blocks|key|4584972|text|我认为ggplot不支持开箱即用，但您可以使用heatmap|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4584973|+heatmap(
+++as.matrix(dat),+Rowv=NA,
+++Colv=as.dendrogram(hclust(dist(t(as.matrix(dat)))))
+)|code-block|syntax|javascript|4584974|​|4584975|📷|atomic|4584976|4584977|注意这看起来不像你的，因为我只是使用你的数据的head，而不是全部。|4584978|在这里，我们使用从带有Colv参数的hclust派生的树状图手动指定集群。如果默认使用的集群与您想要的不一致，也可以通过Colv参数手动指定集群。|4584979|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/06bdd32168fb93a9c76c8be2599b89f1.png|imageAlt^0|3|6|N|7|0|0|0|0|1|0|0|0|N|4|0|B|4|I|6|1O|4|0^^$0|@$1|2|3|4|5|6|7|13|8|@$9|14|A|15|B|C]|$9|16|A|17|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|18|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|19|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|1A|8|@]|D|@$9|1B|A|1C|1|1D]]|E|$]]|$1|P|3|L|5|6|7|1E|8|@]|D|@]|E|$]]|$1|Q|3|R|5|6|7|1F|8|@$9|1G|A|1H|B|C]]|D|@]|E|$]]|$1|S|3|T|5|6|7|1I|8|@$9|1J|A|1K|B|C]|$9|1L|A|1M|B|C]|$9|1N|A|1O|B|C]]|D|@]|E|$]]|$1|U|3|-4|5|6|7|1P|8|@]|D|@]|E|$]]]|V|$W|$5|X|Y|Z|E|$10|11|12|-4]]]]

I don't think <code>ggplot</code> supports this out of the box, but you can use <code>heatmap</code>:

<pre><code> heatmap(
 as.matrix(dat), Rowv=NA,
 Colv=as.dendrogram(hclust(dist(t(as.matrix(dat)))))
 )
</code></pre>

<img src="https://i.stack.imgur.com/csYgz.png" alt="enter image description here">

Note this won't look like yours because I'm just using the <code>head</code> of your data, not the whole thing.

Here we specify the clustering manually with a dendogram derived from your <code>hclust</code> with the <code>Colv</code> argument. You can specify the clustering manually too through the <code>Colv</code> argument if the one used by default doesn't line up with what you want.

blocks|key|78286|text|在将hclust应用于数据之后，您可以通过定义树状图中的时间点顺序来实现这一点：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|78287|data+<-+scale(t(data))
ord+<-+hclust(+dist(data,+method+=+"euclidean"),+method+=+"ward.D"+)$order
ord
[1]++2++3++1++4++8++5++6+10++7++9|code-block|syntax|javascript|78288|然后，您要做的唯一一件事就是将时间列转换为因子级别按ord排序的factor|78289|pd+<-+as.data.frame(+data+)
pd$Time+<-+sub("_.*",+"",+rownames(pd))
pd.m+<-+melt(+pd,+id.vars+=+"Time",+variable.name+=+"Gene"+)

pd.m$Gene+<-+factor(+pd.m$Gene,+levels+=+colnames(data),+labels+=+seq_along(+colnames(data)+)+)
pd.m$Time+<-+factor(+pd.m$Time,+levels+=+rownames(data)[ord],++labels+=+c("0h",+"0.25h",+"0.5h","1h","2h","3h","6h","12h","24h","48h")+)|78290|剩下的工作由ggplot自动完成：|78291|ggplot(+pd.m,+aes(Time,+Gene)+)+%2B
++geom_tile(aes(fill+=+value))+%2B
++scale_fill_gradient2(low=muted("blue"),+high=muted("red"))|78292|​|78293|📷|atomic|78294|78295|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/9ba1005bc347f44d9bbd4169eed0f1ec.png|imageAlt^0|2|6|0|0|Q|3|W|6|0|0|6|6|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|17|8|@$9|18|A|19|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1A|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1B|8|@$9|1C|A|1D|B|C]|$9|1E|A|1F|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|1G|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1H|8|@$9|1I|A|1J|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1K|8|@]|D|@]|E|$I|J]]|$1|S|3|T|5|6|7|1L|8|@]|D|@]|E|$]]|$1|U|3|V|5|W|7|1M|8|@]|D|@$9|1N|A|1O|1|1P]]|E|$]]|$1|X|3|T|5|6|7|1Q|8|@]|D|@]|E|$]]|$1|Y|3|-4|5|6|7|1R|8|@]|D|@]|E|$]]]|Z|$10|$5|11|12|13|E|$14|15|16|-4]]]]

You can achieve this by defining the order of Timepoints in a dendrogram after you have applied <code>hclust</code> to your data: 

<pre><code>data &lt;- scale(t(data))
ord &lt;- hclust( dist(data, method = "euclidean"), method = "ward.D" )$order
ord
[1] 2 3 1 4 8 5 6 10 7 9
</code></pre>

The only thing you have to do then is transforming your Time-column to a <code>factor</code> where the factor levels are ordered by <code>ord</code>:

<pre><code>pd &lt;- as.data.frame( data )
pd$Time &lt;- sub("_.*", "", rownames(pd))
pd.m &lt;- melt( pd, id.vars = "Time", variable.name = "Gene" )

pd.m$Gene &lt;- factor( pd.m$Gene, levels = colnames(data), labels = seq_along( colnames(data) ) )
pd.m$Time &lt;- factor( pd.m$Time, levels = rownames(data)[ord], labels = c("0h", "0.25h", "0.5h","1h","2h","3h","6h","12h","24h","48h") )
</code></pre>

The rest is done by <code>ggplot</code> automatically:

<pre><code>ggplot( pd.m, aes(Time, Gene) ) +
 geom_tile(aes(fill = value)) +
 scale_fill_gradient2(low=muted("blue"), high=muted("red"))
</code></pre>

<img src="https://i.stack.imgur.com/sEQJA.png" alt="enter image description here">

blocks|key|4585055|text|我想补充一下，您不需要将data.frame中的列转换为因子，您可以使用ggplot的scale_*_discrete函数来设置轴的绘图顺序。只需使用limits参数设置绘图顺序，使用labels参数设置标签，如下所示。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4585056|data<-read.table(text="X0++++++X1++++++X2+++++++X3+++++++X4+++++++X5+++++++X6++++++++X7++++++++X8++++++++X9
+NM_001001144+6.52334+9.75243+5.62914+6.833650+6.789850+7.421440+8.675330+12.117600+11.551500++7.676900
+NM_001001327+1.89826+3.74708+1.48213+0.590923+2.915120+4.052600+0.758997++3.653680++1.931400++2.487570
+NM_001002267+1.70346+2.72858+2.10879+1.898050+3.063480+4.435810+7.499640++5.038870+11.128700+22.016500
+NM_001003717+6.02279+7.46547+7.39593+7.344080+4.568470+3.347250+2.230450++3.598560++2.470390++4.184450
+NM_001003920+1.06842+1.11961+1.38981+1.054000+0.833823+0.866511+0.795384++0.980946++0.731532++0.949049
+NM_001003953+7.50832+7.13316+4.10741+5.327390+2.311230+1.023050+2.573220++1.883740++3.215150++2.483410",+header+=+TRUE,+stringsAsFactors+=+FALSE)
data+<-+scale(t(data))
ord+<-+hclust(+dist(data,+method+=+"euclidean"),+method+=+"ward.D"+)$order
pd+<-+as.data.frame(+data+)
pd$Time+<-+sub("_.*",+"",+rownames(pd))
pd.m+<-+melt(+pd,+id.vars+=+"Time",+variable.name+=+"Gene"+)
ggplot(+pd.m,+aes(Time,+Gene)+)+%2B
++geom_tile(aes(fill+=+value))+%2B
++scale_x_discrete(limits=pd.m$Time[ord],+labels+=+c("0h",+"0.25h",+"0.5h","1h","2h","3h","6h","12h","24h","48h"))%2B
++scale_y_discrete(limits=colnames(data),+labels+=+seq_along(colnames(data)))%2B
++scale_fill_gradient2(low=muted("blue"),+high=muted("red"))|code-block|syntax|javascript|4585057|​|4585058|📷|atomic|4585059|4585060|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/56a5a0d17ef18ba7cc900eba2e90e838.png|imageAlt^0|C|A|10|6|17|G|23|6|2K|6|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@$9|10|A|11|B|C]|$9|12|A|13|B|C]|$9|14|A|15|B|C]|$9|16|A|17|B|C]|$9|18|A|19|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|1A|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1B|8|@]|D|@]|E|$]]|$1|M|3|N|5|O|7|1C|8|@]|D|@$9|1D|A|1E|1|1F]]|E|$]]|$1|P|3|L|5|6|7|1G|8|@]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1H|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X|Y|-4]]]]

Thought I'd add you don't need to transform the columns in the <code>data.frame</code> to factors, you can use <code>ggplot</code>'s <code>scale_*_discrete</code> function to set the plotting order of axes. Simply set the plotting order using the <code>limits</code> argument and the labels using the <code>labels</code> argument as shown below. 

<pre><code>data&lt;-read.table(text="X0 X1 X2 X3 X4 X5 X6 X7 X8 X9
 NM_001001144 6.52334 9.75243 5.62914 6.833650 6.789850 7.421440 8.675330 12.117600 11.551500 7.676900
 NM_001001327 1.89826 3.74708 1.48213 0.590923 2.915120 4.052600 0.758997 3.653680 1.931400 2.487570
 NM_001002267 1.70346 2.72858 2.10879 1.898050 3.063480 4.435810 7.499640 5.038870 11.128700 22.016500
 NM_001003717 6.02279 7.46547 7.39593 7.344080 4.568470 3.347250 2.230450 3.598560 2.470390 4.184450
 NM_001003920 1.06842 1.11961 1.38981 1.054000 0.833823 0.866511 0.795384 0.980946 0.731532 0.949049
 NM_001003953 7.50832 7.13316 4.10741 5.327390 2.311230 1.023050 2.573220 1.883740 3.215150 2.483410", header = TRUE, stringsAsFactors = FALSE)
data &lt;- scale(t(data))
ord &lt;- hclust( dist(data, method = "euclidean"), method = "ward.D" )$order
pd &lt;- as.data.frame( data )
pd$Time &lt;- sub("_.*", "", rownames(pd))
pd.m &lt;- melt( pd, id.vars = "Time", variable.name = "Gene" )
ggplot( pd.m, aes(Time, Gene) ) +
 geom_tile(aes(fill = value)) +
 scale_x_discrete(limits=pd.m$Time[ord], labels = c("0h", "0.25h", "0.5h","1h","2h","3h","6h","12h","24h","48h"))+
 scale_y_discrete(limits=colnames(data), labels = seq_along(colnames(data)))+
 scale_fill_gradient2(low=muted("blue"), high=muted("red"))
</code></pre>

<a href="https://i.stack.imgur.com/EogvH.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/EogvH.png" alt="enter image description here"></a>

Please see my plot below: 
<img src="https://i.stack.imgur.com/0Ydc3.png" alt="enter image description here">

my code:

<pre><code> &gt; head(data)
 X0 X1 X2 X3 X4 X5 X6 X7 X8 X9
 NM_001001144 6.52334 9.75243 5.62914 6.833650 6.789850 7.421440 8.675330 12.117600 11.551500 7.676900
 NM_001001327 1.89826 3.74708 1.48213 0.590923 2.915120 4.052600 0.758997 3.653680 1.931400 2.487570
 NM_001002267 1.70346 2.72858 2.10879 1.898050 3.063480 4.435810 7.499640 5.038870 11.128700 22.016500
 NM_001003717 6.02279 7.46547 7.39593 7.344080 4.568470 3.347250 2.230450 3.598560 2.470390 4.184450
 NM_001003920 1.06842 1.11961 1.38981 1.054000 0.833823 0.866511 0.795384 0.980946 0.731532 0.949049
 NM_001003953 7.50832 7.13316 4.10741 5.327390 2.311230 1.023050 2.573220 1.883740 3.215150 2.483410

pd &lt;- as.data.frame(scale(t(data)))
pd$Time &lt;- sub("_.*", "", rownames(pd))
pd.m &lt;- melt(pd)
pd.m$variable &lt;- as.numeric(factor(pd.m$variable, levels = rev(as.character(unique(pd.m$variable))), ordered=F))
p &lt;- ggplot(pd.m, aes(Time, variable))
p + geom_tile(aes(fill = value)) + scale_fill_gradient2(low=muted("blue"), high=muted("red")) +
 scale_x_discrete(labels=c("0h", "0.25h", "0.5h","1h","2h","3h","6h","12h","24h","48h")) + 
 theme_bw(base_size=20) + theme(axis.text.x=element_text(angle=0, vjust=0.5, hjust=0, size=12),
 axis.text.y=element_text(size=12), strip.text.y=element_text(angle=0, vjust=0.5, hjust=0.5, size=12),
 strip.text.x=element_text(size=12)) + labs(y="Genes", x="Time (h)", fill="")
</code></pre>

Is there a way to cluster the plot so that the plot displays the dynamics in the time course. I would like to use the clustering that comes out of:

<pre><code> hc.cols &lt;- hclust(dist(t(data)))
</code></pre>

<img src="https://i.stack.imgur.com/P4HeE.png" alt="enter image description here">

Cluster data in heat map in R ggplot

请看我的图表：​我的代码： > head(data)              X0      X1      X2       X3       X4       X5       X6        X7        X8        X9 NM_001001144 6.52334 9.75243 5.6291...

问在R ggplot中聚类热图中的数据
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