下图为miRNA与靶基因相关性图，这里简单介绍下如何作图。

首先，要生成作图的输入文件，有脚本，用法如下：

perl  Correlation_analysis.pl  -mirna Donkey_E_vs_Donkey_T.DEG.final.xls  -DE_tar targets.final.xls  -list bta.mir2target.list  -out Correlation_analysis_input.txt

#ID     Donkey2_E_Count Donkey1_T_Count Donkey2_E_TPM   Donkey1_T_TPM   FDR     log2FC  regulated
bta-miR-106a    26      113     1.164865        54.990056       3.20268371822863e-08    4.02066488557677        up
bta-miR-10a     166025  1771    7438.335738     861.835296      0.000238871249442218    -4.55148673628311       down
bta-miR-135a    5631    114     252.2829        55.476693       0.00268305048158635     -3.62181067749427       down
bta-miR-135b    25      89      1.120063        43.310752       6.41785537824902e-07    3.73045876228641        up
bta-miR-141     1201    2       53.807807       0.973275        2.17196461482771e-07    -6.91926313309958       down
bta-miR-182     1882    33      84.318313       16.059043       0.00119553585103038     -3.81519649022355       down
bta-miR-183     1544    40      69.175066       19.465506       0.00778797871791981     -3.25606929861198       down
bta-miR-187     0       18      0       8.759478        6.10013036195234e-07    5.24714335099614        up
bta-miR-196a    2871    31      128.627989      15.085767       0.000103494675187443    -4.51281538763237       down

bta-let-7a-3p   EAS0021476;EAS0000070;EAS0013638;EAS0001074;EAS0004577;EAS0006787;EAS0008899;EAS0013962;EAS0017655;EAS0005413;EAS0006009
bta-let-7a-5p   EAS0008419;EAS0003830;EAS0009803;EAS0011296;EAS0005793;EAS0004645;EAS0014772
bta-let-7b      EAS0017817;EAS0013474;EAS0008780;EAS0018405;EAS0017359;EAS0017674;EAS0020797;EAS0015379;EAS0011272;EAS0010435;EAS0013631
bta-let-7c      EAS0006104;EAS0004549;EAS0003321;EAS0010481;EAS0021491;EAS0015994;EAS0001788;EAS0002956;EAS0007473;EAS0008880;EAS0007345;EAS0003066
bta-let-7d      EAS0005004;EAS0009924;EAS0020635;EAS0002650;EAS0011574

最后生成的文件如下所示：

miRNA   Gene    log2Ratio_miR   log2Ratio_Gene
bta-let-7c      EAS0003321      -1.43072567351625       1.37874741391777
bta-let-7c      EAS0007345      -1.43072567351625       1.11756970634256
bta-let-7c      EAS0003066      -1.43072567351625       1.67994238012443
bta-let-7c      EAS0017368      -1.43072567351625       3.05079253633835
bta-let-7c      EAS0004422      -1.43072567351625       1.50329494091278
bta-let-7c      EAS0003988      -1.43072567351625       1.18612566219656
bta-let-7c      EAS0005608      -1.43072567351625       1.89027598062113
bta-let-7c      EAS0012078      -1.43072567351625       3.50870147552217
bta-let-7c      EAS0015105      -1.43072567351625       1.06781330535694
bta-let-7c      EAS0020084      -1.43072567351625       1.10413933910007

有了输入文件，然后就可以作图了，作图脚本比较简单，用法：

Rscript correlation_analysis.R -i Correlation_analysis_input.txt -n Correlation_analysis

脚本代码：

library(ggplot2)
library('getopt');
spec = matrix(c(
'help' , 'h', 0, "logical","for help",
'input' , 'i', 1, "character","input file ,required",
'name' , 'n', 1, "character","photo name"
), byrow=TRUE, ncol=5);
opt = getopt(spec);
print_usage <- function(spec=NULL){
cat(getopt(spec, usage=TRUE));
q(status=1);
}
if ( !is.null(opt$help) ) { print_usage(spec) }
if ( is.null(opt$input) ){ print_usage(spec) }
if ( is.null(opt$name) ){ opt$name = "Correlation analysis" }

point <- read.table(opt$input,sep="\t",header = TRUE,comment.char = "")

p <- ggplot(point, aes(x=log2Ratio_miR, y=log2Ratio_Gene)) + geom_point(size=0.01,colour="red")+
theme(
######取消默认的背景颜色方框等
panel.background = element_rect(fill = "transparent",colour = "black"), 
panel.grid.minor = element_blank(), 
panel.grid.major = element_blank(),
plot.background = element_rect(fill = "transparent",colour = "black"))
#输出文件名称
png_name=paste(opt$name, ".png", sep="")
pdf_name=paste(opt$name, ".pdf", sep="")
#输出pdf格式图片
pdf(pdf_name,width =3,height = 3)
print(p)
dev.off()
#输出png格式图片
png(png_name,width =2000,height =2000,res = 500,units = "px")
print(p)
dev.off()