热图中z分数和相应p值的共享图例

Question

我有一个z分数matrix:

set.seed(1)
z.score.mat <- matrix(rnorm(1000),nrow=100,ncol=10)

这是一些生物实验数据的结果,以及相应的p值矩阵:

p.val.mat <- pnorm(abs(z.score.mat),lower.tail = F)

两者都相同dimnames:

rownames(z.score.mat) <- paste("p",1:100,sep="")
colnames(z.score.mat) <- paste("c",1:10,sep="")
rownames(p.val.mat) <- paste("p",1:100,sep="")
colnames(p.val.mat) <- paste("c",1:10,sep="")

我正在绘制heatmap像这样的z分数的分层聚类:

hc.col <- hclust(dist(z.score.mat))
dd.col <- as.dendrogram(hc.col)
col.ord <- order.dendrogram(dd.col)
hc.row <- hclust(dist(t(z.score.mat)))
dd.row <- as.dendrogram(hc.row)
row.ord <- order.dendrogram(dd.row)
clustered.mat <- z.score.mat[col.ord,row.ord]
clustered.mat.names <- attr(clustered.mat,"dimnames")
clustered.mat.df <- as.data.frame(clustered.mat)
colnames(clustered.mat.df) <- clustered.mat.names[[2]]
clustered.mat.df[,"process"] <- clustered.mat.names[[1]]
clustered.mat.df[,"process"] <- with(clustered.mat.df,factor(clustered.mat.df[,"process"],levels=clustered.mat.df[,"process"],ordered=TRUE))

require(reshape2)
clustered.mat.df <- reshape2::melt(clustered.mat.df,id.vars="process")
colnames(clustered.mat.df)[2:3] <- c("condition","z.score")
clustered.mat.df$p.value <- sapply(1:nrow(clustered.mat.df),function(x) p.val.mat[which(rownames(p.val.mat) == clustered.mat.df$process[x]),which(colnames(p.val.mat) == clustered.mat.df$condition[x])])
lab.legend <- colnames(clustered.mat.df)[3]
lab.row <- colnames(clustered.mat.df)[1]
lab.col <- colnames(clustered.mat.df)[2]

require(ggplot2)
ggplot(clustered.mat.df,aes(x=condition,y=process))+
  geom_tile(aes(fill=z.score))+
  scale_fill_gradient2(lab.legend,high="darkred",low="darkblue")+
  theme_bw()+
  theme(legend.key=element_blank(),
        legend.position="right",
        panel.border=element_blank(),
        strip.background=element_blank(),
        axis.text.x=element_text(angle=45,vjust=0.5)
  )

我的问题是,如果有可能,如何在图例栏的一侧有z得分范围(目前在右边),另一边有相应的p值范围？

Answer 1

当绘图尺寸发生变化时,这非常繁琐,但您确实得到了所需的结果:

br <- seq(-3, 3, 1)
lab <- round(pnorm(abs(br),lower.tail = F), 3)

p <- ggplot(clustered.mat.df,aes(x=condition,y=process))+
  geom_tile(aes(fill=z.score), show.legend = FALSE)+
  scale_fill_gradient2(lab.legend, high="darkred", low="darkblue", breaks = br)

p1 <- ggplot(clustered.mat.df,aes(x=condition,y=process))+
  geom_tile(aes(fill=z.score))+
  scale_fill_gradient2(lab.legend, high="darkred", low="darkblue", breaks = br) +
  guides(fill = guide_colorbar(title = '', label.position = 'right', barheight = 10))

p2 <- ggplot(clustered.mat.df,aes(x=condition,y=process))+
  geom_tile(aes(fill=z.score))+
  scale_fill_gradient2(lab.legend, high="darkred", low="darkblue", breaks = br, labels = lab) +
  guides(fill = guide_colorbar('', label.position = 'left', barheight = 10))

library(cowplot)
l1 <- get_legend(p1)
l2 <- get_legend(p2)

ggdraw() + 
  draw_plot(p, width = 0.85) +
  draw_grob(l1, 0.89, 0, 0.1, 1) +
  draw_grob(l2, 0.85, 0, 0.1, 1) +
  draw_label('p         z', 0.88, 0.675, hjust = 0)

Answer 2

这种方法使用gtable和grid功能.它从您的绘图中获取图例,编辑图例以使p值显示在左侧,然后将编辑的图例放回到绘图中.

# Your data
set.seed(1)
z.score.mat <- matrix(rnorm(1000),nrow=100,ncol=10)
# which are the result of some biological experimental data, and a corresponding p-value matrix:

p.val.mat <- pnorm(abs(z.score.mat),lower.tail = F)

rownames(z.score.mat) <- paste("p",1:100,sep="")
colnames(z.score.mat) <- paste("c",1:10,sep="")
rownames(p.val.mat) <- paste("p",1:100,sep="")
colnames(p.val.mat) <- paste("c",1:10,sep="")

hc.col <- hclust(dist(z.score.mat))
dd.col <- as.dendrogram(hc.col)
col.ord <- order.dendrogram(dd.col)
hc.row <- hclust(dist(t(z.score.mat)))
dd.row <- as.dendrogram(hc.row)
row.ord <- order.dendrogram(dd.row)
clustered.mat <- z.score.mat[col.ord,row.ord]
clustered.mat.names <- attr(clustered.mat,"dimnames")
clustered.mat.df <- as.data.frame(clustered.mat)
colnames(clustered.mat.df) <- clustered.mat.names[[2]]
clustered.mat.df[,"process"] <- clustered.mat.names[[1]]
clustered.mat.df[,"process"] <- with(clustered.mat.df,factor(clustered.mat.df[,"process"],levels=clustered.mat.df[,"process"],ordered=TRUE))

require(reshape2)
clustered.mat.df <- reshape2::melt(clustered.mat.df,id.vars="process")
colnames(clustered.mat.df)[2:3] <- c("condition","z.score")
clustered.mat.df$p.value <- sapply(1:nrow(clustered.mat.df),function(x) p.val.mat[which(rownames(p.val.mat) == clustered.mat.df$process[x]),which(colnames(p.val.mat) == clustered.mat.df$condition[x])])
lab.legend <- colnames(clustered.mat.df)[3]
lab.row <- colnames(clustered.mat.df)[1]
lab.col <- colnames(clustered.mat.df)[2]

# Your plot
require(ggplot2)
p = ggplot(clustered.mat.df,aes(x=condition,y=process))+
  geom_tile(aes(fill=z.score))+
  scale_fill_gradient2(lab.legend,high="darkred",low="darkblue") +
  theme_bw()+
  theme(legend.key=element_blank(),
        legend.position="right",
        panel.border=element_blank(),
        strip.background=element_blank(),
        axis.text.x=element_text(angle=45,vjust=0.5))


library(gtable)
library(grid)
# Get the ggplot grob
g = ggplotGrob(p)

# Get the legend
index = which(g$layout$name == "guide-box")
leg = g$grobs[[index]] 

# Get the legend labels 
# and calculate corresponding p values
z.breaks = as.numeric(leg$grobs[[1]]$grobs[[3]]$label)
p.breaks = as.character(round(pnorm(abs(z.breaks), lower.tail = F), 3))

# Get the width of the longest p.break string, taking account of font and font size
w = lapply(na.omit(p.breaks), function(x) grobWidth(textGrob(x, 
             gp = gpar(fontsize = leg$grobs[[1]]$grobs[[3]]$gp$fontsize,
                       fontfamily = leg$grobs[[1]]$grobs[[3]]$gp$fontfamily))))
w = do.call(unit.pmax, w)
w = convertX(w, "mm")

# Add columns to the legend gtable to take p.breaks, 
# setting the width of relevant column to w
leg$grobs[[1]] = gtable_add_cols(leg$grobs[[1]], leg$grobs[[1]]$widths[3], 1)
leg$grobs[[1]] = gtable_add_cols(leg$grobs[[1]], w, 1)

# Construct grob containing p.breaks
# Begin with the z.score grob, then make relevant changes
p.values = leg$grobs[[1]]$grobs[[3]]
p.values[c("label", "x", "hjust")] = list(p.breaks, unit(1, "npc"), 1)

# Put the p.values grob into the legend gtable
leg$grobs[[1]] = gtable_add_grob(leg$grobs[[1]], p.values, t=4, l=2, 
                        name = "p.values", clip = "off")

# Put 'p' and 'z' labels into the legend gtable
leg$grobs[[1]] = gtable_add_grob(leg$grobs[[1]], list(textGrob("p"), textGrob("z")), 
                        t=2, l=c(2,6), clip = "off")

# Drop the current legend title
leg$grobs[[1]]$grobs[[4]] = nullGrob()

# Put the legend back into the plot,
# and make sure the relevant column is wide enough to take the new legend
g$grobs[[index]] = leg
g$widths[8] = g$widths[8] + sum(leg$grobs[[1]]$widths[2:3])

# Draw the plot
grid.newpage()
grid.draw(g)

Answer 3

不完全是您描述的内容,但您可以将p值和z值放在图例一侧的相同标签中:

z.breaks = c(-2,0,2)
p.breaks = pnorm(abs(z.breaks),lower.tail = F)

ggplot(clustered.mat.df,aes(x=condition,y=process)) +
  geom_tile(aes(fill = z.score)) +
  scale_fill_gradient2("z score (p value)", high="darkred",low="darkblue", 
                       breaks = z.breaks, 
                       labels = paste0(z.breaks, ' (p = ', round(p.breaks,2), ')')  ) +
  theme_bw() +
  theme(legend.key = element_blank(),
        legend.position = 'right',
        panel.border = element_blank(),
        strip.background = element_blank(),
        axis.text.x=element_text(angle=45,vjust=0.5))