m6A peak分布饼图绘制 2019-02-25

#读取注释文件
> read.table("PA_NC_specific.anno (distribution).txt", header = F, sep = "\t", quote = "")
       V1        V2        V3                        V4     V5        V6        V7                       V8 V9 V10                    V11       V12    V13 V14
1     chr1 205749988 205749989  chr1:205749771-205750206  chr1 205712819 205750276        ENST00000367142.4  0   -                 NUCKS1    coding   5UTR  95
2    chr22  36227753  36227754   chr22:36227583-36227924 chr22  36226203  36239643        ENST00000358502.9  0   -                  APOL2    coding    CDS  66
3     chr1 212608907 212608908  chr1:212608769-212609046  chr1 212608628 212620772        ENST00000613954.4  0   +                   ATF3    coding   5UTR  63
4     chr3 184261175 184261176  chr3:184260989-184261362  chr3 184259213 184261463        ENST00000296238.3  0   -                CAMK2N2    coding    CDS  47
5    chr10  86499737  86499738   chr10:86499570-86499904 chr10  86435257  86521768        ENST00000618527.4  0   -                   WAPL    coding    CDS  43
6     chr8 143919203 143919204  chr8:143918854-143919552  chr8 143915153 143950876        ENST00000322810.8  0   -                   PLEC    coding    CDS  79
7    chr12  14803321  14803322   chr12:14803170-14803473 chr12  14784579  14803540        ENST00000261167.6  0   -                  WBP11    coding intron   2
8     chr2 105334705 105334706  chr2:105334480-105334931  chr2 105334027 105337475        ENST00000610036.1  0   -          RP11-332H14.2 noncoding   exon  81
9     chr1 144423827 144423828  chr1:144423706-144423949  chr1 144421386 144456809        ENST00000577412.5  0   -                 NBPF15    coding intron   8
10   chr16  11691563  11691564   chr16:11691237-11691889 chr16  11679080  11742878        ENST00000283033.9  0   -                TXNDC11    coding    CDS  57
                          V15                                V16                 V17
1          ENSG00000069275.12                     protein_coding                mRNA
2          ENSG00000128335.13                     protein_coding                mRNA
3          ENSG00000162772.16                     protein_coding                mRNA
4           ENSG00000163888.3                     protein_coding                mRNA
5          ENSG00000062650.17                     protein_coding                mRNA
6          ENSG00000178209.14                     protein_coding                mRNA
7           ENSG00000084463.7                     protein_coding                mRNA
8           ENSG00000272994.1                            lincRNA Long non-coding RNA
9           ENSG00000266338.6                     protein_coding                mRNA
10         ENSG00000153066.12                     protein_coding                mRNA

需要的是V17，所以要提取第17列出来计算每个位置出现的频率，然后用于画饼图
流程：

data <- read.table("PA_NC_specific.anno (distribution).txt", header = F, sep = "\t", quote = "")
#提取第17行，统计频率
data1 <- table(data[,17])
>data1
Long non-coding RNA                mRNA              Others          Pseudogene 
               1074                7941                 108                 103 
#把data1转换成数据帧
data2 <- as.data.frame(data1)
data2
                 Var1 Freq
1 Long non-coding RNA 1074
2                mRNA 7941
3              Others  108
4          Pseudogene  103
#方便画图，把第一列的名字改成type
names(data2)[1] <- "type"
data2
                 type Freq
1 Long non-coding RNA 1074
2                mRNA 7941
3              Others  108
4          Pseudogene  103
#饼图函数
pie_plot <- function(dt){
#colnames must be Freq and type
#####Freq is Freq or percent , type is type or label
library(ggplot2)
library(ggsci)
#dt = dt[order(dt$Freq, decreasing = TRUE),]   ##  order the Freq 
  
myLabel = paste(dt$type, " ( ", round(dt$Freq/sum(dt$Freq)*100,2), "% )   ", sep = "")  
p = ggplot(dt, aes(x = "", y = Freq, fill = type)) + 
  geom_bar(stat = "identity", width = 1) + scale_fill_jama(name="",label=myLabel)+ 
  coord_polar(theta = "y") + theme_void()+
  labs(x = "", y = "", title = "") + 
  theme(axis.ticks = element_blank()) + 
  theme(legend.title = element_blank())#, legend.position = "bottom")
     
return(p)
}
#生成饼图
pie_plot(dt=data2)

pie plot