##======QCplot
# 5种QC图像函数的快速通道
#' @export
QCplot <- function(DesignEset,
contrast=NULL,
names,
cluster_cols=T,
log.convert = F,
savefile=T){
## 加载包
library(Biobase);library(limma);library(stringr)
##预览
## MA QC
ma <- QC.plotMA(DesignEset,
contrast,
savefile=F,
names = names)
print("完成QC.plotMA")
##Heatmap QC:
hm <- QC.cluster(DesignEset,
contrast,
savefile=F,
names=names,
log.convert = log.convert,
cluster_cols =cluster_cols)
print("完成QC.cluster")
##Boxplot QC
bo <- QC.boxplot(DesignEset,
contrast,
savefile=F,
names=names)
print("完成QC.boxplot")
##Density QC:
ds <- QC.density(DesignEset,
contrast,
savefile=F,
names=names)
print("完成QC.density")
##PCA QC
pca <- QC.PCA(DesignEset,
contrast,
savefile=F,
names=names)
print("完成QC.PCA")
if(savefile==F){
print("完成QCplots展示。")
} else {
##保存为PDF格式
p1 <- c(ma,hm,bo,ds,pca)
pdf(str_c(names,"_QCplot.pdf"),width=9,height=8)
for(i in 1:length(p1)){
p.i <- p1[[i]]
print(p.i)
}
dev.off()
print(str_c(names,"_QCPlot.pdf已保存在本地"))
}
}
# QCplot(DesignEset,
# contrast,
# names="test1",
# savefile=F)
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