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我有以下数据框：

          stat                     mTADs                    DE_genes
    5267 -5.452819  chr2:167337500-167447500  chr2:167318145-167341673:+
    5268  4.114012    chr6:41532500-41642500    chr6:41555481-41570508:+
    5269  9.812369   chr10:18157500-18262500   chr10:18259929-18265882:-
    5270  3.371969   chr17:40957500-41062500   chr17:41060000-41071996:-
    5271  4.576930   chr17:40957500-41062500   chr17:41012431-41017507:-
    5272  2.952151   chr11:72251250-72352500   chr11:72254857-72265270:+
    5273 -3.349795  chr1:174307500-174407500  chr1:174405489-174408706:+
    5274 -2.685897 chr13:100777500-100877500 chr13:100787949-100874025:-
    5275  2.865269 chr13:100777500-100877500 chr13:100718488-100785594:-
    5276  6.436959  chr4:150417500-150517500  chr4:150377761-150418774:-
    5277  2.622196      chr7:6072500-6162500      chr7:6123828-6142951:+
    5278 -5.605531   chr11:48597500-48682500   chr11:48675470-48685185:-
    5279  3.554733   chr11:48597500-48682500   chr11:48639642-48665711:+
    5280  4.399655   chr11:48597500-48682500   chr11:48638848-48640157:-

如您所见，一些 DE_genes 属于同一个 mTAD。我想为所有 DE_genes 绘制它们的统计值并按 mTAD 对它们进行分组。我想把它作为一个水平条形图，在 y 轴上有基因，在 x 轴上有统计值并按 TAD 分组，但首先我不知道该怎么做，其次我认为热图可能更好选项。有没有办法在 R 中做到这一点？我总共有 1700 个 mTAD，我想看看数据中是否有任何模式。

非常感谢， 迪米特里斯

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1 回答 1

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您可能会考虑使用排序的点图而不是条形图。

> thing
     ID      stat                     mTADs                    DE_genes
1  5267 -5.452819  chr2:167337500-167447500  chr2:167318145-167341673:+
2  5268  4.114012    chr6:41532500-41642500    chr6:41555481-41570508:+
3  5269  9.812369   chr10:18157500-18262500   chr10:18259929-18265882:-
4  5270  3.371969   chr17:40957500-41062500   chr17:41060000-41071996:-
5  5271  4.576930   chr17:40957500-41062500   chr17:41012431-41017507:-
6  5272  2.952151   chr11:72251250-72352500   chr11:72254857-72265270:+
7  5273 -3.349795  chr1:174307500-174407500  chr1:174405489-174408706:+
8  5274 -2.685897 chr13:100777500-100877500 chr13:100787949-100874025:-
9  5275  2.865269 chr13:100777500-100877500 chr13:100718488-100785594:-
10 5276  6.436959  chr4:150417500-150517500  chr4:150377761-150418774:-
11 5277  2.622196      chr7:6072500-6162500      chr7:6123828-6142951:+
12 5278 -5.605531   chr11:48597500-48682500   chr11:48675470-48685185:-
13 5279  3.554733   chr11:48597500-48682500   chr11:48639642-48665711:+
14 5280  4.399655   chr11:48597500-48682500   chr11:48638848-48640157:-

首先，我们将通过 mTAD 取中位数。

medians.of.stat.by.mTADs<-aggregate(stat~mTADs,data=thing,FUN=median)
names(medians.of.stat.by.mTADs)[2]<-"median stat for mTAD"

现在将这些中位数与原始数据框合并，并通过对排序stat值进行排序来创建一个因子。

thing<-merge(thing,medians.of.stat.by.mTADs,all.x = T,by="mTADs")
thing$mTADs.reordered <-factor(thing$mTADs, levels=thing[order(thing$`median stat for mTAD`), "mTADs"])

由于重复的关卡，这将引发警告，但它似乎有效。

> thing
                       mTADs   ID      stat                    DE_genes median stat for mTAD           mTADs.reordered
1   chr1:174307500-174407500 5273 -3.349795  chr1:174405489-174408706:+            -3.349795  chr1:174307500-174407500
2    chr10:18157500-18262500 5269  9.812369   chr10:18259929-18265882:-             9.812369   chr10:18157500-18262500
3    chr11:48597500-48682500 5278 -5.605531   chr11:48675470-48685185:-             3.554733   chr11:48597500-48682500
4    chr11:48597500-48682500 5279  3.554733   chr11:48639642-48665711:+             3.554733   chr11:48597500-48682500
5    chr11:48597500-48682500 5280  4.399655   chr11:48638848-48640157:-             3.554733   chr11:48597500-48682500
6    chr11:72251250-72352500 5272  2.952151   chr11:72254857-72265270:+             2.952151   chr11:72251250-72352500
7  chr13:100777500-100877500 5274 -2.685897 chr13:100787949-100874025:-             0.089686 chr13:100777500-100877500
8  chr13:100777500-100877500 5275  2.865269 chr13:100718488-100785594:-             0.089686 chr13:100777500-100877500
9    chr17:40957500-41062500 5270  3.371969   chr17:41060000-41071996:-             3.974449   chr17:40957500-41062500
10   chr17:40957500-41062500 5271  4.576930   chr17:41012431-41017507:-             3.974449   chr17:40957500-41062500
11  chr2:167337500-167447500 5267 -5.452819  chr2:167318145-167341673:+            -5.452819  chr2:167337500-167447500
12  chr4:150417500-150517500 5276  6.436959  chr4:150377761-150418774:-             6.436959  chr4:150417500-150517500
13    chr6:41532500-41642500 5268  4.114012    chr6:41555481-41570508:+             4.114012    chr6:41532500-41642500
14      chr7:6072500-6162500 5277  2.622196      chr7:6123828-6142951:+             2.622196      chr7:6072500-6162500

现在做一个简单的点图。

ggplot() + geom_point(data=thing, aes(x=stat,y=mTADs.reordered), shape=20, cex=3.3)

按 mTAD 的中位数排序的点图

于 2016-05-06T08:37:32.200 回答