.pc.gene <- df.pc.gene[order(Harmony2, decreasing = F),]
df.pc.gene$idx <- 1:nrow(df.pc.gene)
# sig genes
sigs.new <- sigs$sig.genes$AdultsvsChildren
new.genes <- intersect(rownames(sigs.new$sig.profiles), rownames(mat.gene))
length(new.genes)
# XY genes
df.genes.v19 <- read.delim(
'/local/yzj/publicData/annotation/hg19/gencode_v19_gene_pos.txt',
header = F)
XY.genes <- df.genes.v19$V1[df.genes.v19$V2 %in% c('chrY', 'chrX')]
new.genes <- setdiff(new.genes, XY.genes)
length(new.genes)
sigs.new$sig.profiles <- sigs.new$sig.profiles[new.genes,]
sigs.new$coefficients <- sigs.new$coefficients[new.genes,]
sigs.new$group.coeffs <- sigs.new$group.coeffs[new.genes,]
sigs.new$sig.pvalues <- sigs.new$sig.pvalues[new.genes,]
file.cluster <- paste0(path.change, 'cluster.Rdata')
res.cluster <- readRDS(file.cluster)
df.pval <- sigs.new$sig.pvalues
df.coff <- sigs.new$coefficients
View(merge(res.cluster$cut, df.pval, by = 'row.names'))
View(merge(res.cluster$cut, df.coff, by = 'row.names'))
df_out <- merge(res.cluster$cut, df.pval, by = 'row.names')
names(df_out) <- c('Gene', 'Module', names(df.pval))
file.info.module <- paste0(path.change, 'Info_modules.txt')
# 输出module的信息
write.table(df_out, file.info.module, sep = '\t', quote = F,
row.names = F, col.names = T)
############################# supp module trend
#################### 12个module的图
# plot by clust
df.pc.gene.sel <- df.pc.gene[, c('idx', 'Harmony2', 'celltype', 'status', new.genes)]
clusters <- names(table(res.cluster$cut))
sort.plot.clusters <- c('Module_3', 'Module_12', 'Module_11',
'Module_7', 'Module_4', 'Module_5',
'Module_1', 'Module_2', 'Module_9',
'Module_8', 'Module_10', 'Module_6')
sort.plot.Modules <- c('Module_3', 'Module_4', 'Module_9',
'Module_12', 'Module_5', 'Module_8',
'Module_11', 'Module_1', 'Module_10',
'Module_7', 'Module_2', 'Module_6')
colors <- c("#EE9572","#B2DF8A" ,"#A6CEE3","#9999FF")
names(colors) <- c('CSC', 'C0', 'C1', 'C2')
###
sample.cells <- sample(rownames(df.pc.gene.sel),5000)
df.pc.gene.sel.sample <- df.pc.gene.sel[rownames(df.pc.gene.sel)%in%sample.cells,]
###
list.plot <- list()
for (cluster in sort.plot.Modules) {
cluster_id <- as.numeric(strsplit(cluster, split = '_')[[1]][2])
sub.gene <- intersect(names(res.cluster$cut[res.cluster$cut == cluster_id]), new.genes)
if (length(sub.gene) < 1) {next()}
sub.df.gene <- df.pc.gene.sel.sample[,sub.gene]
sub.df <- data.frame(idx = df.pc.gene.sel.sample$idx, status = df.pc.gene.sel.sample$status,
celltype = df.pc.gene.sel.sample$celltype,
mean.exp = rowMeans(sub.df.gene))
sub.df$status <- factor(sub.df$status,levels = c('Microtia','Normal'))
p.mean <-
ggplot(data = sub.df, aes(x = idx, y = mean.exp, linetype = status)) +
geom_point(aes(color = celltype), size = 0.3) + theme_bw()+
scale_color_discrete(breaks = c('CSC', 'C0', 'C1', 'C2'),
labels = c('CSC', 'C0', 'C1', 'C2')) +
scale_color_manual(labels = c('CSC', 'C0', 'C1', 'C2'),
values = colors) +
# xlim(-30, 10) +
geom_smooth(color = '#696969') +
labs(x = '', y = '') +
theme(panel.background=element_rect(fill='transparent', color='black',size = 0.4),
panel.grid = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
legend.position = 'none',
plot.margin = unit(c(0.1,0.1,-0.5,-0.5),'cm')) +
annotate('text', label = cluster,
x = 22000, y = max(sub.df$mean.exp),
hjust = 1, vjust = 1, size = 2.7)
list.plot[[cluster]] <- p.mean
}
library(gridExtra)
p.merge <- marrangeGrob(list.plot, ncol = 4, nrow = 3, top = NULL)
ggsave(plot = p.merge, path = path.change,
filename = 'sFig6_module_plot.pdf',
height = 8, width = 16, units = 'cm')
# plot
Time <- as.character(seurat.chon$celltype)
Harmony2 <- seurat.chon@reductions$harmony@cell.embeddings[, 'harmony_2']
quantile.H2 <- quantile(Harmony2, probs = seq(0, 1, 1/101))
for (i in 0:100) {
Time[Harmony2 >= quantile.H2[i+1] & Harmony2 <= quantile.H2[i+2]] <- i
}
Time <- as.numeric(Time)
# mat.sel <- seurat.chon@assays$RNA@scale.data[new.genes,]
# length(new.genes)
# dim(mat.sel)
mat.gene <- seurat.chon@assays$RNA@data
df.sig.genes <- data.frame(t(as.matrix(mat.gene[new.genes,])), check.names = F)
df.sig.genes$Harmony2 <- Harmony2
df.sig.genes$celltype <- seurat.chon$celltype
df.sig.genes$status <- seurat.chon$type
df.sig.genes$Time.point <- Time
df.sig.genes <- df.sig.genes[order(Harmony2, decreasing = F),]
df.sig.genes$idx <- 1:nrow(df.sig.genes)
df.time <- data.frame()
df.time.status <- data.frame()
for (cluster in clusters) {
sub.gene <- intersect(names(res.cluster$cut[res.cluster$cut == cluster]), new.genes)
vec.single <- rowMeans(df.sig.genes[,sub.gene])
agge.group <- aggregate(vec.single, by = list(df.sig.genes$Time.point), FUN = mean)
sub.df <- data.frame(Time.point = agge.group$Group.1,
cluster = rep(paste0('Module_', cluster), nrow(agge.group)),
mean.exp = agge.group$x)
df.time <- rbind(df.time, sub.df)
for (st in unique(df.sig.genes$status)) {
vec.single.st <- vec.single[df.sig.genes$status == st]
vec.time.st <- df.sig.genes$Time.point[df.sig.genes$status == st]
agge.group.st <- aggregate(vec.single.st, by = list(vec.time.st), FUN = mean)
sub.df.st <- data.frame(Time.point = agge.group.st$Group.1,
cluster = rep(paste0('Module_', cluster), nrow(agge.group.st)),
status = rep(st, nrow(agge.group.st)),
mean.exp = agge.group.st$x)
df.time.status <- rbind(df.time.status, sub.df.st)
}
}
mat.plot <- reshape2::dcast(df.time, cluster ~ Time.point, value.var = 'mean.exp')
row.names(mat.plot) <- mat.plot$cluster
mat.plot$cluster <- NULL
mat.plot <- t(scale(t(as.matrix(mat.plot))))
# no status / clust tree
bk <- c(seq(-10,-0.1,by=0.01),seq(0,10,by=0.01))
# 从pheatmap截出来一个层级聚类树
pheatmap::pheatmap(mat.plot,
color = c(colorRampPalette(colors = c("blue","white"))(length(bk)/2),colorRampPalette(colors = c("white","red"))(length(bk)/2)),
cluster_rows = T, cluster_cols = F, scale = "none",
display_numbers = F,
# annotation_col = annotation_col, annotation_colors = ann_colors,
show_rownames = T, show_colnames = F, legend = T,
fontsize_row = 10,
# labels_row = labels_row,
# gaps_col = c(4),
filename = paste0(path.change, 'heatmap_time_change.png'),
width = 10, height = 6
)
# add status
sort.clusters <- c('Module_3', 'Module_12', 'Module_11',
'Module_7', 'Module_4', 'Module_5',
'Module_1', 'Module_2', 'Module_9',
'Module_8', 'Module_10', 'Module_6')
sort.row <- c()
for (sub in rev(sort.clusters)) {
sort.row <- c(sort.row, c(paste0('Microtia_', sub), paste0('Normal_', sub)))
}
df.time.status$row_name <- paste(df.time.status$Time.point, df.time.status$status, sep = '_')
mat.heatmap <- reshape2::dcast(df.time.status, row_name ~ cluster, value.var = 'mean.exp')
row.names(mat.heatmap) <- mat.heatmap$row_name
mat.heatmap$row_name <- NULL
mat.heatmap <- scale(as.matrix(mat.heatmap))
mat.heatmap[mat.heatmap > 2.95] <- 2.95
df.heatmap <- reshape2::melt(mat.heatmap)
names(df.heatmap) <- c('row_name', 'cluster', 'MeanExp')
df.heatmap <- merge(df.heatmap, df.time.status, by = c('cluster', 'row_name'))
df.heatmap$cluster <- factor(df.heatmap$cluster, levels = sort.clusters)
df.heatmap$status <- factor(df.heatmap$status, levels = c("Microtia", "Normal"))
df.heatmap$Time.point <- as.factor(df.heatmap$Time.point)
df.heatmap$st_cluster <- paste(df.heatmap$status, df.heatmap$cluster, sep = '_')
df.heatmap$st_cluster <- factor(df.heatmap$st_cluster, levels = sort.row)
# 渐变的heatmap图
plot.heatmap <-
ggplot(data = df.heatmap, aes(x = Time.point, y = st_cluster)) +
geom_tile(aes(fill = MeanExp)) +
facet_grid(cluster ~ ., scales = 'free', space = 'free') +
scale_fill_gradientn(
colors = colorRampPalette(rev(brewer.pal(n = 11, name = "Spectral"))[1:9])(100)) +
scale_y_discrete(position = 'left') +
scale_x_discrete(breaks = c(0, 25, 50, 75, 100)) +
coord_cartesian(xlim=c(0, 100)) +
labs(x = 'Pseudotime', y = '', fill = 'Scaled \nexpression') +
theme(panel.background = element_rect(color = 'transparent',
fill = 'transparent'),
axis.text.x = element_text(size = 7, color = "black", family = 'Arial'),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
# strip.text.x = element_text(
# size = 8, color = "black", family = 'Arial'),
strip.background = element_rect(
color = "transparent", fill = "transparent"),
strip.text.y = element_text(angle = 0, size = 7, color = "black",
family = 'Arial'),
panel.spacing = unit(0.05, "cm"),
strip.placement = "inside",
axis.title.x = element_text(
size = 8, color = "black", family = 'Arial'),
legend.title = element_text(
size = 8, color = "black", family = 'Arial'),
legend.text = element_text(
size = 7, color = "black", family = 'Arial'),
legend.position = 'right',
plot.margin = unit(c(0, 0.2, 0.2, -0.6),"cm"),
legend.key.size = unit(10, "pt"))
ggsave(filename = 'heatmap_time_change_status.png',
path = path.change, plot = plot.heatmap,
units = 'cm', height = 12, width = 18)
# library(tidyverse)
# library(aplot)
library(patchwork)
df.status <- data.frame(status=rep(c("Microtia","Normal"), times=12),
p = rep('white', 24), row_name = sort.row,
cluster = rep(rev(sort.clusters), c(rep(2, 12))))
df.status$row_name <- factor(df.status$row_name, levels = (sort.row))
df.status$cluster <- factor(df.status$cluster, levels = sort.clusters)
df.status$status <- factor(df.status$status, levels = c("Microtia", "Normal"))
# 状态的左侧标识
plot.Status <-
ggplot(data = df.status, aes(x=p,y=row_name,fill=status))+
geom_tile() +
facet_grid(cluster ~ ., scales = 'free', space = 'free') +
scale_y_discrete(position="right") +
scale_fill_manual(breaks=c("Microtia", "Normal"),
values = c("#DC143C", "#6C6C6C"),
label = c("Microtia", "NC")) +
xlab(NULL) + ylab(NULL) +
theme(panel.background = element_rect(color = 'transparent',
fill = 'transparent'),
strip.text.y = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.spacing = unit(0.1, "cm"),
legend.title = element_text(
size = 8, color = "black", family = 'Arial'),
legend.text = element_text(
size = 7, color = "black", family = 'Arial'),
legend.position = 'left',
plot.margin = unit(c(0, -1, 0.2, 0.2),"cm"),
legend.key.size = unit(10, "pt"))+
labs(fill = "Status") +
guides(fill=guide_legend(reverse=T))
ggsave(filename = 'heatmap_bar.png',
path = path.change, plot = plot.Status,
units = 'cm', height = 12, width = 4.2)
# 合并状态标识和变化热图，但是空隙太大，还是手动截取再合并吧
plot.final <- plot.Status + plot.heatmap + plot_layout(widths = c(1, 32),
guides = 'collect')
ggsave(filename = 'heatmap_time_change_final.png',
path = path.change, plot = plot.final,
units = 'cm', height = 7, width = 14)
colors <- c("#EE9572","#B2DF8A" ,"#A6CEE3","#9999FF")
names(colors) <- c('CSC', 'TC', 'C1', 'C2')
# 密度曲线
plot.dens <-
ggplot(df.sig.genes, aes(x = Time.point, color = celltype, fill = celltype)) +
geom_density(alpha = 0.3) +
scale_color_manual(breaks = names(colors), values = colors) +
scale_color_discrete(breaks = c('CSC', 'TC', 'C1', 'C2'),
labels = c('CSPC', 'EC', 'IC', 'LC')) +
scale_fill_discrete(breaks = c('CSC', 'TC', 'C1', 'C2'),
labels = c('CSPC', 'EC', 'IC', 'LC')) +
labs(x = '', y = 'Probability density',
color = 'Cell type', fill = 'Cell type') +
theme(panel.background = element_rect(color = 'transparent',
fill = 'transparent'),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_text(
size = 6, color = "black", family = 'Arial',
margin=margin(0,-10,0,0)),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.title = element_text(
size = 8, color = "black", family = 'Arial'),
legend.text = element_text(
size = 7, color = "black", family = 'Arial'),
plot.margin = unit(c(0.2, 0, 0, 0.2),"cm"),
legend.key.size = unit(10, "pt"))
ggsave(filename = 'heatmap_density.png',
path = path.change, plot = plot.dens,
units = 'cm', height = 2, width = 11)
# percentage of TF
df.TF <- read.table('/home/disk/drizzle/DataBase/Human_TF/TF_names_v_1.01.txt')
length(intersect(new.genes, df.TF$V1))
percent.TF <- data.frame()
for (cluster in clusters) {
sub.gene <- intersect(names(res.cluster$cut[res.cluster$cut == cluster]), new.genes)
percent.TF <-
rbind(percent.TF,
data.frame(Cluster = paste0('Module_', cluster),
Percent = length(intersect(sub.gene, df.TF$V1))/length(sub.gene)))
}
percent.TF$Cluster <- factor(percent.TF$Cluster, levels = rev(sort.clusters))
plot.TF <-
ggplot(percent.TF, aes(x = Cluster, y = Percent)) +
geom_bar(stat = 'identity', color = 'transparent', fill = '#3CB371') +
labs(y = 'Percentage of TF') +
scale_y_continuous(labels = c(0, 0.2, 0.4, 0.6)) +
theme_classic() +
coord_flip() +
theme(panel.background = element_rect(color = 'transparent',
fill = 'transparent'),
axis.text.y = element_blank(),
axis.title.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.x = element_text(
size = 8, color = "black", family = 'Arial'),
axis.text.x = element_text(
size = 7, color = "black", family = 'Arial'),
plot.margin = unit(c(0.6, 0.6, 0.6, -0.1),"cm"))
ggsave(filename = 'heatmap_percent_TF.png',
path = path.change, plot = plot.TF,
units = 'cm', height = 8, width = 3)
############################### 下面全是module的GO富集
# GO enrich per cluster
path.GO <- paste0(path.change, 'GO/')
if (!file.exists(path.GO)) {
dir.create(path.GO)
}
library(org.Hs.eg.db)
library(clusterProfiler)
all.genes <- rownames(mat.count)
use.genes <- intersect(keys(org.Hs.eg.db, keytype = "SYMBOL"), all.genes)
list.go.BP <- list()
list.go.MF <- list()
list.go.CC <- list()
list.go.BP.all <- list()
list.go.MF.all <- list()
list.go.CC.all <- list()
for (cluster in clusters) {
type <- paste0('Module_', cluster)
sub.gene <- intersect(names(res.cluster$cut[res.cluster$cut == cluster]), new.genes)
genes.input <- intersect(sub.gene, use.genes)
egmt <- enrichGO(gene = genes.input, OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
universe = use.genes, pvalueCutoff = 0.1, ont = 'BP')
res.egmt <- egmt@result
list.go.BP.all[[as.character(type)]] <- res.egmt
res.egmt <- simplify(egmt)@result
list.go.BP[[as.character(type)]] <- res.egmt
egmt <- enrichGO(gene = genes.input, OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
universe = use.genes, pvalueCutoff = 0.5, ont = 'MF')
res.egmt <- egmt@result
list.go.MF.all[[as.character(type)]] <- res.egmt
res.egmt <- simplify(egmt)@result
list.go.MF[[as.character(type)]] <- res.egmt
egmt <- enrichGO(gene = genes.input, OrgDb = org.Hs.eg.db, keyType = "SYMBOL",
universe = use.genes, pvalueCutoff = 0.5, ont = 'CC')
list.go.CC.all[[as.character(type)]] <- egmt@result
res.egmt <- simplify(egmt)@result
list.go.CC[[as.character(type)]] <- res.egmt
}
file.go.BP.all <- paste0(path.GO, 'GO_BP_all.Rdata')
# saveRDS(list.go.BP.all, file = file.go.BP.all)
# file.go.MF.all <- paste0(path.GO, 'GO_MF_all.Rdata')
# saveRDS(list.go.MF.all, file = file.go.MF.all)
# file.go.CC.all <- paste0(path.GO, 'GO_CC_all.Rdata')
# saveRDS(list.go.CC.all, file = file.go.CC.all)
list.go.BP.all <- readRDS(file.go.BP.all)
# file.go.BP <- paste0(path.GO, 'GO_BP.Rdata')
# saveRDS(list.go.BP, file = file.go.BP)
# file.go.MF <- paste0(path.GO, 'GO_MF.Rdata')
# saveRDS(list.go.MF, file = file.go.MF)
# file.go.CC <- paste0(path.GO, 'GO_CC.Rdata')
# saveRDS(list.go.CC, file = file.go.CC)
list.go.BP <- readRDS(file.go.BP)
# GO plot
list.sel.GO <- list()
list.sel.GO$Module_3 <- c('cellular response to reactive oxygen species')
list.sel.GO$Module_11 <- c('p38MAPK cascade')
list.sel.GO$Module_7 <- c('response to unfolded protein',
'extrinsic apoptotic signaling pathway')
list.sel.GO$Module_4 <- c('negative regulation of protein phosphorylation')
list.sel.GO$Module_5 <- c('cellular response to zinc ion',
'cellular response to copper ion')
list.sel.GO$Module_1 <- c('cartilage development')
list.sel.GO$Module_2 <- c('extracellular matrix organization',
'chondrocyte differentiation',
'cartilage development')
list.sel.GO$Module_9 <- c('cellular transition metal ion homeostasis')
list.sel.GO$Module_8 <- c('chemokine-mediated signaling pathway')
list.sel.GO$Module_6 <- c('interferon-gamma-mediated signaling pathway',
'regulation of inflammatory response',
'extracellular matrix disassembly')
# bubble plot
sort.cells <- names(list.sel.GO)
colors <- brewer.pal(10,"Paired")
df.plot <- data.frame()
i = 0
GOterms <- c()
for (cell in sort.cells) {
i = i + 1
sub.go <- list.go.BP.all[[cell]]
sel.go.term <- list.sel.GO[[cell]]
sel.go <- sub.go[sub.go$Description %in% sel.go.term,
c('Description', 'pvalue')]
sel.go$log10Pval <- -log10(sel.go$pvalue)
sel.go$celltype <- rep(cell, nrow(sel.go))
# sel.go$Description <- factor(sel.go$Description, levels = rev(sel.go.term))
df.plot <- rbind(df.plot, sel.go)
GOterms <- c(GOterms, sel.go.term)
}
col_name <- paste(df.plot$celltype, df.plot$Description, sep = '_')
df.plot$col_name <- factor(col_name, levels = rev(col_name))
df.plot$celltype <- factor(df.plot$celltype, levels = sort.cells)
p <- ggplot(df.plot, aes(x = celltype, y = col_name,
color = celltype, size = log10Pval)) +
geom_point(fill = 'cornsilk') +
scale_color_manual(breaks = sort.cells,
values = colors) +
scale_size_continuous(range = c(3,5)) +
scale_y_discrete(breaks = col_name, labels = GOterms) +
labs(x = '', y = 'GO term', color = 'Module',
size = expression(paste("-log"[10], "(", italic("P"), "-value)"))) +
theme(panel.background = element_rect(color = 'black',
fill = 'transparent'),
panel.grid.major = element_line(colour = 'gray', size = 0.2, linetype = 5),
axis.title = element_text(size = 14, face = 'bold',
color = 'black', family = 'Arial'),
axis.text.y = element_text(size = 12, face = 'bold',
color = 'black', family = 'Arial'),
axis.text.x = element_text(size = 12, face = 'bold',
color = 'black', family = 'Arial',
angle = 45, hjust = 1, vjust = 1),
legend.text = element_text(size = 12, color = 'black', family = 'Arial'),
legend.title = element_text(size = 14, face = 'bold',
color = 'black', family = 'Arial'),
legend.key = element_blank()) +
guides(colour = guide_legend(override.aes = list(size=4)))
ggsave(plot = p, path = path.change,
filename = paste0('GO.png'),
height = 15, width = 24, units = 'cm')