Question

我一直在努力定位heatmap.2输出的组成部分。

I found this old answer解释了@IanSudbery中元素定位的工作原理，这看起来似乎很清楚，我认为它已经使我获得了所需的理解，但我仍然没有掌握一些东西。

我知道这些元素基本上都放在一个窗口的格子中，但是它们的行为方式并不符合我的理解。

这是我的代码和当前输出（在最底部是对图形元素进行排序的感兴趣的位）：

for(i in 1:length(ConditionsAbbr)) {

# creates its own colour palette 
    my_palette <- colorRampPalette(c("snow", "yellow", "darkorange", "red"))(n = 399)

  # (optional) defines the colour breaks manually for a "skewed" colour transition
    col_breaks = c(seq(0,0.09,length=100),    #white 'snow'
                 seq(0.1,0.19,length=100), # for yellow
                 seq(0.2,0.29,length=100), # for orange 'darkorange'
                 seq(0.3,1,length=100))    # for red

  # creates a 5 x 5 inch image
  png(paste(SourceDir, "Heatmap_", ConditionsAbbr[i], "XYZ.png"),    # create PNG for the heat map        
      width = 5*600,        # 5 x 600 pixels
      height = 5*600,
      res = 300,            # 300 pixels per inch
      pointsize = 8)        # smaller font size

  heatmap.2(ConditionsMtx[[ConditionsAbbr[i]]],
            cellnote = ConditionsMtx[[ConditionsAbbr[i]]],  # same data set for cell labels
            main =  paste(ConditionsAbbr[i], "XYZ"), # heat map title
            notecol="black",      # change font color of cell labels to black
            density.info="none",  # turns off density plot inside color legend
            trace="none",         # turns off trace lines inside the heat map
            margins =c(12,9),     # widens margins around plot
            col=my_palette,       # use on color palette defined earlier
            breaks=col_breaks,    # enable color transition at specified limits
            dendrogram="none",     # No dendogram
            srtCol = 0 ,        #correct angle of label numbers
            asp = 1 ,         #this overrides layout methinks and for some reason makes it square
            adjCol = c(NA, -35) ,
            adjRow = c(53, NA) ,
            keysize =  1.2 ,
            Colv = FALSE ,      #turn off column clustering
            Rowv =  FALSE ,    # turn off row clustering
            key.xlab = paste("Correlation") ,
            lmat = rbind( c(0, 3), c(2,1), c(0,4) ), 
            lhei = c(0.9, 4, 0.5) )

  dev.off()               # close the PNG device


}

这给出了：

如您所见，键在矩阵的右边，矩阵，上方的标题和下方的键之间有大量的空白，而且标题和矩阵不在PNG的中心吗？

我认为自己“好吧，我将创建一个易于理解和编辑的3x3”，例如

        |        |
        |        |  (3)
        |        |      
--------------------------
        |  (1)   |
   (2)  | Matrix |  
        |        | 
--------------------------   
        |  (4)   |
        |  Key   |
        |        |

然后我可以摆脱空白，所以它更像这样。

        |        |(3)      
    ------------------
        |  (1)   |
     (2)| Matrix |  
        |        | 
    ------------------   
        |(4) Key |

我这样做是：

lmat = rbind( c(0, 0, 3), c(2, 1, 0), c(0, 4, 0) ), 
            lhei = c(0.9, 4, 0.5) ,
            lwid = c(1, 4, 1))

它是这样的：

尽管能看到我的矩阵在中间，但我的钥匙仍然对准矩阵的右侧，而我的头衔是向东丝绸之路？更不用说所有多余的空白了？

如何使它们对齐并一起移动，以使图形部件紧密地配合在一起？

编辑：减少我的边距有助于减少空白，但是它仍然过多。

Answer 1

我不知道您是否愿意接受非基于heatmap.2的解决方案。我认为ggplot提供了更大的灵活性，并且进行了一些调整，您可以舒适地重现与您正在显示的热图类似的热图，同时最大限度地绘制“房地产”并避免过多的空白。

如果您只想寻找heatmap.2解决方案，我很乐意删除此帖子。

此外，ggplot2解决方案可能看起来像这样：

首先，让我们生成一些示例数据

set.seed(2018)
df <- as_tibble(matrix(runif(7*10), ncol = 10), .name_repair = ~seq(1:10))

在绘制之前，我们需要将df的形状从宽变长

library(tidyverse)
df <- df %>%
    rowid_to_column("row") %>% 
    gather(col, Correlation, -row) %>%
    mutate(col = as.integer(col))

然后绘图

 ggplot(df, aes(row, col, fill = Correlation)) +
    geom_tile() +
    scale_fill_gradientn(colours = my_palette) +     # Use your custom colour palette
    theme_void() +                                   # Minimal theme
    labs(title = "Main title") +
    geom_text(aes(label = sprintf("%2.1f", Correlation)), size = 2) +
    theme(
        plot.title = element_text(hjust = 1),        # Right-aligned text
        legend.position="bottom") +                  # Legend at the bottom
    guides(fill = guide_colourbar(
        title.position = "bottom",                   # Legend title below bar
        barwidth = 25,                               # Extend bar length
        title.hjust = 0.5))

通过`facet_wrap`

在网格布局中具有多个热图的示例

首先，让我们生成更复杂的数据。

set.seed(2018)
df <- replicate(
    4,
    as_tibble(matrix(runif(7*10), ncol = 10), .name_repair = ~seq(1:10)), simplify = F) %>%
    setNames(., paste("data", 1:4, sep = "")) %>%
    map(~ .x %>% rowid_to_column("row") %>%
        gather(col, Correlation, -row) %>%
        mutate(col = as.integer(col))) %>%
    bind_rows(.id = "data")

然后绘图与我们之前所做的相同，外加一个附加的facet_wrap(~data, ncol = 2)语句

ggplot(df, aes(row, col, fill = Correlation)) +
    geom_tile() +
    scale_fill_gradientn(colours = my_palette) +     # Use your custom colour palette
    theme_void() +                                   # Minimal theme
    labs(title = "Main title") +
    geom_text(aes(label = sprintf("%2.1f", Correlation)), size = 2) +
    facet_wrap(~ data, ncol = 2) + 
    theme(
        plot.title = element_text(hjust = 1),        # Right-aligned text
        legend.position="bottom") +                  # Legend at the bottom
    guides(fill = guide_colourbar(
        title.position = "bottom",                   # Legend title below bar
        barwidth = 25,                               # Extend bar length
        title.hjust = 0.5))

最后一次更新

我认为很有趣/有趣的一点是，我们可以达到与您的link to from the paper类似的复杂热图。

示例数据放在最后，因为这会占用一些空间。

我们首先构造三个不同的ggplot2绘图对象，这些对象显示主热图（gg3），另外一个较小的热图，其中缺少值（gg2），以及一个表示组标签的条形图每行（gg1）。

gg3 <- ggplot(df.cor, aes(col, row, fill = Correlation)) +
    geom_tile() +
    scale_fill_distiller(palette = "RdYlBu") +
    theme_void() +
    labs(title = "Main title") +
    geom_text(aes(label = sprintf("%2.1f", Correlation)), size = 2) +
    scale_y_discrete(position = "right") +
    theme(
        plot.title = element_text(hjust = 1),
        legend.position="bottom",
        axis.text.y = element_text(color = "black", size = 10)) +
    guides(fill = guide_colourbar(
        title.position = "bottom",
        barwidth = 10,
        title.hjust = 0.5))

gg2 <- ggplot(df.flag, aes(col, row, fill = Correlation)) +
    geom_tile(colour = "grey") +
    scale_fill_distiller(palette = "RdYlBu", guide = F, na.value = "white") +
    theme_void() +
    scale_x_discrete(position = "top") +
    theme(
        axis.text.x = element_text(color = "black", size = 10, angle = 90, hjust = 1, vjust = 0.5))


gg1 <- ggplot(df.bar, aes(1, row, fill = grp)) +
    geom_tile() +
    scale_fill_manual(values = c("grp1" = "orange", "grp2" = "green")) +
    theme_void() +
    theme(legend.position = "left")

我们现在可以使用egg::ggarrange来定位所有三个图，以使y轴范围对齐。

library(egg)
ggarrange(gg1, gg2, gg3, ncol = 3, widths = c(0.1, 1, 3))

样本数据

library(tidyverse)
set.seed(2018)
nrow <- 7
ncol <- 20
df.cor <- matrix(runif(nrow * ncol, min = -1, max = 1), nrow = nrow) %>%
    as_tibble(.name_repair = ~seq(1:ncol)) %>%
    rowid_to_column("row") %>%
    gather(col, Correlation, -row) %>%
    mutate(
        row = factor(
            paste("row", row, sep = ""),
            levels = paste("row", 1:nrow, sep = "")),
        col = factor(
            paste("col", col, sep = ""),
            levels = paste("col", 1:ncol, sep = "")))

nrow <- 7
ncol <- 10
df.flag <- matrix(runif(nrow * ncol, min = -1, max = 1), nrow = nrow) %>%
    as_tibble(.name_repair = ~seq(1:ncol)) %>%
    rowid_to_column("row") %>%
    gather(col, Correlation, -row) %>%
    mutate(
        row = factor(
            paste("row", row, sep = ""),
            levels = paste("row", 1:nrow, sep = "")),
        col = factor(
            paste("col", col, sep = ""),
            levels = paste("col", 1:ncol, sep = ""))) %>%
    mutate(Correlation = ifelse(abs(Correlation) < 0.5, NA, Correlation))


df.bar <- data.frame(
    row = 1:nrow,
    grp = paste("grp", c(rep(1, nrow - 3), rep(2, 3)), sep = "")) %>%
    mutate(
        row = factor(
            paste("row", row, sep = ""),
            levels = paste("row", 1:nrow, sep = "")))

Answer 2

这是我为了获得结果而做出的最后更改，但是，如果您不太喜欢hotmap.2，我建议您使用Maurits Evers的建议。不要忽略我对图像尺寸所做的更改。

# creates my own colour palette
    my_palette <- colorRampPalette(c("snow", "yellow", "darkorange", "red"))(n = 399)

  # (optional) defines the colour breaks manually for a "skewed" colour transition
    col_breaks = c(seq(0,0.09,length=100),    #white 'snow'
                 seq(0.1,0.19,length=100), # for yellow
                 seq(0.2,0.29,length=100), # for orange 'darkorange'
                 seq(0.3,1,length=100))    # for red

  # creates an image
  png(paste(SourceDir, "Heatmap_XYZ.png" )

  # create PNG for the heat map        
      width = 5*580,        # 5 x 580 pixels
      height = 5*420,       # 5 x 420 pixels
      res = 300,            # 300 pixels per inch
      pointsize =11)        # smaller font size

  heatmap.2(ConditionsMtx[[ConditionsAbbr[i]]],
            cellnote = ConditionsMtx[[ConditionsAbbr[i]]],  # same data set for cell labels
            main =  "XYZ", # heat map title
            notecol="black",      # change font color of cell labels to black
            density.info="none",  # turns off density plot inside color legend
            trace="none",         # turns off trace lines inside the heat map
            margins=c(0,0),     # widens margins around plot
            col=my_palette,       # use on color palette defined earlier
            breaks=col_breaks,    # enable color transition at specified limits
            dendrogram="none",     # only draw a row dendrogram
            srtCol = 0 ,        #correct angle of label numbers
            asp = 1 ,         #this overrides layout methinks and for some reason makes it square
            adjCol = c(NA, -38.3) , #shift column labels
            adjRow = c(77.5, NA) , #shift row labels
            keysize =  2 ,  #alter key size
            Colv = FALSE ,      #turn off column clustering
            Rowv =  FALSE ,    # turn off row clustering
            key.xlab = paste("Correlation") , #add label to key 
            cexRow = (1.8) , # alter row label font size
            cexCol = (1.8) , # alter column label font size
            notecex = (1.5) , # Alter cell font size
            lmat = rbind( c(0, 3, 0), c(2, 1, 0), c(0, 4, 0) ) , 
            lhei = c(0.43, 2.6, 0.6) , # Alter dimensions of display array cell heighs
            lwid = c(0.6, 4, 0.6) , # Alter dimensions of display array cell widths
            key.par=list(mar=c(4.5,0, 1.8,0) ) ) #tweak specific key paramters

  dev.off()

这里是输出，我将继续对其进行改进，直到所有间距和字体大小都符合我的审美偏好为止。我会确切地告诉您我做了什么，但是我不是100％地确定，坦白地说，这一切都感觉像是与旧的口香糖和细绳缠绕在一起，但正如他们所说，不要在代码中踢礼马。

定位热图困难。2个组件

2 个答案:

通过`facet_wrap`

最后一次更新

样本数据

定位热图困难。2个组件

2 个答案:

通过facet_wrap

最后一次更新

样本数据

通过`facet_wrap`