热图绘制-pheatmap
原创
热图绘制-pheatmap
概述
新买的蓝牙耳机到了，试了试感觉还不错，低音也非常出色，窗外的颜色变得丰富了起来，看着街角那家咖啡店，仿佛回到了昨天，血色染红的天空在斑斓的世界之上，我匆匆茫茫的写下“这把火在我心底永远不会熄灭”。
代码
安装和调用
install.packages("pheatmap")
library(pheatmap)
# 建立测试数据集
test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
# 重命名列和行，列为名，行位基因
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")
# 结果为20行10列的数据集
# 绘图
pheatmap(test)
# 进行聚合，聚为2
pheatmap(test, kmeans_k = 2)
# 是否进行标准化，距离的选择
pheatmap(test, scale = "row", clustering_distance_rows = "correlation")
# 颜色调试
pheatmap(test, color = colorRampPalette(c("navy", "white", "firebrick3"))(50))
# 是否对行进行聚类
pheatmap(test, cluster_row = FALSE)
# 是否显示图例
pheatmap(test, legend = FALSE)
# cells中显示数值
pheatmap(test, display_numbers = TRUE)
# 数字的格式
pheatmap(test, display_numbers = TRUE, number_format = "\ %.1e")
# 对于大于5的cells加星号
pheatmap(test, display_numbers = matrix(ifelse(test > 5, "*", ""), nrow(test)))
后面涉及一些微小的改变，就不粘贴图片了，有兴趣可以粘贴代码去试试
# 对于图例进行调整
pheatmap(test, cluster_row = FALSE, legend_breaks = -1:4, legend_labels = c("0","1e-4", "1e-3", "1e-2", "1e-1", "1"))
# 建立注释集
annotation_col = data.frame(
CellType = factor(rep(c("CT1", "CT2"), 5)),
Time = 1:5)
rownames(annotation_col) = paste("Test", 1:10, sep = "")
annotation_row = data.frame(
GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6))))
rownames(annotation_row) = paste("Gene", 1:20, sep = "")
annotation_row
# 显示行和颜色注释
pheatmap(test, annotation_col = annotation_col)
# 去掉注释图例
pheatmap(test, annotation_col = annotation_col, annotation_legend = FALSE)
# 注释行
pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row)
# 更改列中字符的角度
pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row, angle_col = "45")
# 更改列角度为0
pheatmap(test, annotation_col = annotation_col, angle_col = "0")
# 建立颜色数据集
ann_colors = list(
Time = c("white", "firebrick"),
CellType = c(CT1 = "#1B9E77", CT2 = "#D95F02"),
GeneClass = c(Path1 = "#7570B3", Path2 = "#E7298A", Path3 = "#66A61E"))
# 注释颜色
pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors, main = "Title")
pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row,annotation_colors = ann_colors)
pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors[2])
# 增加一个gap
pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14))
pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14),cutree_col = 2)
# 自定义行名
labels_row = c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "","", "", "Il10", "Il15", "Il1b")
pheatmap(test, annotation_col = annotation_col, labels_row = labels_row)
# 指定聚类的方法
drows = dist(test, method = "minkowski")
dcols = dist(t(test), method = "minkowski")
pheatmap(test, clustering_distance_rows = drows, clustering_distance_cols = dcols)结束语
每一个pheatmap函数都可生成一个图片，合适自己的才是最好的
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
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