R ggplot中的热图中的聚类数据

问题描述

请参阅下面的图：

Please see my plot below:

我的代码：

my code:

 > 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 <- as.data.frame(scale(t(data)))
pd$Time <- sub("_.*", "", rownames(pd))
pd.m <- melt(pd)
pd.m$variable <- as.numeric(factor(pd.m$variable, levels =     rev(as.character(unique(pd.m$variable))), ordered=F))
p <- 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="")

是否有一种方法可以对图进行聚类，以便图显示时间过程中的动态。我想使用来自以下的聚类：

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:

 hc.cols <- hclust(dist(t(data)))

推荐答案

您可以通过在树状图中定义时间点的顺序来实现此目的您已将 hclust 应用于您的数据：

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

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

然后你唯一要做的就是改变你的时间-column转换为因子，其中因子水平按 ord 排序：

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

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") )

其余由 ggplot 自动完成：

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

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