利用R语言heatmap.2函数进行聚类并画热图

数据聚类然后展示聚类热图是生物信息中组学数据分析的常用方法，在R语言中有很多函数可以实现，譬如heatmap,kmeans等，除此外还有一个用得比较多的就是heatmap.2。最近在网上看到一个笔记文章关于《一步一步学heatmap.2函数》，在此与大家分享。由于原作者不详，暂未标记来源，请原作者前来认领哦，O(∩_∩)O哈哈~

数据如下：

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* library(gplots)

   
* data(mtcars)

   
* x <- as.matrix(mtcars)

   
* rc <- rainbow(nrow(x), start=0, end=.3)

   
* cc <- rainbow(ncol(x), start=0, end=.3)

X就是一个矩阵，里面是我们需要画热图的数据。

Rc是一个调色板，有32个颜色，渐进的

Cc也是一个调色板，有11个颜色，也是渐进的

首先画一个默认的图：

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* heatmap.2(x)

然后可以把聚类数可以去掉：就是控制这个dendrogram参数

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* heatmap.2(x, dendrogram="none")

然后我们控制一下聚类树

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* heatmap.2(x, dendrogram="row") # 只显示行向量的聚类情况

   
* heatmap.2(x, dendrogram="col") #只显示列向量的聚类情况

下面还是在调控聚类树，但是我没看懂跟上面的参数有啥子区别！

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* heatmap.2(x, keysize=2) ## default - dendrogram plotted and reordering done.

   
* heatmap.2(x, Rowv=FALSE, dendrogram="both") ## generate warning!

   
* heatmap.2(x, Rowv=NULL, dendrogram="both") ## generate warning!

   
* heatmap.2(x, Colv=FALSE, dendrogram="both") ## generate warning!

接下来我们可以调控行列向量的label的字体大小方向

首先我们调控列向量，也就是x轴的label

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* heatmap.2(x, srtCol=NULL)

   
* heatmap.2(x, srtCol=0, adjCol = c(0.5,1) )

   
* heatmap.2(x, srtCol=45, adjCol = c(1,1) )

   
* heatmap.2(x, srtCol=135, adjCol = c(1,0) )

   
* heatmap.2(x, srtCol=180, adjCol = c(0.5,0) )

   
* heatmap.2(x, srtCol=225, adjCol = c(0,0) ) ## not very useful

   
* heatmap.2(x, srtCol=270, adjCol = c(0,0.5) )

   
* heatmap.2(x, srtCol=315, adjCol = c(0,1) )

   
* heatmap.2(x, srtCol=360, adjCol = c(0.5,1) )

然后我们调控一下行向量，也就是y轴的label

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* heatmap.2(x, srtRow=45, adjRow=c(0, 1) )

   
* heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=45, adjCol=c(1,1) )

   
* heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=270, adjCol=c(0,0.5) )

设置 offsetRow/offsetCol 可以把label跟热图隔开！

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* ## Show effect of offsetRow/offsetCol (only works when srtRow/srtCol is 

   
* ## not also present) heatmap.2(x, offsetRow=0, offsetCol=0)

   
* heatmap.2(x, offsetRow=1, offsetCol=1) 

   
* heatmap.2(x, offsetRow=2, offsetCol=2) 

   
* heatmap.2(x, offsetRow=-1, offsetCol=-1) 

   
* heatmap.2(x, srtRow=0, srtCol=90, offsetRow=0, offsetCol=0)

   
* heatmap.2(x, srtRow=0, srtCol=90, offsetRow=1, offsetCol=1)

   
* heatmap.2(x, srtRow=0, srtCol=90, offsetRow=2, offsetCol=2)

   
* heatmap.2(x, srtRow=0, srtCol=90, offsetRow=-1, offsetCol=-1)

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* ## Show effect of z-score scaling within columns, blue-red color scale 

   
* hv <- heatmap.2(x, col=bluered, scale="column", tracecol="#303030")

hv是一个热图对象！！！

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* > names(hv) # 可以看到hv对象里面有很多子对象

   
* > "rowInd" "colInd" "call" "colMeans" "colSDs" "carpet" "rowDendrogram" "colDendrogram" "breaks" "col" "vline" "colorTable" ## Show the mapping of z-score values to color bins hvKaTeX parse error: Expected 'EOF', got '#' at position 638: ...an class="com">#̲# Extract the r...colorTable[hvKaTeX parse error: Expected 'EOF', got '#' at position 124: ...n class="str">"#̲FFFFFF"</span><...colorTable[hvKaTeX parse error: Expected 'EOF', got '#' at position 124: ...n class="str">"#̲FFFFFF"</span><...colSDs + hv c o l M e a n s < / s p a n > < s p a n c l a s s = " p u n " > , < / s p a n > < s p a n c l a s s = " p l n " > w h i t e B i n < / s p a n > < s p a n c l a s s = " p u n " > [ < / s p a n > < s p a n c l a s s = " l i t " > 2 < / s p a n > < s p a n c l a s s = " p u n " > ] < / s p a n > < s p a n c l a s s = " p l n " > < / s p a n > < s p a n c l a s s = " p u n " > ∗ < / s p a n > < s p a n c l a s s = " p l n " > h v colMeans</span><span class="pun">,</span><span class="pln"> whiteBin</span><span class="pun">[</span><span class="lit">2</span><span class="pun">]</span><span class="pln"> </span><span class="pun">*</span><span class="pln"> hv colMeans</span><spanclass="pun">,</span><spanclass="pln">whiteBin</span><spanclass="pun">[</span><spanclass="lit">2</span><spanclass="pun">]</span><spanclass="pln"></span><spanclass="pun">∗</span><spanclass="pln">hvcolSDs + hvKaTeX parse error: Expected 'EOF', got '#' at position 1148: ...n class="str">"#̲303030"</span><...Type)],

   
*  xlab='CellLines',

   
*  ylab='Probes',

   
*  main=Cluster_Method[i],

   
*  col=greenred(64))

   
*  dev.off()

   
* }

这样就可以一下子把七种cluster的方法依次用到heatmap上面来。而且通过对cluster树的比较，我们可以从中挑选出最好、最稳定到cluster方法，为后续分析打好基础！

对下面这个数据聚类：

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* require(graphics)

   
* hc <- hclust(dist(USArrests), "ave")

   
* plot(hc)

首先对一个数据框用dist函数处理得到一个dist对象！

Dist对象比较特殊，专门为hclust函数来画聚类树的！