R-聚类热图
2024-04-11 本文已影响0人
花生学生信
临床信息聚类热图
基础版
OG聚类热图
setwd("D:\\data\\output\\NLR_add\\Orthogroups")
#library(tidyverse)
library(ComplexHeatmap)
library(pheatmap)
library(circlize)
ml <- df #画图的ssGSEA数据
ml <- as.data.frame(t(apply(ml, 2, scale))) #scale标化
colnames(ml) <- rownames(ml)
col_fun <- colorRamp2(c(-5, 0, 5), c("#377EB8", "white", "#E41A1C"))
#获取聚类分组信息
h1 <- Heatmap(ml, cluster_rows = TRUE, cluster_columns = TRUE, clustering_method_columns = "ward.D2",show_row_names = TRUE, show_column_names = FALSE,
clustering_distance_columns = "euclidean",
clustering_distance_rows = "euclidean",
clustering_method_rows = "ward.D2")
tree <- column_dend(h1)
ind <- cutree(as.hclust(tree), k = 2)[order.dendrogram(tree)] #此处划分为2类，根据你自己的数据调整
table(ind)
ml$Immune_infiltration <- ind[ml$barcode]
pdf("1heatmap.pdf",h=20,w=15)
draw(h1)
dev.off()
数据格式
进阶版
heatmapinput$Immune_infiltration <- str_replace(heatmapinput$Immune_infiltration, "1", "Low infiltration")
heatmapinput$Immune_infiltration <- str_replace(heatmapinput$Immune_infiltration, "2", "High infiltration")
#临床信息注释
Immune_infiltration <- heatmapinput[, "Immune_infiltration"]
Tumor_site <- heatmapinput[, "tumor_site"]
TP53_mutation <- heatmapinput[, "TP53mut"]
KRAS_mutation <- heatmapinput[, "KRASmut"]
BRAF_mutation <- heatmapinput[, "BRAFmut"]
EGFR_mutation <- heatmapinput[, "EGFRmut"]
Gender <- heatmapinput[, "gender"]
MSI <- heatmapinput[, "MSI"]
Polyps <- heatmapinput[, "colon_polyps_present"]
Survival <- heatmapinput[, "vital_status"] #这种生存信息是没有什么价值的，建议用生存状态加时间，详细画法可以参考complexheatmap
Anatomic_location <- heatmapinput[, "anatomic_neoplasm_subdivision"]
Stage <- heatmapinput[, "stage_event_pathologic_stage"]
ha = HeatmapAnnotation(Immune_infiltration =Immune_infiltration, Tumor_site = Tumor_site,
TP53_mutation = TP53_mutation, KRAS_mutation = KRAS_mutation,
BRAF_mutation = BRAF_mutation, EGFR_mutation = EGFR_mutation,
Gender = Gender, MSI = MSI, Polyps = Polyps,
Survival = Survival, Anatomic_location = Anatomic_location,
Stage = Stage, show_annotation_name = FALSE,
col = list(Immune_infiltration = c("High infiltration" = "#3FA538",
"Low infiltration" = "#9FD29BFF"),
Tumor_site = c("left" = "#E00115", "right" = "#5E84B6"),
TP53_mutation = c("mutant" = "black", "wildtype" = "grey"),
KRAS_mutation = c("mutant" = "black", "wildtype" = "grey"),
BRAF_mutation = c("mutant" = "black", "wildtype" = "grey"),
EGFR_mutation = c("mutant" = "black", "wildtype" = "grey"),
Gender = c("MALE" = "#C4868E", "FEMALE" = "#97A8C7"),
MSI = c("MSI-H" = "#E5554D", "MSI-L" = "#C4868E",
"MSS" = "#AEB6CE", "Indeterminate" = "#2B3D44"),
Polyps = c("YES" = "black", "NO" = "grey"),
Survival = c("Alive" = "#3FA538", "Dead" = "#E00115"),
Stage = c("Stage I" = "#B0B0FFFF", "Stage IA" = "#6060FFFF", "Stage II" = "#B0FFB0FF" ,
"Stage IIA" ="#95FF95FF", "Stage IIB" = "#7AFF7AFF", "Stage IIC" = "#60FF60FF",
"Stage III" = "#F7E897FF", "Stage IIIA" = "#F9EF64FF",
"Stage IIIB" = "#FCF732FF", "Stage IIIC" = "#FFFF00FF",
"Stage IV" = "#FF6060FF", "Stage IVA"="#FF3030FF" ,
"Stage IVB" = "#FF0000FF")),
na_col = "white", #各个临床信息的颜色，Anatomic_location颜色输入也同其他，更好颜色搭配信息请参考FigureYa28color
show_legend = rep(TRUE, 12),#是否要显示annotation legend
annotation_height = unit(rep(5, 12), "mm"),#临床annotation的高度
annotation_legend_param = list(
Immune_infiltration = list(title = "Immune infiltration"),
Tumor_site = list(title = "Tumor site"),
TP53_mutation = list(title = "TP53 mutation"),
KRAS_mutation = list(title = "KRAS mutation"),
BRAF_mutation = list(title = "BRAF mutation"),
EGFR_mutation = list(title = "EGFR mutation"),
Gender = list(title = "Gender"),
MSI = list(title = "MSI"),
Polyps = list(title = "Polyps"),
Survival = list(title = "Survival"),
Anatomic_location = list(title = "Anatomic location"),
Stage = list(title = "Stage"))#annotation legend的标签
)
ht <- Heatmap(ml, col = col_fun,
name = "CRC ssGSEA",
cluster_rows = TRUE, cluster_columns = TRUE,
show_row_names = TRUE, show_column_names = FALSE,
bottom_annotation = ha, column_title = qq("TCGA CRC samples (n = @{ncol(ml)})"),
clustering_method_columns = "ward.D2",
clustering_distance_columns = "euclidean",
clustering_distance_rows = "euclidean",
clustering_method_rows = "ward.D2", column_dend_height = unit(30, "mm")
)
pdf("2ssGSEA.pdf", 16, 12)
draw(ht, annotation_legend_side = "left", heatmap_legend_side = "left")
#装饰heatmap
annotation_titles <- c(Immune_infiltration = "Immune infiltration",
Tumor_site = "Tumor site",
TP53_mutation = "TP53 mutation",
KRAS_mutation = "KRAS mutation",
BRAF_mutation = "BRAF mutation",
EGFR_mutation = "EGFR mutation",
Gender = "Gender",
MSI = "MSI",
Polyps = "Polyps",
Survival = "Survival",
Anatomic_location = "Anatomic location",
Stage = "Stage")
for(an in names(annotation_titles)) {
decorate_annotation(an, {
grid.text(annotation_titles[an], unit(-2, "mm"), just = "right")#对齐方向是右边
grid.rect(gp = gpar(fill = NA, col = "black"))
})
}
#分组划线：具体数值要参考聚类的信息
decorate_heatmap_body("CRC ssGSEA", {
grid.lines(c(0, 0), c(0, 1), gp = gpar(lty = 1, lwd = 2))
grid.lines(c(table(ind)[[1]]/sum(table(ind)), table(ind)[[1]]/sum(table(ind))),
gp = gpar(lty = 2, lwd = 2))
grid.lines(c(1, 1), c(0, 1), gp = gpar(lty = 1, lwd = 2))
})
#在heatmap上注释文字信息
decorate_heatmap_body("CRC ssGSEA", {
grid.text("High infiltration", (table(ind)[[1]]/2)/sum(table(ind)), 0.1, #文字所放位置的x和y，根据自己的数据调整
default.units = "npc", gp = gpar(fontsize = 16))
grid.text("Low infiltration", (table(ind)[[1]] + table(ind)[[2]]/2)/sum(table(ind)), 0.1, default.units = "npc", gp = gpar(fontsize = 16))
})
dev.off()
