Pheatmap

Pheatmap

Quick Start

Create a matrix

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
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")

## Check the matrix
head(test)

         Test1       Test2    Test3       Test4    Test5      Test6    Test7      Test8    Test9     Test10
Gene1 3.113742  0.61097912 2.256865 -0.08655622 2.824332 -0.3824264 2.826347 -0.8726526 2.264322  1.9392831
Gene2 0.973717  0.03505342 2.681469  0.14051028 4.208013 -0.9295952 2.699840 -0.8473697 2.077007  0.2552222
Gene3 3.068895 -1.40479792 1.695945 -1.02215630 2.410066 -1.0977577 1.115832  2.4075234 3.305659 -0.4305648
Gene4 1.739518  0.43744627 3.438071  0.88019288 2.531289  0.5330686 3.914910 -1.5281725 3.853844  0.2501110
Gene5 3.500457 -0.76045729 3.161374 -1.09123751 3.486412 -2.8363874 3.362371 -1.3717895 3.585826  1.4036547
Gene6 3.385101 -0.45133918 3.537412 -2.23503436 1.783411  0.9475567 3.016503  0.4012510 1.886079  0.2387753

library(pheatmap )
## Draw heatmaps
pheatmap(t(test))

Arguments

Quick View:

pheatmap(mat, color = colorRampPalette(rev(brewer.pal(n = 7, name =
       "RdYlBu")))(100), kmeans_k = NA, breaks = NA, border_color = "grey60",
       cellwidth = NA, cellheight = NA, scale = "none", cluster_rows = TRUE,
       cluster_cols = TRUE, clustering_distance_rows = "euclidean",
       clustering_distance_cols = "euclidean", clustering_method = "complete",
       clustering_callback = identity2, cutree_rows = NA, cutree_cols = NA,
       treeheight_row = ifelse((class(cluster_rows) == "hclust") || cluster_rows,
       50, 0), treeheight_col = ifelse((class(cluster_cols) == "hclust") ||
       cluster_cols, 50, 0), legend = TRUE, legend_breaks = NA,
       legend_labels = NA, annotation_row = NA, annotation_col = NA,
       annotation = NA, annotation_colors = NA, annotation_legend = TRUE,
       annotation_names_row = TRUE, annotation_names_col = TRUE,
       drop_levels = TRUE, show_rownames = T, show_colnames = T, main = NA,
       fontsize = 10, fontsize_row = fontsize, fontsize_col = fontsize,
       angle_col = c("270", "0", "45", "90", "315"), display_numbers = F,
       number_format = "%.2f", number_color = "grey30", fontsize_number = 0.8
       * fontsize, gaps_row = NULL, gaps_col = NULL, labels_row = NULL,
       labels_col = NULL, filename = NA, width = NA, height = NA,
       silent = FALSE, na_col = "#DDDDDD", ...)

Color

pheatmap(test, color =
          colorRampPalette(c("red", "white", "black"))(50))

Dendrogram

cluster_rows = TRUE,
cluster_cols = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
clustering_method = "complete",
clustering_callback = identity2,
cutree_rows = NA,
cutree_cols = NA,
treeheight_row = ifelse((class(cluster_rows) == "hclust") || cluster_rows,50, 0),
treeheight_col = ifelse((class(cluster_cols) == "hclust") || cluster_cols, 50, 0),

Disable dendrogram

pheatmap(test, cluster_rows = F, cluster_cols = F)

Cut trees

the parameters:
cutree_rows=int and cutree_cols=int

for example:

pheatmap(test, cutree_rows = 3, cutree_cols = 2)

Group Annotation

In pheatmap, we use a matrix to store the annotaiton tags. An example you can see at below is Group which its rownames inhereted from colnames of test

Parameter about annotation

annotation_row = NA,
annotation_col = NA,
annotation = NA,
annotation_colors = NA,
annotation_legend = TRUE,
annotation_names_row = TRUE,
annotation_names_col = TRUE,

Example:

##  Group annotation
Group = rep(c("A","B"),5)
Group = data.frame(Group)
rownames(Group) = colnames(test)

## Check the Group
head(Group)

Group
Test1      A
Test2      B
Test3      A
Test4      B
Test5      A

pheatmap(test, annotation_col=Group)
## img left

## assign the colors
colors=list(Group=c(A="red", B="black"))
pheatmap(test, annotation_col=Group,
         annotation_colors=colors)
## img right

Two or more Layers of annotation

## Annotation 1
Group_2 = c(rep("A",10),rep("B",10))
Group_2 = data.frame(Group_2)
rownames(Group_2) = rownames(test)

## Annotation 2
Group_2$Phy = c(rep("甲",10),rep("乙",4),rep("丙",6))

## Check the annotation matrix
head(Group_2)

      Group_2 Phy
Gene1       A  甲
Gene2       A  甲
Gene3       A  甲
Gene4       A  甲
Gene5       A  甲
Gene6       A  甲

Display Numbers or Characters

display_numbers is the parameter we’d like to add.

pheatmap(test, display_numbers = T)
## img at left

Except for numbers, we can also adding characters,
for example:

## make a new matrix with symbol or characters
TB_mark <- test
TB_mark[which(test>=5)] = "★"
TB_mark[which(test<5)] = ""
TB_mark[which(test<=0)] = "☆"

pheatmap(test, display_numbers = TB_mark)
## img at right

Labels Annotation

labels_row = c("what a find day", "", "", "",
                "", "", "", "", "", "", "", "",
                "", "", "","", "", "Il10",
                "Il15", "Il1b")
## labels_row参数添加行标签
pheatmap(test, labels_row = labels_row)

Turn to ggplot

install.packages('ggplotify')
library(ggplotify)

d <- matrix(rnorm(100), ncol=10)
library(pheatmap)
p <- pheatmap(d)
g = as.ggplot(p)

Heatmap for DEGs matrix

reference: Trinity

primary_data = read.table("diffExpr.P1e-5_C2.matrix", header=T, com='', row.names=1, check.names=F, sep='\t')

primary_data = as.matrix(primary_data)

##transformations
data = log2(primary_data+1)
data = as.matrix(data) # convert to matrix
## Centering rows
data = data.frame(t(scale(t(data), scale=F)))

pheatmap(data, scale = "row", clustering_distance_row = "correlation", fontsize=9, fontsize_row=6) #改变排序算法

annotation<-data.frame(Var1=factor(patientcolors,labels=c("class1","class2")),Var2=groups)

pheatmap(data, annotation=annotation, fontsize=9, fontsize_row=6)

geom_texttmap(data, cluster_row=FALSE, fontsize=9, fontsize_row=6) #关闭按行排序(aes(label = B, vjust = 1.1, hjust = -0.5, angle = 45), show_guide = FALSE)

Parameters

mat：用来画热图的数据参数，一般是一个矩阵，数据是基因表达值，行代表基因，列代表样本。
color：表示颜色，用来画热图的颜色，可以自己定义，默认值为colorRampPalette(rev(brewer.pal(n = 7, name =”RdYlBu”)))(100)，RdYlBu也就是Rd红色，Yi黄色，Bu蓝色的过度，则主调色为红黄蓝。
scale：是指对数值进行均一化处理，在基因表达量的数据中，有些基因表达量极低，有些基因表达量极高，因此把每个基因在不同处理和重复中的数据转换为平均值为0，方差为1的数据，可以看出每个基因在某个处理和重复中表达量是高还是低，一般选择做row均一化。
clustering_method：表示聚类方法，值可以是hclust的任何一种，如”ward.D”,”single”, “complete”, “average”, “mcquitty”, “median”, “centroid”, “ward.D2″。
cluster_rows：表示行是否聚类，值可以是FALSE或TRUE
clustering_distance_rows：行距离度量的方法，如欧氏距离
cutree_rows：行聚类数
treeheight_row：行聚类树的高度，默认为50
gaps_row：对行进行分割，就不应对相应的行进行聚类
cluster_cols：表示列是否聚类，值可以是FALSE或TRUE
clustering_distance_cols：列距离度量的方法
cutree_cols：列聚类数
treeheight_col：列聚类树的高度，默认为50
gaps_col：对列进行分割，就不应对相应的列进行聚类
legend：逻辑值，是否显示色度条，默认为T
legend_breaks：显示多少个颜色数值段
legend_labels：对色度条上对应位置的字符进行修改
annotation_colors：对标签的颜色进行修改
annotation_legend：是否显示标签注释条
annotation_row：数据框格式，用来定义热图所在行的注释条
annotation_names_row：逻辑值，是否显示行标签名称
annotation_col：数据框格式，用来定义热图所在列的注释条
annotation_names_col：逻辑值，是否显示列标签名称
main：设置图的标题
fontsize：是设置所有除主图以外的标签的大小
number_color：字体的颜色
show_rownames：是否显示行名
fontsize_row：行名的字体大小
labels_row：X轴坐标名设置
show_colnames：是否显示列名
fontsize_col：列名的字体大小
labels_col：y轴坐标名设置
fontsize_number：小格子中数字大小
display_numbers：逻辑值，是否在小格子中显示数字
number_format：小格子中数字显示形式，但仅有在display_numbers=T时才能使用
na_col：设置小格子为缺失值时的颜色
cellwidth：表示每个小格子的宽度
cellheight：表示每个小格子的高度
filename：输出图画的文件名
width：输出图画的宽度
height：输出图画的高度

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Reference: Davey1220 2018

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