质控三张图 gaochao ards 多个表型写成循环


#差异分析三张图#组别
gp = as.factor(phe_info$sex)
gp
table(gp)  #倒置矩阵
str(exprSet)
exprSet=data.frame(exprSet)
head(exprSet)[1:5,1:10]
dat.pca= t(exprSet  )   #取的是基因不是样本
head(dat.pca)[1:5,1:10]#pca
??fviz_pca_ind
library("FactoMineR")
library(factoextra)dat.pca <- PCA(dat.pca , graph = FALSE)
head(dat.pca$var$coord);dim(dat.pca$var$coord)
p1=fviz_pca_ind(dat.pca,geom.ind = "point", # show points only (nbut not "text")col.ind = gp, # color by groups# alette = c("#00AFBB", "#E7B800"),addEllipses = TRUE, # Concentration ellipseslegend.title = "Sex",#title = 'Paired Samples',ggtheme = theme_minimal()
)
p1
#ggsave(p1,filename = paste0(pro,'_','output/plot/step1.pca-surv.pdf'))#尝试写成循环
head(phe_info)
ftp = as.data.frame(colnames(phe_info)[2:3])n2=phe_info
n2 = n2[2:3] #第11列是na
groupl = colnames(n2)
dat.pca <- PCA(t(exprSet) , graph = FALSE)library("FactoMineR")
library("factoextra") pca_list <- lapply(groupl, function(i){#i = groupl[3]gp = n2[,i]gp = as.factor(gp)p1=fviz_pca_ind(dat.pca, geom.ind = "point", # show points only (nbut not "text")col.ind = gp, # color by groups# alette = c("#00AFBB", "#E7B800"),addEllipses = TRUE, # Concentration ellipseslegend.title = 'group',title = i,ggtheme = theme_minimal())}) #拼图
library(patchwork)
p2 = wrap_plots(pca_list,nrow=2)
p2
ggsave(p2,filename = 'output/plot/step1.pca.png',width = 20,height = 10,limitsize = FALSE)


getwd()dir.create("D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/gaochao")
setwd("D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/gaochao")
getwd()myexprset=read.table(file = "D:/Win10 System/Documents/WeChat Files/wxid_f27yna03e0v622/FileStorage/File/2022-10/GSE154918_Schughart_Sepsis_200320.txt",header = T)
head(myexprset)library(dplyr)
exprSet=myexprset  %>%select( c(which(colnames(myexprset)=="gene_symbol"),which(colnames(myexprset)=='INFG_139') :ncol(myexprset)  ))  %>%  select("gene_symbol",everything())
head(exprSet)
dim(exprSet)
length(unique(exprSet$gene_symbol))library(tibble)
exprSet=column_to_rownames(exprSet,var = "gene_symbol")
head(exprSet)
dim(exprSet)grep("_",colnames(exprSet))
library(stringr)
str_split(colnames(exprSet),pattern = "_",simplify = T)[,1]
table(str_split(colnames(exprSet),pattern = "_",simplify = T)[,1])boxplot(exprSet,las=2)getwd()
??read.xlsx
phe_info=openxlsx::read.xlsx("D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/gaochao/ST3_Patients_characteristics_080221.xlsx",colNames = T)
head(phe_info)
library(tibble)
colnames(phe_info)=phe_info[1,]
rownames(phe_info)=phe_info$sample_ID
phe_info=phe_info[-1,]
head(phe_info)
dim(phe_info)getwd()
identical(colnames(exprSet),rownames(phe_info))
#save(exprSet,phe_info, file = "D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/gaochao/step1_GSE154918.RData")
load("D:/ARDS_scripts_1012/ARDS/Step2_harmony_f200_R3/gaochao/step1_GSE154918.RData")boxplot(exprSet["METTL3",],las=2)#差异分析三张图#组别
gp = as.factor(phe_info$sex)
gp
table(gp)  #倒置矩阵
str(exprSet)
exprSet=data.frame(exprSet)
head(exprSet)[1:5,1:10]
dat.pca= t(exprSet  )   #取的是基因不是样本
head(dat.pca)[1:5,1:10]#pca
??fviz_pca_ind
library("FactoMineR")
library(factoextra)dat.pca <- PCA(dat.pca , graph = FALSE)
head(dat.pca$var$coord);dim(dat.pca$var$coord)
p1=fviz_pca_ind(dat.pca,geom.ind = "point", # show points only (nbut not "text")col.ind = gp, # color by groups# alette = c("#00AFBB", "#E7B800"),addEllipses = TRUE, # Concentration ellipseslegend.title = "Sex",#title = 'Paired Samples',ggtheme = theme_minimal()
)
p1
#ggsave(p1,filename = paste0(pro,'_','output/plot/step1.pca-surv.pdf'))#尝试写成循环
head(phe_info)
ftp = as.data.frame(colnames(phe_info)[2:3])n2=phe_info
n2 = n2[2:3] #第11列是na
groupl = colnames(n2)
dat.pca <- PCA(t(exprSet) , graph = FALSE)library("FactoMineR")
library("factoextra") pca_list <- lapply(groupl, function(i){#i = groupl[3]gp = n2[,i]gp = as.factor(gp)p1=fviz_pca_ind(dat.pca, geom.ind = "point", # show points only (nbut not "text")col.ind = gp, # color by groups# alette = c("#00AFBB", "#E7B800"),addEllipses = TRUE, # Concentration ellipseslegend.title = 'group',title = i,ggtheme = theme_minimal())}) #拼图
library(patchwork)
p2 = wrap_plots(pca_list,nrow=2)
p2
ggsave(p2,filename = 'output/plot/step1.pca.png',width = 20,height = 10,limitsize = FALSE)

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