热图中的行顺序？

发布于 2024-10-22 08:54:23 字数 201 浏览 3 评论 0原文

采取以下代码：

 heatmap(data.matrix(signals),col=colors,breaks=breaks,scale="none",Colv=NA,labRow=NA)

如何提取、预计算或重新计算生成的热图中的行顺序？有没有办法将 hclust(dist(signals)) 的输出注入到热图函数中？

原文

Take the following code:

 heatmap(data.matrix(signals),col=colors,breaks=breaks,scale="none",Colv=NA,labRow=NA)

How can I extract, pre-calculate or re-calculate the order of the rows in the heatmap produced? Is there a way to inject the output of hclust(dist(signals)) into the heatmap function?

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今天小雨转甜 2024-10-29 08:54:23

感谢杰西和保罗的反馈。我编写了以下排序函数，希望对其他人有用：

data        = data.matrix(data)
distance    = dist(data)
cluster     = hclust(distance, method="ward")
dendrogram  = as.dendrogram(cluster)
Rowv        = rowMeans(data, na.rm = T)
dendrogram  = reorder(dendrogram, Rowv)

## Produce the heatmap from the calculated dendrogram.
## Don't allow it to re-order rows because we have already re-ordered them above.

reorderfun = function(d,w) { d }
png("heatmap.png", res=150, height=22,width=17,units="in")

heatmap(data,col=colors,breaks=breaks,scale="none",Colv=NA,Rowv=dendrogram,labRow=NA, reorderfun=reorderfun)

dev.off()


## Re-order the original data using the computed dendrogram
rowInd = rev(order.dendrogram(dendrogram))
di = dim(data)
nc = di[2L]
nr = di[1L]
colInd = 1L:nc
data_ordered <- data[rowInd, colInd]
write.table(data_ordered, "rows.txt",quote=F, sep="\t",row.names=T, col.names=T)

Thanks for the feedback, Jesse and Paolo. I wrote the following ordering function which will hopefully be useful to others:

data        = data.matrix(data)
distance    = dist(data)
cluster     = hclust(distance, method="ward")
dendrogram  = as.dendrogram(cluster)
Rowv        = rowMeans(data, na.rm = T)
dendrogram  = reorder(dendrogram, Rowv)

## Produce the heatmap from the calculated dendrogram.
## Don't allow it to re-order rows because we have already re-ordered them above.

reorderfun = function(d,w) { d }
png("heatmap.png", res=150, height=22,width=17,units="in")

heatmap(data,col=colors,breaks=breaks,scale="none",Colv=NA,Rowv=dendrogram,labRow=NA, reorderfun=reorderfun)

dev.off()


## Re-order the original data using the computed dendrogram
rowInd = rev(order.dendrogram(dendrogram))
di = dim(data)
nc = di[2L]
nr = di[1L]
colInd = 1L:nc
data_ordered <- data[rowInd, colInd]
write.table(data_ordered, "rows.txt",quote=F, sep="\t",row.names=T, col.names=T)

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肤浅与狂妄 2024-10-29 08:54:23

有多种选择。如果运行?heatmap，您将看到可以调整的各种参数。也许最简单的方法是设置 Rowv=NA ，它应该抑制行重新排序，然后传入矩阵，其中行已经按照您想要的顺序排列。但您也可以通过 Rowv 和 hclustfun 等手动提供聚类函数或树状图...

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毁我热情 2024-10-29 08:54:23

我相信这篇文章可能有用：

How does R heatmap order rows by default ？

以下面的矩阵为例：

set.seed(321)
m = matrix(nrow=7, ncol = 7, rnorm(49))
> m
           [,1]       [,2]       [,3]        [,4]       [,5]        [,6]      [,7]
[1,]  1.7049032  0.2331354 -1.1534395 -0.10706154 -1.1203274  0.11453945 0.2503958
[2,] -0.7120386  0.3391139 -0.8046717  0.98833540 -0.4746847 -2.22626331 0.2440872
[3,] -0.2779849 -0.5519147  0.4560691 -1.07223880 -1.5304122  1.63579034 0.7997382
[4,] -0.1196490  0.3477014  0.4203326 -0.75801528  0.4157148 -0.15932072 0.3414096
[5,] -0.1239606  1.4845918  0.5775845  0.09500072  0.6341979  0.02826746 0.2587177
[6,]  0.2681838  0.1883255  0.4463561 -2.33093117  1.2308474 -1.53665329 0.9538786
[7,]  0.7268415  2.4432598  0.9172555  0.41751598 -0.1545637  0.07815779 1.1364147

您可以使用参数Rowv和Colv覆盖行和列的顺序。您可以使用这些树状图来覆盖顺序。例如，您可以使用函数 hclust 计算顺序，然后将其作为树状图传递给 heatmap：

 rhcr <- hclust(dist(m))
 chrc <- hclust(dist(t(m)))
 heatmap(m,Rowv = as.dendrogram(rhcr),
           Colv = as.dendrogram(rhcr))

 > rhcr$order
 [1] 1 3 6 2 7 4 5
 > chrc$order
 [1] 6 4 5 1 2 3 7

给出：

Hclust heatmap

默认的heatmap函数使用了一个额外的步骤，但是，通过参数reorderfun = function(d, w) reorder(d, w )，它根据行/列均值尽可能地对树状图进行重新排序。您可以通过此附加步骤重现默认顺序。因此，要获得与 heatmap 相同的顺序，您可以执行以下操作：

rddr <- reorder(as.dendrogram(rhcr),rowMeans(m))
cddr <- reorder(as.dendrogram(chcr),colMeans(m))

> as.hclust(rddr)$order
[1] 3 1 6 2 4 5 7
> as.hclust(cddr)$order
[1] 6 4 5 1 2 3 7

输出与简单的 heatmap(m) 相同：

其 sstatic.net/3EPYS.png" rel="nofollow noreferrer">默认热图

在此示例中，列恰好没有重新排序，但行却重新排序。最后，为了简单地检索顺序，您可以将热图分配给变量并获取输出。

> p <- heatmap(m)
> p$rowInd
[1] 3 1 6 2 4 5 7
> p$colInd
[1] 6 4 5 1 2 3 7

I believe this post might be useful:

How does R heatmap order rows by default?

Take the following matrix for example:

set.seed(321)
m = matrix(nrow=7, ncol = 7, rnorm(49))
> m
           [,1]       [,2]       [,3]        [,4]       [,5]        [,6]      [,7]
[1,]  1.7049032  0.2331354 -1.1534395 -0.10706154 -1.1203274  0.11453945 0.2503958
[2,] -0.7120386  0.3391139 -0.8046717  0.98833540 -0.4746847 -2.22626331 0.2440872
[3,] -0.2779849 -0.5519147  0.4560691 -1.07223880 -1.5304122  1.63579034 0.7997382
[4,] -0.1196490  0.3477014  0.4203326 -0.75801528  0.4157148 -0.15932072 0.3414096
[5,] -0.1239606  1.4845918  0.5775845  0.09500072  0.6341979  0.02826746 0.2587177
[6,]  0.2681838  0.1883255  0.4463561 -2.33093117  1.2308474 -1.53665329 0.9538786
[7,]  0.7268415  2.4432598  0.9172555  0.41751598 -0.1545637  0.07815779 1.1364147

You can override the order of the rows and columns with the parameters Rowv and Colv. You can override the order with these as dendrograms. For instance, you can calculate an order using the function hclust, then pass that to heatmap as a dendrogram:

 rhcr <- hclust(dist(m))
 chrc <- hclust(dist(t(m)))
 heatmap(m,Rowv = as.dendrogram(rhcr),
           Colv = as.dendrogram(rhcr))

 > rhcr$order
 [1] 1 3 6 2 7 4 5
 > chrc$order
 [1] 6 4 5 1 2 3 7

Gives:

Hclust heatmap

The default heatmap function uses one additional step, however, through the parameter reorderfun = function(d, w) reorder(d, w), which reorders the dendrogram as much as possible bases on row/column mean. you can reproduce the default order with this additional step. So to get the same ordering as heatmap, you can do:

rddr <- reorder(as.dendrogram(rhcr),rowMeans(m))
cddr <- reorder(as.dendrogram(chcr),colMeans(m))

> as.hclust(rddr)$order
[1] 3 1 6 2 4 5 7
> as.hclust(cddr)$order
[1] 6 4 5 1 2 3 7

Which gives the same output as simply heatmap(m):

Default heatmap

In this example the columns happen to not get reordered, but the rows do. Finally, to simply retrieve the order you can assign the heatmap to a variable and get the output.

> p <- heatmap(m)
> p$rowInd
[1] 3 1 6 2 4 5 7
> p$colInd
[1] 6 4 5 1 2 3 7

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不奢求什么 2024-10-29 08:54:23

我同意杰西的观点。对于您的问题，请查看热图函数的 Rowv、distfun 和 hclustfun 参数。
如需更多选择，请使用 gplots 包中的函数 heatmap.2、Heatplus 包中的 heatmap_plus 和 pheatmap 包中的 pheatmap 可能会有一些用处。