library(data.table)
gLogCpmData = as.matrix(read.table("test_matrix.txt"))
gAnnotationData = read.table("test_annotation.txt")

# 针对不同的药物治疗的样本进行颜色的标注，A blue，B green， else red
mapDrugToColor<-function(annotations){
    colorsVector = ifelse(annotations["subject_drug"]=="MiracleDrugA", 
        "blue", ifelse(annotations["subject_drug"]=="MiracleDrugB", 
        "green", "red"))
    return(colorsVector)
}

# gplots自带的数据集
library(gplots)
data(mtcar)

# 价格态度数据
data("attitude")
corr <- cor(attitude) ## cor计算相关性，然后变成矩阵

# highcharter自带的数据集
install.packages("highcharter")  
library(viridisLite)
data("vaccines")

# 

heatmap(x, Rowv = NULL, Colv =if(symm)"Rowv" else NULL,

        distfun = dist, hclustfun =hclust,

        reorderfun = function(d, w)reorder(d, w),

        add.expr, symm = FALSE, revC =identical(Colv, "Rowv"),

        scale = c("row","column", "none"), na.rm = TRUE,

        margins = c(5, 5), ColSideColors,RowSideColors,

        cexRow = 0.2 + 1/log10(nr),cexCol = 0.2 + 1/log10(nc),

        labRow = NULL, labCol = NULL,main = NULL,

        xlab = NULL, ylab = NULL,
        
        keep.dendro = FALSE, verbose =getOption("verbose"), ...)

testHeatmap <- function(logCPM, annotations) {    
    sampleColors = mapDrugToColor(annotations)
    heatmap(logCPM, margins=c(5,8), ColSideColors=sampleColors)
}

testHeatmap(gLogCpmData, gAnnotationData)

mtcars <- data(mtcars)
df <- as.matrix((scale(mtcars)))#归一化、矩阵化
heatmap(df, scale = "none") ## 初始画图
# 使用自定义颜色
col <- colorRampPalette(c("red", "white", "blue"))(256)
heatmap(df, scale = "none", col=col)
#使用RColorBrewer color palette
library(RColorBrewer)col <- colorRampPalette(brewer.pal(10, "RdYlBu"))(256)#自设置调色板
dim(df) #查看行列数
heatmap(df, scale = "none", col=col, RowSideColors = rep(c("blue", "pink"), each=16), 
ColSideColors = c(rep("purple", 5), rep("orange", 6))) #参数RowSideColors和ColSideColors用于分别注释行和列颜色等

install.packages("gplots")
library(gplots)

testHeatmap2<-function(logCPM, annotations) {    
    sampleColors = mapDrugToColor(annotations)
    heatmap.2(logCPM, margins=c(5,8), ColSideColors=sampleColors,
        key.xlab="log CPM",
        key=TRUE, symkey=FALSE, density.info="none", trace="none")
}

testHeatmap2(gLogCpmData, gAnnotationData)

mtcars <- data(mtcars)
df <- as.matrix((scale(mtcars)))#归一化、矩阵化
heatmap.2(df, scale = "none", col=bluered(100), trace = "none", density.info = "none") 

install.packages("NMF")
library(NMF)

testAheatmap<-function(logCPM, annotations) {    
    aheatmap(logCPM, annCol=annotations[
        "subject_drug"])
}

testAheatmap(gLogCpmData, gAnnotationData)

install.packages("pheatmap")
library(pheatmap)

# 也可以使用数据集
data(all_data)


# 或者生成测试数据
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 = "")

## 对前面的数据进行绘制
testPheatmap<-function(logCPM, annotations) {    
    drug_info = data.frame(annotations[,"subject_drug"])
    rownames(drug_info) = annotations[["sample_name"]]
    
    # 从输入的注释数据进行列注释分配
    pheatmap(logCPM, annotation_col=drug_info, 
        annotation_names_row=FALSE,
        annotation_names_col=FALSE,
        fontsize_col=5)
    
    # 首先将列注释分配给中间变量，以便更改PheatMap的名称用于其图例
    subject_drug = annotations[["subject_drug"]]
    drug_df = data.frame(subject_drug)
    rownames(drug_df) = annotations[["sample_name"]]
    
    pheatmap(logCPM, annotation_col=drug_df, 
        annotation_names_row=FALSE,
        annotation_names_col=FALSE,
        fontsize_col=5)
}
testPheatmap(gLogCpmData, gAnnotationData)

# 行聚为3类
testPheatmap(gLogCpmData, gAnnotationData, kmeans_k = 3)

# 切分热图
testPheatmap(gLogCpmData, gAnnotationData，cluster_rows = F, gaps_row = c(10, 14), cluster_cols = T, cutree_col = 4)

install.packages("heatmap3")
library(heatmap3)

testHeatmap3<-function(logCPM, annotations) {    
    sampleColors = mapDrugToColor(annotations)
    
     # 只分配列注释
    heatmap3(logCPM, margins=c(5,8), ColSideColors=sampleColors) 
    
    # 分配列注释并为它们制作自定义图例
    heatmap3(logCPM, margins=c(5,8), ColSideColors=sampleColors, 
        legendfun=function()showLegend(legend=c("MiracleDrugA", 
        "MiracleDrugB", "?"), col=c("blue", "green", "red"), cex=1.5))
    
    # 将列注释指定为迷你图而不是颜色，并为它们使用内置标签
    ColSideAnn<-data.frame(Drug=annotations[["subject_drug"]])
    heatmap3(logCPM,ColSideAnn=ColSideAnn,
        ColSideFun=function(x)showAnn(x),
        ColSideWidth=0.8)
}
             
testHeatmap3(gLogCpmData, gAnnotationData)

source("http://bioconductor.org/biocLite.R")
biocLite("Heatplus")
library(Heatplus)

testAnnHeatmap2<-function(logCPM, annotations){
    ann.dat = data.frame(annotations[,"subject_drug"])

    plot(annHeatmap2(logCPM, legend=2,
        ann = list(Col = list(data = ann.dat))))
}

testAnnHeatmap2(gLogCpmData, gAnnotationData)

install.packages("lattice")
install.packages("latticeExtra")
library("lattice")
library(latticeExtra)

testlevelplot<-function(logCPM, annotations) { 
  hc=hclust(dist(logCPM)) ###按行聚类
  dd.row=as.dendrogram(hc) ###保存行聚类树形
  row.ord=order.dendrogram(dd.row) ###保存行聚类顺序
  hc=hclust(dist(t(logCPM))) ###按列聚类
  dd.col=as.dendrogram(hc) ###保存列聚类树形
  col.rod=order.dendrogram(dd.col) ###保存列聚类顺序
  logCPM1=logCPM[row.ord,] ###只对行聚类（是否对行、列聚类)
  levelplot(t(logCPM1), aspect = 'fill', colorkey = list(space = 'left', width = 1.5), xlab = '', ylab = '', legend = list(right = list(fun = dendrogramGrob, args = list(x = dd.row, rod = row.ord, side = 'right', size = 5)), scales= list(x = list(rot = 60)) ###x轴标签旋转60度
}

if (!require("devtools")) 
install.packages("devtools") 
devtools::install_github("rstudio/d3heatmap")

library(d3heatmap)
mtcars <- data(mtcars)
df <- as.matrix((scale(mtcars)))#归一化、矩阵化

d3heatmap(df, colors = "RdBu", k_row = 4, k_col = 2) # k_row、k_col分别指定用于对行列中树形图分支进行着色所需组数
# 使用dendextend包增强热图
library(dendextend) 
# order for rows
Rowv <- mtcars %>% scale %>% dist %>% hclust %>% as.dendrogram %>% set("branches_k_color", k = 3) %>% set("branches_lwd", 1.2) %>% ladderize
# Order for columns We must transpose the data
Colv <- mtcars %>% scale %>% t %>% dist %>% hclust %>% as.dendrogram %>% set("branches_k_color", k = 2, value = c("orange", "blue")) %>% set("branches_lwd", 1.2) %>% ladderize
#增强heatmap()函数
heatmap(df, Rowv = Rowv, Colv = Colv, scale = "none")
#增强heatmap.2()函数
heatmap.2(df, scale = "none", col = bluered(100), Rowv = Rowv, Colv = Colv, trace = "none", density.info = "none")
#增强交互式绘图函数
d3heatmap(scale(mtcars), colors = "RdBu", Rowv = Rowv, Colv = Colv)

if (!require("devtools"))
install.packages("devtools") 
devtools::install_github("jokergoo/ComplexHeatmap")

library(ComplexHeatmap)
mtcars <- data(mtcars)
df <- as.matrix((scale(mtcars)))#归一化、矩阵化
Heatmap(df, name = "mtcars")
#自设置颜色
library(circlize)
Heatmap(df, name = "mtcars", col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")))
# 使用调色板
Heatmap(df, name = "mtcars",col = colorRamp2(c(-2, 0, 2), brewer.pal(n=3, name="RdBu")))
#自定义颜色
mycol <- colorRamp2(c(-2, 0, 2), c("blue", "white", "red"))
Heatmap(df, name = "mtcars", col = mycol, column_title = "Column title", row_title = 
"Row title") #热图及行列标题设置
# 行标题的默认位置是“left”，列标题的默认是“top”，可以使用以下选项更改：
  # row_title_side：允许的值为“左”或“右”（例如：row_title_side =“right”）
  # column_title_side：允许的值为“top”或“bottom”（例如：column_title_side =“bottom”） 也可以使用以下选项修改字体和大小：
  # row_title_gp：用于绘制行文本的图形参数
  # column_title_gp：用于绘制列文本的图形参数
  # show_row_names：是否显示行名称。默认值为TRUE
  # show_column_names：是否显示列名称。默认值为TRUE

# 默认情况下，行和列是包含在聚类里的，可以使用参数修改：
  # cluster_rows = FALSE。如果为TRUE，则在行上创建集群
  # cluster_columns = FALSE。如果为TRUE，则将列置于簇上

# 可以使用选项column_dend_height 和 row_dend_width 更改行列聚类的高度或宽度
Heatmap(df, name = "mtcars", col = mycol, column_dend_height = unit(2, "cm"), row_dend_width = unit(2, "cm") )

# 利用color_branches()自定义树状图外观
library(dendextend)
row_dend = hclust(dist(df)) # row clustering
col_dend = hclust(dist(t(df))) # column clustering
Heatmap(df, name = "mtcars", col = mycol, cluster_rows = color_branches(row_dend, k = 4), cluster_columns = color_branches(col_dend, k = 2)) # 行分为4类，列分为2类

# 参数clustering_distance_rows 和 clustering_distance_columns 用于分别指定行和列聚类的度量标准，允许的值有“euclidean”, “maximum”, “manhattan”, “canberra”, “binary”, “minkowski”, “pearson”, “spearman”, “kendall”。
Heatmap(df, name = "mtcars", clustering_distance_rows = "pearson", clustering_distance_columns = "pearson")

#也可以自定义距离计算方式
Heatmap(df, name = "mtcars", clustering_distance_rows = function(m) dist(m))
Heatmap(df, name = "mtcars", clustering_distance_rows = function(x, y) 1 - cor(x, y))

#行聚类clustering_distance_rows，列聚类使用相同的度量标准
# Clustering metric function
robust_dist = function(x, y) { 
    qx = quantile(x, c(0.1, 0.9)) qy = quantile(y, c(0.1, 0.9)) l = x > qx[1] & x < qx[2] & y > qy[1] & y < qy[2] x = x[l] y = y[l] sqrt(sum((x - y)^2))
}
# 画图
Heatmap(df, name = "mtcars", clustering_distance_rows = robust_dist, clustering_distance_columns = robust_dist, col = colorRamp2(c(-2, 0, 2), c("purple", "white", "orange")))

# 参数clustering_method_rows和clustering_method_columns可用于指定进行层次聚类的方法。允许的值是hclust()函数支持的值，包括“ward.D”，“ward.D2”，“single”，“complete”，“average”，…
Heatmap(df, name = "mtcars", clustering_method_rows = "ward.D", clustering_method_columns = "ward.D")

# 热图拆分，应用k-means使用参数km
set.seed(1122)
Heatmap(df, name = "mtcars", col = mycol, km = 2)

#由指定行类的向量分割
Heatmap(df, name = "mtcars", col = mycol, split = mtcars$cyl )
#split也可以是一个数据框，其中不同级别的组合拆分热图的行。
Heatmap(df, name ="mtcars", col = mycol, split = data.frame(cyl = mtcars$cyl, am = mtcars$am))
# Combine km and split
Heatmap(df, name ="mtcars", col = mycol, km = 2, split = mtcars$cyl)
#自定义分割
library("cluster")
set.seed(1122)
pa = pam(df, k = 3)
Heatmap(df, name = "mtcars", col = mycol, split = paste0("pam", pa$clustering))
# 将自定义的树形图和分割相结合
row_dend = hclust(dist(df)) # row clustering
grow_dend = color_branches(row_dend, k = 4)
Heatmap(df, name = "mtcars", col = mycol, cluster_rows = row_dend, split = 2)

# HeatmapAnnotation()对行或列注释
df <- t(df)
Heatmap(df, name ="mtcars", col = mycol)
# Annotation data frame
annot_df <- data.frame(cyl = mtcars$cyl, am = mtcars$am, mpg = mtcars$mpg)
# Define colors for each levels of qualitative variables
# Define gradient color for continuous variable (mpg)
col = list(cyl = c("4" = "green", "6" = "gray", "8" = "darkred"), am = c("0" = "yellow", 
"1" = "orange"), mpg = colorRamp2(c(17, 25), c("lightblue", "purple")) )
# Create the heatmap annotation
ha <- HeatmapAnnotation(annot_df, col = col)
# Combine the heatmap and the annotation
Heatmap(df, name = "mtcars", col = mycol, top_annotation = ha)
#可以使用参数show_legend = FALSE来隐藏注释图例
ha <- HeatmapAnnotation(annot_df, col = col, show_legend = FALSE)
Heatmap(df, name = "mtcars", col = mycol, top_annotation = ha)

#注释名称可以使用下面的R代码添加
library("GetoptLong")
# Combine Heatmap and annotation
ha <- HeatmapAnnotation(annot_df, col = col, show_legend = FALSE)
Heatmap(df, name = "mtcars", col = mycol, top_annotation = ha)
# 在右侧添加注释名称
for(an in colnames(annot_df)) { 
  seekViewport(qq("annotation_@{an}")) 
  grid.text(an, unit(1, "npc") + unit(2, "mm"), 0.5, default.units = "npc", just = "left")
}
#要在左侧添加注释名称，请使用以下代码
# Annotation names on the left
for(an in colnames(annot_df)) { 
  seekViewport(qq("annotation_@{an}")) grid.text(an, unit(1, "npc") - unit(2, "mm"), 0.5, default.units = "npc", just = "left")
}
#复杂注释
# Define some graphics to display the distribution of columns
.hist = anno_histogram(df, gp = gpar(fill = "lightblue"))
.density = anno_density(df, type = "line", gp = gpar(col = "blue"))
ha_mix_top = HeatmapAnnotation(hist = .hist, density = .density)
# Define some graphics to display the distribution of rows
.violin = anno_density(df, type = "violin", gp = gpar(fill = "lightblue"), which = "row")
.boxplot = anno_boxplot(df, which = "row")
ha_mix_right = HeatmapAnnotation(violin = .violin, bxplt = .boxplot, which = "row", 
width = unit(4, "cm"))
# Combine annotation with heatmap
Heatmap(df, name = "mtcars", col = mycol, column_names_gp = gpar(fontsize = 8), top_annotation = ha_mix_top, top_annotation_height = unit(4, "cm")) + ha_mix_right
    
#热图组合
# Heatmap 1
ht1 = Heatmap(df, name = "ht1", col = mycol, km = 2, column_names_gp = gpar(fontsize = 9))
# Heatmap 2
ht2 = Heatmap(df, name = "ht2", col = colorRamp2(c(-2, 0, 2), c("green", "white", "red")), column_names_gp = gpar(fontsize = 9))
# Combine the two heatmaps
ht1 + ht2
        
#可以使用选项width = unit（3，“cm”））来控制热图大小。注意，当组合多个热图时，第一个热图被视为主热图。剩余热图的一些设置根据主热图的设置自动调整。这些设置包括：删除行集群和标题，以及添加拆分等。
draw(ht1 + ht2, 
      # Titles 
     row_title = "Two heatmaps, row title",
     row_title_gp = gpar(col = "red"), 
     column_title = "Two heatmaps, column title", 
     column_title_side = "bottom", 
      # Gap between heatmaps 
     gap = unit(0.5, "cm"))
#可以使用参数show_heatmap_legend = FALSE，show_annotation_legend = FALSE删除图例。 
        
# 使用基因表达矩阵绘制热图
expr = readRDS(paste0(system.file(package = "ComplexHeatmap"), "/extdata/gene_expression.rds"))
mat = as.matrix(expr[, grep("cell", colnames(expr))])
type = gsub("s\\d+_", "", colnames(mat))
ha = HeatmapAnnotation(df = data.frame(type = type))
Heatmap(mat, name = "expression", km = 5, top_annotation = ha, top_annotation_height = unit(4, "mm"), show_row_names = FALSE, show_column_names = FALSE) + Heatmap(expr$length, name = "length", width = unit(5, "mm"), col = colorRamp2(c(0, 100000), c("white", "orange"))) + Heatmap(expr$type, name = "type", width = unit(5, "mm")) + Heatmap(expr$chr, name = "chr", width = unit(5, "mm"), col = rand_color(length(unique(expr$chr))))
        
#可视化矩阵中列的分布
densityHeatmap(df)

library(reshape2)
library(ggplot2)
library(RColorBrewer)

corr <- cor(gLogCpmData)

# 使用三色阶颜色
plot <- function(m){
  ggplot(m, aes(x=Var1,y=Var2,fill=value)) +
    theme(legend.position="none", panel.background = element_blank(), axis.text = element_blank(), axis.ticks = element_blank()) + labs(x=NULL, y=NULL) +
    geom_tile(aes(fill=value)) +
    scale_fill_gradient2("Corr", low = "green", high = "red", mid = "white", midpoint = data("attitude")0.5, limit = c(0,1), space = "Lab") + coord_fixed()
}
plot(corr)

#使用自定义色阶
colors=colorRampPalette(rev(brewer.pal(n = 9, name ="RdYlBu")))(200)
plotn <- function(m){
  ggplot(m, aes(x=Var1,y=Var2,fill=value)) +
    theme(panel.background = element_blank(), axis.text = element_blank(), axis.ticks = element_blank()) + labs(x=NULL, y=NULL) +
    geom_tile(aes(fill=value)) +
    scale_fill_gradientn("Corr", colours = colors, space = "Lab") + coord_fixed()
}
plotn(corr)

#在R中直接输入代码在CRAN上查找安装
install.packages("ggplot2")
install.packages("highcharter")  

#导入包
library(ggplot2) 
library(highcharter) 

#data
data("vaccines")

#将year转换成数值型
vaccines$year <- as.numeric(vaccines$year)

#作图
p <- ggplot(vaccines,aes(x=year,y=state,fill=count))+
  scale_x_continuous(breaks = seq(1928,2003,by=5)) +
  theme_bw()+
  theme(panel.grid.major = element_blank()) + 
  theme(legend.key=element_blank()) +
  geom_raster()

#采用GSEA调色板,灵感来自GSEA GenePattern生成的热图
install.packages("ggsci")
library(ggsci)
p + scale_fill_gsea()

library(cowplot)
xplot <- ggdensity(iris, "Sepal.Length", fill = "Species",
                   palette = "jco")
yplot <- ggdensity(iris, "Sepal.Width", fill = "Species", 
                   palette = "jco") +
  rotate()

# Cleaning the plots
sp <- sp + rremove("legend")
yplot <- yplot + clean_theme() + rremove("legend")
xplot <- xplot + clean_theme() + rremove("legend")
# Arranging the plot using cowplot
plot_grid(xplot, NULL, sp, yplot, ncol = 2, align = "hv", rel_widths = c(2, 1), rel_heights = c(1, 2))

library(cowplot)
p1 <- insert_xaxis_grob(pmain, xdens, grid::unit(.2, "null"), position = "top")
p2 <- insert_yaxis_grob(p1, ydens, grid::unit(.2, "null"), position = "right")
ggdraw(p2)

library(ggplot2)
library(ALL) #可以使用biocLite("ALL")安装该数据包
data("ALL")
library(limma)
eset<-ALL[,ALL$mol.biol %in% c("BCR/ABL","ALL1/AF4")]
f<-factor(as.character(eset$mol.biol))
design<-model.matrix(~f)
fit<-eBayes(lmFit(eset,design)) #对基因芯片数据进行分析，得到差异表达的数据
selected  <- p.adjust(fit$p.value[, 2]) <0.001 
esetSel <- eset[selected,] #选择其中一部分绘制热图
dim(esetSel) #从这尺度上看，数目并不多，但也不少。如果基因数过多，可以分两次做图。
Features  Samples 
      84       47 
library(hgu95av2.db)
data<-exprs(esetSel)
probes<-rownames(data)
symbol<-mget(probes,hgu95av2SYMBOL,ifnotfound=NA)
symbol<-do.call(rbind,symbol)
symbol[is.na(symbol[,1]),1]<-rownames(symbol)[is.na(symbol[,1])]
rownames(data)<-symbol[probes,1] #给每行以基因名替换探针名命名，在绘制热图时直接显示基因名。
heatmap(data,cexRow=0.5)

# 使用heatmap函数top.colors填充生成的热图
color.map <- function(mol.biol) { if (mol.biol=="ALL1/AF4") "#FF0000" else "#0000FF" }
patientcolors <- unlist(lapply(esetSel$mol.bio, color.map))
heatmap(data, col=topo.colors(100), ColSideColors=patientcolors, cexRow=0.5)

# heatmap.plus
library(heatmap.plus)
hc<-hclust(dist(t(data)))
dd.col<-as.dendrogram(hc)
groups <- cutree(hc,k=5)
color.map <- function(mol.biol) { if (mol.biol=="ALL1/AF4") 1 else 2 }
patientcolors <- unlist(lapply(esetSel$mol.bio, color.map))
col.patientcol<-rbind(groups,patientcolors)
mode(col.patientcol)<-"character"
heatmap.plus(data,ColSideColors=t(col.patientcol),cexRow=0.5)

# heatmap.2
library("gplots")
heatmap.2(data, col=redgreen(75), scale="row", ColSideColors=patientcolors, key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)

# heatmap.2修改颜色
heatmap.2(data, col=heat.colors(100), scale="row", ColSideColors=patientcolors,
+ key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)
heatmap.2(data, col=terrain.colors(100), scale="row", ColSideColors=patientcolors,
+ key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)
heatmap.2(data, col=cm.colors(100), scale="row", ColSideColors=patientcolors,
+ key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)
heatmap.2(data, col=redblue(100), scale="row", ColSideColors=patientcolors,
+ key=TRUE, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)
heatmap.2(data, col=colorpanel(100,low="white",high="steelblue"), scale="row", ColSideColors=patientcolors,
+ key=TRUE, keysize=1, symkey=FALSE, density.info="none", trace="none", cexRow=0.5)
                                   
# ggplot2当中的geom_tile
library(ggplot2)
hc<-hclust(dist(data))
rowInd<-hc$order
hc<-hclust(dist(t(data)))
colInd<-hc$order
data.m<-data[rowInd,colInd] #聚类分析的作用是为了色块集中，显示效果好。如果本身就对样品有分组，基因有排序，就可以跳过这一步。
data.m<-apply(data.m,1,rescale) #以行为基准对数据进行变换，使每一行都变成［0,1］之间的数字。变换的方法可以是scale,rescale等等，按照自己的需要来变换。
data.m<-t(data.m) #变换以后转置了。
coln<-colnames(data.m) 
rown<-rownames(data.m) #保存样品及基因名称。因为geom_tile会对它们按坐标重排，所以需要使用数字把它们的序列固定下来。
colnames(data.m)<-1:ncol(data.m)
rownames(data.m)<-1:nrow(data.m)
data.m<-melt(data.m) #转换数据成适合geom_tile使用的形式
head(data.m)
  X1 X2     value
1  1  1 0.1898007
2  2  1 0.6627467
3  3  1 0.5417057
4  4  1 0.4877054
5  5  1 0.5096474
6  6  1 0.2626248
base_size<-12 #设置默认字体大小，依照样品或者基因的多少而微变。
(p <- ggplot(data.m, aes(X2, X1)) + geom_tile(aes(fill = value), #设定横坐标为以前的列，纵坐标为以前的行，填充色为转换后的数据
+ colour = "white") + scale_fill_gradient(low = "white", #设定渐变色的低值为白色，变值为钢蓝色。
+ high = "steelblue"))
p + theme_grey(base_size = base_size) + labs(x = "", #设置xlabel及ylabel为空
+ y = "") + scale_x_continuous(expand = c(0, 0),labels=coln,breaks=1:length(coln)) + #设置x坐标扩展部分为0，刻度为之前的样品名
+ scale_y_continuous(expand = c(0, 0),labels=rown,breaks=1:length(rown)) + opts( #设置y坐标扩展部分为0，刻度为之前的基因名
+ axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size *  #设置坐标字体为基准的0.8倍，贴近坐标对节，x坐标旋转90度，色彩为中灰
+ 0.8, angle = 90, hjust = 0, colour = "grey50"), axis.text.y = theme_text(
+ size = base_size * 0.8, hjust=1, colour="grey50"))
                                 
# gplot2修改颜色，红黄渐变
(p <- ggplot(data.m, aes(X2, X1)) + geom_tile(aes(fill = value),
+ colour = "white") + scale_fill_gradient(low = "yellow",
+ high = "red"))
p + theme_grey(base_size = base_size) + labs(x = "",
+ y = "") + scale_x_continuous(expand = c(0, 0),labels=coln,breaks=1:length(coln)) +
+ scale_y_continuous(expand = c(0, 0),labels=rown,breaks=1:length(rown)) + opts(
+ axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size *
+ 0.8, angle = 90, hjust = 0, colour = "grey50"), axis.text.y = theme_text(
+ size = base_size * 0.8, hjust=1, colour="grey50"))

# 红绿渐变
(p <- ggplot(data.m, aes(X2, X1)) + geom_tile(aes(fill = value),
+ colour = "white") + scale_fill_gradient(low = "green",
+ high = "red"))
p + theme_grey(base_size = base_size) + labs(x = "",
+ y = "") + scale_x_continuous(expand = c(0, 0),labels=coln,breaks=1:length(coln)) +
+ scale_y_continuous(expand = c(0, 0),labels=rown,breaks=1:length(rown)) + opts(
+ axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size *
+ 0.8, angle = 90, hjust = 0, colour = "grey50"), axis.text.y = theme_text(
+ size = base_size * 0.8, hjust=1, colour="grey50"))
                                 
# 绿白渐变
(p <- ggplot(data.m, aes(X2, X1)) + geom_tile(aes(fill = value),
+ colour = "white") + scale_fill_gradient(low = "seagreen",
+ high = "white"))
p + theme_grey(base_size = base_size) + labs(x = "",
+ y = "") + scale_x_continuous(expand = c(0, 0),labels=coln,breaks=1:length(coln)) +
+ scale_y_continuous(expand = c(0, 0),labels=rown,breaks=1:length(rown)) + opts(
+ axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size *
+ 0.8, angle = 90, hjust = 0, colour = "grey50"), axis.text.y = theme_text(
+ size = base_size * 0.8, hjust=1, colour="grey50"))
                                 
# 棕白渐变
(p <- ggplot(data.m, aes(X2, X1)) + geom_tile(aes(fill = value),
+ colour = "white") + scale_fill_gradient(low = "white",
+ high = "sienna4"))
p + theme_grey(base_size = base_size) + labs(x = "",
+ y = "") + scale_x_continuous(expand = c(0, 0),labels=coln,breaks=1:length(coln)) +
+ scale_y_continuous(expand = c(0, 0),labels=rown,breaks=1:length(rown)) + opts(
+ axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size *
+ 0.8, angle = 90, hjust = 0, colour = "grey50"), axis.text.y = theme_text(
+ size = base_size * 0.8, hjust=1, colour="grey50"))      
                                 
# 灰阶填充
(p <- ggplot(data.m, aes(X2, X1)) + geom_tile(aes(fill = value),
+ colour = "white") + scale_fill_gradient(low = "black",
+ high = "gray85"))
p + theme_grey(base_size = base_size) + labs(x = "",
+ y = "") + scale_x_continuous(expand = c(0, 0),labels=coln,breaks=1:length(coln)) +
+ scale_y_continuous(expand = c(0, 0),labels=rown,breaks=1:length(rown)) + opts(
+ axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size *
+ 0.8, angle = 90, hjust = 0, colour = "grey50"), axis.text.y = theme_text(
+ size = base_size * 0.8, hjust=1, colour="grey50"))
                                 
# lattice的levelplot绘制，heat.colors填充，dendrogramGrob绘树型
hc<-hclust(dist(data))
dd.row<-as.dendrogram(hc)
row.ord<-order.dendrogram(dd.row) #介绍另一种获得排序的办法
hc<-hclust(dist(t(data)))
dd.col<-as.dendrogram(hc)
col.ord<-order.dendrogram(dd.col)
data.m<-data[row.ord,col.ord]
library(ggplot2)
data.m<-apply(data.m,1,rescale) #rescale是ggplot2当中的一个函数
library(lattice)
levelplot(data.m,
+           aspect = "fill",xlab="",ylab="",
+           scales = list(x = list(rot = 90, cex=0.8),y=list(cex=0.5)),
+           colorkey = list(space = "left"),col.regions = heat.colors)
library(latticeExtra)
levelplot(data.m,
+           aspect = "fill",xlab="",ylab="",
+           scales = list(x = list(rot = 90, cex=0.5),y=list(cex=0.4)),
+           colorkey = list(space = "left"),col.regions = heat.colors,
+           legend =
+           list(right =
+                list(fun = dendrogramGrob, #dendrogramGrob是latticeExtra中绘制树型图的一个函数
+                     args =
+                     list(x = dd.row, ord = row.ord,
+                          side = "right",
+                          size = 5)),
+                top =
+                list(fun = dendrogramGrob,
+                     args =
+                     list(x = dd.col, 
+                          side = "top",
+                          type = "triangle")))) #使用三角型构图
                                 
# pheatmap 绘制
library(pheatmap)
pheatmap(data,fontsize=9, fontsize_row=6) #最简单地直接出图
pheatmap(data, scale = "row", clustering_distance_row = "correlation", fontsize=9, fontsize_row=6) #改变排序算法
pheatmap(data, color = colorRampPalette(c("navy", "white", "firebrick3"))(50), fontsize=9, fontsize_row=6) #自定义颜色
pheatmap(data, cluster_row=FALSE, fontsize=9, fontsize_row=6) #关闭按行排序
pheatmap(data, legend = FALSE, fontsize=9, fontsize_row=6) #关闭图例
pheatmap(data, cellwidth = 6, cellheight = 5, fontsize=9, fontsize_row=6) #设定格子的尺寸
color.map <- function(mol.biol) { if (mol.biol=="ALL1/AF4") 1 else 2 }
patientcolors <- unlist(lapply(esetSel$mol.bio, color.map))
hc<-hclust(dist(t(data)))
dd.col<-as.dendrogram(hc)
groups <- cutree(hc,k=7)
annotation<-data.frame(Var1=factor(patientcolors,labels=c("class1","class2")),Var2=groups)
pheatmap(data, annotation=annotation, fontsize=9, fontsize_row=6) #为样品分组
Var1 = c("navy", "skyblue")
Var2 = c("snow", "steelblue")
names(Var1) = c("class1", "class2")
ann_colors = list(Var1 = Var1, Var2 = Var2)
pheatmap(data, annotation=annotation, annotation_colors = ann_colors, fontsize=9, fontsize_row=6) #为分组的样品设定颜色