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proteomics_library_linda.R
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# pacman::p_load(readxl,magrittr,
# RColorBrewer,
# Rtsne, umap,ggthemes,
# pheatmap,vioplot,
# ggpubr, ggplot2,
# corrplot, stringr,
# barplot)
#################################################################matrix process#################################################################
linda.summary.matrix <- function(matrix){
cat("Sample size: ",
ncol(matrix),
"\nNumber of protein: ",
nrow(matrix),
"\nMissing rate: ",
round(sum(is.na(matrix))/dim(matrix)[1]/dim(matrix)[2],4),
"\nMaximum value: ",
round(fivenum(as.matrix(matrix))[5],4),
"\nMinimum value: ",
round(fivenum(as.matrix(matrix))[1],4),
"\nIQR: ",
round(IQR(as.matrix(matrix),na.rm = T),4)
)
}
####################################################read data###############################
ge.read <- function(data,colname = T){
data=input
suffix <- strsplit(data,"\\.")[[1]][length(strsplit(data,"\\.")[[1]])]
data2 <- switch(suffix,
txt=read.table(data,sep = "\t",header = colname,stringsAsFactors = F),
csv=read.csv(data,header = colname),
tsv=read_tsv(data,col_names = colname),
xlsx=read_xlsx(data,col_names = colname)
)
return(data2)
}
############################remove complete NA rows or column####################
linda.removeblanksamples <- function(matrix){
newR <- matrix[apply(matrix, 1, function(x) {sum(!is.na(x))>0}),];
newC <- newR[,apply(newR, 2, function(x) {sum(!is.na(x))>0})];
return(newC)
}
#####################################clear DIA/DDA matrix##################################
linda.ClearRawmat <- function(matrix){
newR <- matrix[apply(matrix, 1, function(x) {sum(!is.na(x))>0}),];
newC <- newR[,apply(newR, 2, function(x) {sum(!is.na(x))>0})];
Dat<- newC[!grepl(pattern = ";",newC[,1]),]
Dat<- Dat[!grepl(pattern = "^SWISS|^TRE|^H-|ENSEMBL",Dat[,1]),]
Dat<- data.frame(Dat[,-1],row.names=Dat[,1])
}
##############################################################0-1标准化##############################################################
norm.mat <- function(matrix){
apply(matrix, 1, function(x){
(x-min(x,na.rm=T))/(max(x,na.rm = T)-min(x,na.rm=T))
})
}
#################################split string#######################################################################
linda.split <- function(string,
pattern="_",
character_pos=1
){
as.character(lapply(string, function(x) {str_split(x,pattern)[[1]][character_pos]}))
}
#############################################color###########################################################################
ge.color <- function(n){
if(n<10){
set.seed(21)
m <- sample(9,n)
color <- brewer.pal(9,"Set1")[m]
}else{
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))#74种颜色
set.seed(10)
color=sample(col_vector, n)
}
return(color)
}
##################################################################QC--densityplot######################################################
linda.plot.density <- function(data,outlier=F){
if(!outlier){
plot(density(na.omit(unlist(data))),main="density default",mgp = c(2, 0.7, 0),cex.lab=0.9,xlab = "Abundance") #多个type ggplot?
}else{
df2.matrix <- as.matrix(data)
outline1 <- (fivenum(df2.matrix)[4]-fivenum(df2.matrix)[2])*2+fivenum(df2.matrix)[4]
outline2 <- fivenum(df2.matrix)[2]-(fivenum(df2.matrix)[4]-fivenum(df2.matrix)[2])*2
data[data>outline1] <- outline1
data[data<outline2] <- outline2
plot(density(na.omit(unlist(data))),main="density default",mgp = c(2, 0.7, 0),cex.lab=0.9,xlab = "Abundance")
}
}
##########################################################barplot count proteins of each sample#######################################################################################
linda.samplefile.statistic <- function(matrix,cex.axis = 1.5,label =F,col = brewer.pal(12,"Set3")[1]){
df_sample3=matrix
count <- apply(df_sample3, 2, function(x) sum(!is.na(x)))
count_sample <- data.frame(file=names(df_sample3),proteins=count)
write.csv(count_sample,file = "count_sample.csv",row.names = F)
data <- count_sample[order(count_sample[,2]),]
# pdf("file_proteins_statistic.pdf",width = 30,height = 10)
# barplot(data$proteins,
# names.arg = data$file,cex.names = 0.8,
# xlab = "sample",ylab = "protein number",
# cex.axis = cex.axis,
# col = col
# # main = "Number of proteins in each sample"
# )
# title(main = "Number of proteins in each sample", font.main = 15)
# dev.off()
p<- ggbarplot(data,
x = "file",
y = "proteins",
xlab ="sample",
width = 0.8,
ylab = "protein number",
fill = col, # 分组调整填充颜色
color = "black", # 轮廓颜色为白色
palette =col,
label = label, #显示每个bar的数值
title = "Number of proteins in each sample"
# sort.val = "asc", # 按升序对值进行排序
# sort.by.groups = T, # 组内进行排序
#x.text.angle = 90 # 旋转垂直x轴文本
)+theme(axis.title.x = element_text(size = 15),axis.title.y = element_text(size = 15))
# + theme(axis.text.x = element_blank()
ggsave("file.stat.pdf",plot = p,width = 7,height = 5)
}
linda.samplefile.statbardot <- function(matrix,cex.axis = 1.5,label =NULL,col = c("#377EB8","#984EA3","#A65628"),
dot.size = "missing",
shape = 1,
font.label=list(size = 7, color = "black",vjust=0.5),
legend.position = "none",
plot.width = 10,plot.height = 7
){
count <- apply(matrix, 2, function(x) sum(!is.na(x)))
na_ratio <- apply(matrix, 2, function(x) sum(!is.na(x))/length(x))
count_sample <- data.frame(file=names(matrix),proteins=count,missing=na_ratio)
data <- count_sample[order(count_sample[,2]),]
data$batch <- str_extract(data$file,"[Fb][0-9]*")
write.csv(count_sample,file = "count_sample.csv",row.names = F)
p <- ggdotchart(data,x="file",y="proteins",color = "batch",
palette = col,
sorting = "desc",#组别内降序排列
add = "segment",
add.params = list(color = "batch",size=1),#线段的颜色
group = "batch",
#dot.size = 8,
dot.size = dot.size,
shape = shape,
label = label,#dot的标签data$proteins
#label.rectangle=T,
font.label =font.label,#dot的标签颜色大小距离dot的调整距离等
xlab = "",ylab = "Protein number",
ggtheme = theme_pubr()
)+
theme(axis.title.y = element_text(face = 'bold',color = 'black',size = 15),
#axis.title.x = element_text(face = 'bold',color = 'black',size = 10),
axis.text.y = element_text(color = 'black',size = 12),
axis.text.x = element_text(color = 'black',size = 12,angle = 80,vjust = 1), #x轴标签偏转45°，并下降0.1
# axis.text.x = element_blank(),
#axis.line = element_line(colour = "black"),
panel.grid = element_blank(),
#panel.background = element_blank(),
#legend.text = element_text(face = 'bold',color = 'black',size = 10)),
legend.position = legend.position
)
ggsave("file.stat_bardotplot.pdf",p,width = plot.width,height = plot.height)
}
###########################################################pool correlation corrplot############################################################################
linda.pool_corrplot <- function(df_pool=df_pool,title=paste0("pool","_","correlation.pdf"),
r.method = "pearson",#alternative:"kendall", "spearman"
plot.method = "circle",#alternative:square', 'ellipse', 'number', 'pie', 'shade' and 'color'
plot.type="lower",#c("full", "lower", "upper")
color=c(brewer.pal(11,'BrBG')[10:7],"azure1",brewer.pal(11,'BrBG')[5:2]),
corr.bool=TRUE,
corr.color="grey",
tl.col = "black",
tl.cex = 0.5,
cl.cex = 0.6,
cl.ratio = 0.1,
number.cex = 1.5
){
pdf(title,width = 10,height = 10)
corrplot(cor(df_pool,method = r.method,use = "pairwise.complete.obs"),
method = plot.method,
type = plot.type,
col = color,
is.corr = corr.bool,
addCoef.col = corr.color,
tl.col = tl.col,
tl.cex = tl.cex,
cl.cex = cl.cex,
cl.ratio = cl.ratio,
number.cex = number.cex,
mar=c(2,0,5,2))
dev.off()
}
###########################################################bioreplivate CV violin############################################################################
linda.violin_biorep.cv <- function(matrix,type,col=col,title=title,width = 8, height = 8){
data=matrix
tmp <- data.frame(t(data))
tmp1 <- tmp[,apply(tmp, 2, function(x) {sum(!is.na(x))>0})]
tmp1 <- data.frame(group=type,tmp1)
tmp.cv <- aggregate(tmp1[,-c(1)],by = list(tmp1$group),FUN = function(x) {sd(x,na.rm = T)/mean(x,na.rm =T)})
box.cv <- melt(tmp.cv,measure.vars = names(tmp.cv)[-1],variable.name = "protein",value.name = "cv")
median <- round(tapply(box.cv$cv,box.cv$Group.1,median,na.rm=T),3)
box.cv$mid <- median[match(box.cv$Group.1,names(median))]
p <- ggplot(box.cv,aes(x = Group.1, y=cv,color= Group.1)) +
geom_violin(trim=F)+
ylab("Coefficient of variation")+
scale_fill_manual(values=col)+
geom_boxplot(width=0.2)+
xlab("")+
# stat_compare_means(method = "wilcox.test",comparisons = my_comparisons)+
# stat_compare_means(label.y = 1.5,label.x = 2.2)+
#coord_trans(x = "identity", y = "identity", xlim = NULL, ylim = c(0,0.8))+
geom_hline(yintercept = 0.1,linetype=2,show.legend = T)+
theme(legend.direction = 'horizontal',legend.position = 'top',panel.grid.major =element_blank(), panel.grid.minor = element_blank(),panel.background = element_blank(),axis.line = element_line(colour = "black"))+
theme(axis.text = element_text(size = 15,colour = "black"),text = element_text(size = 15,colour = "black"))+
theme(axis.text.x = element_text(hjust = 1,angle = 45))+
theme(legend.position = "none")+ #reomove legend
theme(plot.margin = unit(rep(2,4),'lines'))+ #调整边缘距离
geom_text(aes(y=0.6,label=paste0("Median=",box.cv$mid)),hjust=0.5,vjust=1,size=5) #调整文字位置
ggsave(paste0(title,"_violin_CV.pdf"),plot =p ,device = NULL,width = width, height = height)
}
###########################################################bioreplivate CV half_violin###########################################################
linda.halfviolin_biorep.cv <- function(matrix,#待计算的全距阵
info,#File.Name列（matrix的列名）和Biology_replicate列(sample name名）是必须的
dup,#生物重复的sample名，非重复非实验名
col,title,width = 8, height = 8){
bio.cv <- c()
Bio_CV_Median <- c()
violin.sum <- c()
for (i in dup) {
tmp <-
matrix[, which(names(matrix) %in% info$File.Name[which(info$Biology_replicate== i)])]
tmp.cv <- apply(tmp, 1 , function(x) {
# sd(x) / mean(x)
sd(x,na.rm = T) / mean(x,na.rm=T)
})
bio.cv <- c(bio.cv,tmp.cv)
Bio_CV_Median <- c(Bio_CV_Median,median(bio.cv,na.rm = T))
}
violin.sum <- rbind(violin.sum,data.frame(Group=rep(dup,each=nrow(matrix)),cv=as.numeric(bio.cv),median=rep(Bio_CV_Median,each=nrow(matrix))))
stat_data <- violin.sum[,-2] %>% unique()
p <- ggplot(violin.sum,aes(x = Group, y=cv,fill= Group)) +
geom_half_violin(position = position_nudge(x = 0.2),side=2,alpha = 0.8)+
scale_fill_manual(values=col)+
geom_jitter(aes(y=cv, color = Group),
position = position_jitter(width = 0.12),#鎵�鏈夌偣鍗犵殑瀹藉害
size = 0.2,#鐐圭殑澶у皬
alpha = 0.5)+
scale_color_manual(values=col)+
geom_boxplot(width=0.05,outlier.shape = NA,fill="white",size=0.5,position=position_nudge(x=0.2))+
geom_text(data=stat_data,aes(y=median-0.02,x=Group,label=paste0("Median=",round(median,3)),
vjust = 0.5, #绔栫洿涓婁笅璋?
hjust = 0.1#姘村钩宸﹀彸璋冩暣
),size=5)+
ylab("Coefficient of variation")+
xlab("")+
coord_flip()+
theme_bw()+ #鍔犲洓杈规
theme(panel.grid =element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
theme(axis.text = element_text(size = 12,colour = "black"),
text = element_text(size = 10,colour = "black"),#璋冩暣label text鐨勫瓧浣撳ぇ灏?
axis.title.x = element_text(size = 15,colour = "black")
)+
theme(legend.position = "none")+ #reomove legend
theme(plot.margin = unit(rep(2,4),'lines')) #璋冩暣杈圭紭璺濈
ggsave(paste0(title,"_violin_CV.pdf"),plot =p ,device = NULL,width = width, height = height)
}
######################################################correlation smooth scatter density plot#########################################
library(ggpointdensity)
library(viridis) #色系
linda.techcor.scatter <- function(corrdata,method.cor="pearson",point.viridiscolor="D",smooth.line="lm",smooth.col="#e13c50",text.size=7,title="",xlab="rep1",ylab="rep2"){
cor1 = corrdata[,1]
cor2 = corrdata[,2]
r=cor.test(cor1,cor2,method = method.cor)
p <- ggplot(corrdata,aes(get(names(corrdata)[1]),get(names(corrdata)[2])))+
geom_pointdensity(adjust=0.2,
#size = 3
#shape = 17
)+
scale_color_viridis(option = point.viridiscolor)+
geom_smooth(method = smooth.line,col=smooth.col,lty=2,bg="white")+
geom_text(aes(x=min(cor1,na.rm = T)+0.5,y=max(cor2,na.rm = T)-0.5,label=paste0("r: ",round(r$estimate,2))),size = text.size)+
geom_text(aes(x=min(cor1,na.rm = T)+0.5,y=max(cor2,na.rm = T)-0.8,label=paste0("pvalue: ",round(r$p.value,2))),size = text.size)+
theme_base()+
theme(legend.position = "none")+
xlab(xlab)+
ylab(ylab)
ggsave(paste0(title,"Correlation.scatter_density.pdf"),plot = p)
}
###########################################################pca############################################################################
ge.plot.pca <- function(data,type,title="",ptColors=NULL,label2=NULL,width=12,height=8){
M <- t(data)
M[is.na(M)] <- 0
M <- apply(M,2,function(v){(v-mean(v,na.rm=T))/sd(v,na.rm=T)})
clnames <- row.names(data)
set.seed(200)
m1 <- prcomp(M);
Y <- scale(M, m1$center, m1$scale) %*% m1$rotation
Y <- Y[,c(1,2)]
Y <- data.frame(Y,type);
colnames(Y) <- c("PC1","PC2","label")
eigs <- m1$sdev^2
percentages <- eigs[1:2] / sum(eigs)
p <- ggplot(Y, aes(x=PC1, y=PC2, colour=label)) + geom_point(size=4)
p <- p + theme( panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_text(size = 30,color = "black"),
panel.border = element_blank(),
axis.line.x = element_line(color="black", size = 0.25),
axis.line.y = element_line(color="black", size = 0.25),
plot.title = element_text(size=30),
axis.title =element_text(size=30),
panel.background = element_blank())
strLabx <- sprintf("PC1(%4.2f%%)",percentages[1]*100)
p <- p + labs(x =strLabx,y = sprintf("PC2(%4.2f%%)",percentages[2]*100),
title =sprintf("PCA:%d features",length(clnames)))
if(!is.null(ptColors)){
p <- p + scale_colour_manual(values=ptColors)
}
if(!is.null(label2)){
p <- p + geom_text(aes(label=label2,vjust = -0.8, hjust = 0.5,size=0.5),show.legend = FALSE)
}
ggsave(paste0(title,"_PCA.pdf"),plot =p ,width=width,height=height,device = NULL)
}
################################################################umap######################################################################
library(umap)
ge.plot.umap<- function(data,type,label1,batch_color=batch_color,title="",label=NULL){
# col2=ge.color(length(unique(type)))
col2= batch_color[c(1:length(unique(type)))]
cl <- data.frame(col2,row.names =unique(type),stringsAsFactors = F)
cl2 <- cl[match(type,row.names(cl)),1]
df10 <- data
df10[is.na(df10)] <- 0
df10 <- t(apply(df10, 1, scale))
colnames(df10) <- label1
set.seed(10)
df.umap <- umap(t(df10),n_neighbors=ncol(data)-1)
umap_data <- df.umap$layout
colnames(umap_data) <- c("umap1","umap2")
pdf(paste0(title,"_UMAP.pdf"))
if(is.null(label)){
# plot(df.umap$layout,col = cl2, main = "umap", pch = 20,cex=2,cex.axis=2,cex.lab=2)
plot(umap_data,col = cl2, main = "umap", pch = 20,cex=2,cex.axis=2,cex.lab=2)
legend("topright",legend=row.names(cl), fill=cl$col2, lty=1,lwd=1)
}else{
# plot(df.umap$layout,col = cl2, main = "umap", pch = 20,cex=2,cex.axis=2,cex.lab=2)
plot(umap_data,col = cl2, main = "umap", pch = 20,cex=2,cex.axis=2,cex.lab=2)
text(umap_data, pos =2, labels = label, col= "DimGrey",cex = 0.4)
legend("topright",legend=row.names(cl), fill=cl$col2, lty=1,lwd=1)
}
dev.off()
return(df.umap)
}
# #################################################################计算T-test & foldchange##################################################
linda.Ttest <- function(matrix,
group1,#postion number of group1 samples in clumns
group2,#postion number of group2 samples in clumns
paired_bool = F,
var.equal_bool=F,
fold_change=1.5,
pvalue=0.05,
title=title){
df1 <- as.data.frame(matrix)
df1$`log2(foldchange)` <- as.numeric(apply(df1,1, function(x) log2(abs((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))))
##可能会有负数
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = paired_bool,
var.equal = var.equal_bool)$p.value,
error = function(x) NA)
})
#给data增加p-value列
df1$P_value<- P_value
df1$P_value_adjust<-p.adjust(df1$P_value, method="BH")
up <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` > log2(fold_change))
down <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` < -log2(fold_change))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(title,"_upPvalue0.05_volcano.csv"))
write.csv(down,file = paste0(title,"_dwPvalue0.05_volcano.csv"))
write.csv(df1,file = paste0(title, "_all_volcano.csv"))
}
# #################################################################不填充NA的t-TEST多################################################################################
linda.volcano.plot <- function(data,#data.frame
logtransformation=T,
group1,
group2,
label1,
label2,
paired = F,
pvalue= 0.05,
P.adjust=T,
var.equal = F,
fold_change = 2,
up_col="#EC9B8F", #dw_col = "#9370DB",up_col = "#FF7256"
dw_col="#A7DEE8"){
df1 <- as.data.frame(data)
#填充NA
#df1[is.na(df1)]=min(df1,na.rm = T)*0.8
#计算foldchange
if(logtransformation){
df1$`log2(foldchange)` <- apply(2^df1,1, function(x) log2((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))
}else{
df1$`log2(foldchange)` <- apply(df1,1, function(x) log2((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))
}
if(paired){
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = paired,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}else{
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = F,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}
#给data增加p-value列
df1$P_value<- P_value
df1$P_value_adjust<-p.adjust(df1$P_value, method="BH")
if(P.adjust){
#存储上调和下调蛋白信息
up <- subset(df1, df1$P_value_adjust < pvalue & df1$`log2(foldchange)` > log2(fold_change))
down <- subset(df1, df1$P_value_adjust < pvalue & df1$`log2(foldchange)` < -log2(fold_change))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- c(row.names(up),row.names(down))
#画图
pdf(paste0(label1,"_",label2, "_volcano.pdf"),width=4, height=4)
plot(df1$`log2(foldchange)`, -log10(df1$P_value_adjust), col="#00000033", pch=19,
xlab=paste("log2 (fold change)"),
ylab="-log10 (adjust P value)",
xlim= c(-round(max(abs(df1$`log2(foldchange)`),na.rm=T),1),round(max(abs(df1$`log2(foldchange)`),na.rm=T),1)),#na.rm=T只能过滤NA值如果有-lnf等也不可以计算最值
main=paste0(label1," : ",label2))
points(up$`log2(foldchange)`, -log10(up$P_value_adjust), col=1,
#bg = brewer.pal(9, "YlOrRd")[6],
bg=up_col,
pch=21, cex=1)
points(down$`log2(foldchange)`, -log10(down$P_value_adjust), col = 1,
#bg = brewer.pal(11,"RdBu")[9],
bg=dw_col,
pch = 21,cex=1)
abline(h=-log10(pvalue), v=c(-log2(fold_change),log2(fold_change)), lty=2,lwd=1)
dev.off()}else{
#存储上调和下调蛋白信息
up <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` > log2(fold_change))
down <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` < -log2(fold_change))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- c(row.names(up),row.names(down))
#画图
pdf(paste0(label1,"_",label2, "_volcano.pdf"),width=4, height=4)
plot(df1$`log2(foldchange)`, -log10(df1$P_value), col="#00000033", pch=19,
xlab=paste("log2 (fold change)"),
ylab="-log10 (P value)",xlim=c(-round(max(abs(df1$`log2(foldchange)`),na.rm=T),1),round(max(abs(df1$`log2(foldchange)`),na.rm=T),1)),
main=paste0(label1," : ",label2))
points(up$`log2(foldchange)`, -log10(up$P_value), col=1,
# bg = brewer.pal(9, "YlOrRd")[6],
bg=up_col,
pch=21, cex=1.2)
points(down$`log2(foldchange)`, -log10(down$P_value), col = 1,
# bg = brewer.pal(11,"RdBu")[9],
bg=dw_col,
pch = 21,cex=1.2)
abline(h=-log10(pvalue), v=c(-log2(fold_change),log2(fold_change)), lty=2,lwd=1)
dev.off()
}
return(differprot)
}
#############################################label volcano##########################################
linda.volcano.plot2 <- function(data,#data.frame
logtransformation=F,
group1,
group2,
label1,
label2,
paired = F,
pvalue= 0.05,
P.adjust=T,
var.equal = F,
fold_change = 2){
df1 <- as.data.frame(data)
if(logtransformation){
df1$`log2(foldchange)` <- apply(2^df1,1, function(x) log2((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))
}else{
df1$`log2(foldchange)` <- apply(df1,1, function(x) log2((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))
}
if(paired){
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = paired,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}else{
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = F,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}
#给data增加p-value列
df1$P_value<- P_value
df1$P_value_adjust<-p.adjust(df1$P_value, method="BH")
if(P.adjust){
#存储上调和下调蛋白信息
up <- subset(df1, df1$P_value_adjust < pvalue & df1$`log2(foldchange)` > log2(fold_change))
down <- subset(df1, df1$P_value_adjust < pvalue & df1$`log2(foldchange)` < -log2(fold_change))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- c(row.names(up),row.names(down))
#画图
pdf(paste0(label1,"_",label2, "_volcano.pdf"),width=4, height=4)
plot(df1$`log2(foldchange)`, -log10(df1$P_value_adjust), col="#00000033", pch=19,cex=0.5,
xlab=paste("log2 (fold change)"),
ylab="-log10 (adjust P value)",
xlim= c(-round(max(abs(df1$`log2(foldchange)`),na.rm=T),1),#na.rm=T只能过滤NA值如果log2(foldchange)有非法字符如-lnf等也不可以计算最值
round(max(abs(df1$`log2(foldchange)`),na.rm=T),1)),
main=paste0(label1," : ",label2))
points(up$`log2(foldchange)`, -log10(up$P_value_adjust), col="#FFA500", bg = "#FFA500", pch=21,cex=0.6)
points(down$`log2(foldchange)`, -log10(down$P_value_adjust), col = "#32CD32", bg = "#32CD32", pch = 21,cex=0.6)
abline(h=-log10(pvalue), v=c(-log2(fold_change),log2(fold_change)), lty=2,lwd=1)
if(length(row.names(down))!=0 & length(row.names(up))!=0){
text(down$`log2(foldchange)`, -log10(down$P_value_adjust),labels= row.names(down),adj = c(1,1),cex = 0.2)
text(up$`log2(foldchange)`, -log10(up$P_value_adjust),labels= row.names(up),adj = c(1,1),cex = 0.2)
}else if(length(row.names(down))==0 & length(row.names(up))!=0){
text(up$`log2(foldchange)`, -log10(up$P_value_adjust),labels= row.names(up),adj = c(1,1),cex = 0.2)
}else if(length(row.names(down))!=0 & length(row.names(up))==0){
text(down$`log2(foldchange)`, -log10(down$P_value_adjust),labels= row.names(down),adj = c(1,1),cex = 0.2)
}
dev.off()}else{
#存储上调和下调蛋白信息
up <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` > log2(fold_change))
down <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` < -log2(fold_change))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- c(row.names(up),row.names(down))
#画图
pdf(paste0(label1,"_",label2, "_volcano.pdf"),width=4, height=4)
plot(df1$`log2(foldchange)`, -log10(df1$P_value), col="#00000033", pch=19,cex=0.5,
xlab=paste("log2 (fold change)"),
ylab="-log10 (P value)",xlim=c(-round(max(abs(df1$`log2(foldchange)`),na.rm=T),1),round(max(abs(df1$`log2(foldchange)`),na.rm=T),1)),
main=paste0(label1," : ",label2))
points(up$`log2(foldchange)`, -log10(up$P_value), col="#FFA500", bg = "#FFA500", pch=21, cex=0.6)
points(down$`log2(foldchange)`, -log10(down$P_value), col = "#32CD32", bg = "#32CD32" , pch = 21,cex=0.6)
abline(h=-log10(pvalue), v=c(-log2(fold_change),log2(fold_change)), lty=2,lwd=1)
if(length(row.names(down))!=0 & length(row.names(up))!=0){
text(down$`log2(foldchange)`, -log10(down$P_value),labels= row.names(down),adj = c(1,1),cex = 0.2)
text(up$`log2(foldchange)`, -log10(up$P_value),labels= row.names(up),adj = c(1,1),cex = 0.2)
}else if(length(row.names(down))==0 & length(row.names(up))!=0){
text(up$`log2(foldchange)`, -log10(up$P_value),labels= row.names(up),adj = c(1,1),cex = 0.2)
}else if(length(row.names(down))!=0 & length(row.names(up))==0){
text(down$`log2(foldchange)`, -log10(down$P_value),labels= row.names(down),adj = c(1,1),cex = 0.2)
}
dev.off()
}
return(differprot)
}
##############################renew: solve the problem of xlim infinite plot by ggpubr and ggplot2#################################################
linda.volcano.plot3 <- function(data,#data.frame
logtransformation=F,
group1,
group2,
label1,
label2,
paired = F,
pvalue= 0.05,
P.adjust=T,
var.equal = F,
fold_change = 2,
select_gene = NULL,
palette = c("#ff7e00","#00beff","#00000033") #修改颜色
){
data <- data[,c(group1,group2)]
data <- data[apply(data, 1, function(x){sum(!is.na(x))})>0,]
df1 <- as.data.frame(data)
if(logtransformation){
df1$`log2(foldchange)` <- apply(2^df1,1, function(x) log2((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))
}else{
df1$`log2(foldchange)` <- apply(df1,1, function(x) log2((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))
}
if(paired){
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = paired,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}else{
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = F,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}
#给data增加p-value列
df1$P_value<- P_value
df1$P_value_adjust<- p.adjust(df1$P_value, method="BH")
if(P.adjust){
df1$P <- df1$P_value_adjust
y.lab <- "-log10 (adjust P_value)"
}else{
df1$P <- df1$P_value
y.lab <- "-log10 (unadjust P_value)"
}
df1$Class = factor(ifelse(df1$P < pvalue & (df1$`log2(foldchange)` > log2(fold_change) | df1$`log2(foldchange)` < -log2(fold_change)),ifelse(df1$`log2(foldchange)` > log2(fold_change),'Up','Down'),'NoSignifi'),levels=c('Up','Down','NoSignifi'))
df1$pro <- rownames(df1)
up <- subset(df1, df1$P < pvalue & df1$`log2(foldchange)` > log2(fold_change))
down <- subset(df1, df1$P < pvalue & df1$`log2(foldchange)` < -log2(fold_change))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- c(row.names(up),row.names(down))
df1$`-log10(P)` <- -log10(df1$P)
# if(!is.null(select_gene)) {
# df1$sign <- ifelse(rownames(df1)%in%select_gene,rownames(df1)[rownames(df1)%in%select_gene],NA)
# }
# else {df1$sign <- NA}
df1$sign <- NA
df1$sign[which(rownames(df1)%in%as.character(select_gene))] <- as.character(select_gene)
p <- ggscatter(df1,x= "log2(foldchange)",y="-log10(P)",
xlab = "log2 (foldchange)",
ylab = y.lab,
color="Class",
palette = palette, #修改颜色
label = "sign",repel = T, #显示每个点表示的样本名
#ellipse = F, #分组圈图
font.label = c(10, "bold","black"),#label的样式,如果不指定颜色则默认用点的颜色代替
shape = 19,
#size = 1.5,
size = 2.5,
show.legend.text = F,
# title="DLE vs. NC" #不知道怎么调大小和位置
title=paste(label1,"vs.",label2)
)+
geom_vline(xintercept = c(-log2(fold_change),log2(fold_change)),linetype=2)+
geom_hline(yintercept = -log10(pvalue),linetype=2)+
geom_text(aes(x=-max(df1$`log2(foldchange)`[is.finite(df1$`log2(foldchange)`)])/2,y=2,label=paste0("down: ",sum(na.omit(df1$`log2(foldchange)`< (-log2(fold_change)) & df1$`-log10(P)`> (-log10(pvalue)))))),size = 5)+
geom_text(aes(x=max(df1$`log2(foldchange)`[is.finite(df1$`log2(foldchange)`)])/2,y=2,label=paste0("up: ",sum(na.omit(df1$`log2(foldchange)`> (log2(fold_change)) & df1$`-log10(P)`> (-log10(pvalue)))))),size = 5)+
theme(axis.title.x =element_text(size=15), axis.title.y=element_text(size=15),
axis.text = element_text(colour ="black", size = 10),
axis.line.x = element_line(size = 0.5),
axis.line.y = element_line(size = 0.5),
legend.position = "none",
panel.background = element_blank(),
panel.grid =element_blank(),
axis.ticks = element_blank())
# theme_base()#调整legend位置在图的右侧
ggsave(paste0(label1,"_",label2, "_volcano_id.pdf"),plot =p,device = NULL)
return(differprot)
}
################label
# library(ggrepel)
tan.volcano.plot <- function(data,#data.frame
group1,
group2,
label1,
label2,
paired = F,
pvalue= 0.05,
P.adjust= F,
var.equal = F,
select_gene = NULL,
fold_change1 = 1.2,
fold_change2 = 1.2,
label_size = 3){
df1 <- data
#濉厖NA
df1[is.na(df1)]=min(df1,na.rm = T)
#璁＄畻foldchange
df1$`log2(foldchange)` <- apply(df1,1, function(x) log2((mean(x[group1],na.rm = T)/mean(x[group2],na.rm = T))))
if(paired){
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = paired,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}else{
P_value <- apply(df1,
1,
function(y) {
p_try = tryCatch(t.test(y[group1],
y[group2],
paired = F,
var.equal = var.equal)$p.value,
error = function(x) NA)
})
}
#缁檇ata澧炲姞p-value鍒?
df1$P_value<- P_value
df1$P_value_adjust<-p.adjust(df1$P_value, method="BH")
if(P.adjust){
df1$threshold = factor(ifelse(df1$P_value_adjust < pvalue & (df1$`log2(foldchange)` > log2(fold_change1) | df1$`log2(foldchange)` < -log2(fold_change2)),ifelse(df1$`log2(foldchange)` > log2(fold_change1),'Up','Down'),'NoSignifi'),levels=c('Up','Down','NoSignifi'))
df1$pro <- rownames(df1)
up <- subset(df1, df1$P_value_adjust < pvalue & df1$`log2(foldchange)` > log2(fold_change1))
down <- subset(df1, df1$P_value_adjust < pvalue & df1$`log2(foldchange)` < -log2(fold_change2))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- list(up=row.names(up),down=row.names(down))
if(!is.null(select_gene)) {data_text = df1[select_gene,]}
else {data_text = subset(df1, P_value_adjust < pvalue & (df1$`log2(foldchange)` > log2(fold_change1) | df1$`log2(foldchange)` < -log2(fold_change2)))}
pp <- ggplot(df1,aes(x=`log2(foldchange)`,y=-log10(P_value_adjust),color=threshold))+
geom_point()+
scale_color_manual(values=c('Up'="#DC143C",'Down'="#0A9731",'NoSignifi'="#808080"))+#纭畾鐐圭殑棰滆壊
geom_text_repel(
# data = subset(df1, P_value_adjust < pvalue & (df1$`log2(foldchange)` > log2(fold_change1) | df1$`log2(foldchange)` < -log2(fold_change2))),
data = data_text,
aes(label = pro),
colour = "black", ###鏍囨敞鍑烘潵鐨勫熀鍥犵殑棰滆壊
size = label_size,
max.iter=2500,
segment.color = "black", show.legend = FALSE )+
theme_bw()+#淇敼鍥剧墖鑳屾櫙
theme(
legend.title = element_blank()#涓嶆樉绀哄浘渚嬫爣棰?
)+
ylab("-log10 (adjust P value)")+#淇敼y杞村悕绉?
xlab(paste("log2 (fold change)"))+#淇敼x杞村悕绉?
geom_vline(xintercept=c(-log2(fold_change2),log2(fold_change1)),lty=3,col="black",lwd=0.5) +#娣诲姞妯嚎|FoldChange|>2
geom_hline(yintercept = -log10(pvalue),lty=3,col="black",lwd=0.5)#娣诲姞绔栫嚎padj<0.05
#dev.off()
ggsave(paste0(label1,"_",label2, "_volcano_id.pdf"),plot =pp ,width=8,height=6,device = NULL)
}else{
df1$threshold = factor(ifelse(df1$P_value < pvalue & (df1$`log2(foldchange)` > log2(fold_change1) | df1$`log2(foldchange)` < -log2(fold_change2)),ifelse(df1$`log2(foldchange)` > log2(fold_change1),'Up','Down'),'NoSignifi'),levels=c('Up','Down','NoSignifi'))
df1$pro <- rownames(df1)
#鐢诲浘
if(!is.null(select_gene)) {data_text = df1[select_gene,]}
else {data_text = subset(df1, P_value_adjust < pvalue & (df1$`log2(foldchange)` > log2(fold_change1) | df1$`log2(foldchange)` < -log2(fold_change2)))}
pp <- ggplot(df1,aes(x=`log2(foldchange)`,y=-log10(P_value),color=threshold))+
geom_point()+
scale_color_manual(values=c('Up'="#DC143C",'Down'="#0A9731",'NoSignifi'="#808080"))+#纭畾鐐圭殑棰滆壊
geom_text_repel(
data = data_text,
# data = subset(df1, P_value < pvalue & (df1$`log2(foldchange)` > log2(fold_change1) | df1$`log2(foldchange)` < -log2(fold_change2))),
aes(label = pro),
colour = "black", ###鏍囨敞鍑烘潵鐨勫熀鍥犵殑棰滆壊
size = label_size,
max.iter=3000,
segment.color = "black", show.legend = FALSE )+
theme_bw()+#淇敼鍥剧墖鑳屾櫙
theme(
legend.title = element_blank()#涓嶆樉绀哄浘渚嬫爣棰?
)+
ylab("-log10 (P value)")+#淇敼y杞村悕绉?
xlab(paste("log2 (fold change)"))+#淇敼x杞村悕绉?
geom_vline(xintercept=c(-log2(fold_change2),log2(fold_change1)),lty=3,col="black",lwd=0.5) +#娣诲姞妯嚎|FoldChange|>2
geom_hline(yintercept = -log10(pvalue),lty=3,col="black",lwd=0.5)#娣诲姞绔栫嚎padj<0.05
ggsave(paste0(label1,"_",label2, "_volcano_id.pdf"),plot =pp ,width=12,height=8,device = NULL)
#瀛樺偍涓婅皟鍜屼笅璋冭泲鐧戒俊鎭?
up <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` > log2(fold_change1))
down <- subset(df1, df1$P_value < pvalue & df1$`log2(foldchange)` < -log2(fold_change2))
print(paste0("down:",nrow(down)))
print(paste0("up:",nrow(up)))
write.csv(up,file = paste0(label1,"_",label2, "_up_volcano.csv"))
write.csv(down,file = paste0(label1,"_",label2, "_dw_volcano.csv"))
write.csv(df1,file = paste0(label1,"_",label2, "_all_volcano.csv"))
differprot <- list(up=row.names(up),down=row.names(down))
}
return(differprot)
}
##################################################################填充NA的t-TEST多################################################################################
########################################annova###########################
linda.annova2 <- function(data,label,adjust.p=T,pvalue=0.05,title=""){
df6 =data
df.allanov <- data.frame(deal=label,t(df6))
row.names(df.allanov) <-names(df6)
p <- c()
for(i in 2:ncol(df.allanov)){
name <- df.allanov[,colnames(df.allanov)[i]]
anova_p = tryCatch(summary(aov(name ~ df.allanov$deal))[[1]]$`Pr(>F)`[1] %>%format(digits = 3, scientific = FALSE),error = function(x) NA)#保证组间自由度大于等于1，至少有2组label有数据否则会error
p <- append(p,anova_p)
}
adp <- p.adjust(p, method = "BH")%>%format(digits = 3, scientific = FALSE)
allanov_result <- data.frame(df6,pvalue=p,adjust.pvalue = adp)
write.csv(allanov_result,file = paste0(title,"_allprot_ajustp0.05.csv"),row.names = T)
if(adjust.p){
differ_result <- allanov_result[(allanov_result$adjust.pvalue<0.05),]
write.csv(differ_result,file = paste0(title,"_differprot_ajustp0.05.csv"),row.names = T)
}else{
differ_result <- allanov_result[(allanov_result$pvalue<0.05),]
write.csv(differ_result,file = paste0(title,"_differprot_pvalue0.05.csv"),row.names = T)
}
}
###################################mfuzz############################
ge.mfuzz.cselection <- function(data,range=seq(5,50,5),repeats = 5){
df3a<-as.matrix(data)
df3Ex<- ExpressionSet(assayData = df3a)
if(interactive()){
df3F <- filter.NA(df3Ex)
df3F <- fill.NA(df3F)
df3F <- standardise(df3F) #标准化分析
}
df3F <- filter.NA(df3F)
m<-mestimate(df3F)
cselection(df3F,m=m,crange = range,repeats = repeats,visu = T)
return(df3F)
}
ge.mfuzz.getresult <- function(expressionSet, pic,time.label,filename,anova=F,alldata=NULL,type=NULL){
set.seed(10)
cl <- mfuzz(expressionSet,c=pic,m=1.25)
dir.create(path=filename,recursive = TRUE)
pdf(paste0(filename,".pdf"),width = 10, height = 8)
mfuzz.plot2(expressionSet, cl=cl,time.labels=time.label,mfrow=c(4,4),centre=TRUE,x11=F,centre.lwd=0.2)#min.mem=0.99
dev.off()
for(i in 1:pic){
potname<-names(cl$cluster[unname(cl$cluster)==i])
write.csv(cl[[4]][potname,i],paste0(filename,"/mfuzz_",i,".csv")) #存储每个cluster的蛋白名
}
if(anova){
for(ii in 1:pic){
potname<-names(cl$cluster[unname(cl$cluster)==ii])
tmp <- data.frame(label=as.factor(type),t(alldata[potname,]))
anova <- c()
for (n in 1:length(potname)) {
aov<- summary(aov(tmp[,n+1] ~ label,data=tmp))[[1]]$`Pr(>F)`[1] %>% format(digits = 3, scientific = FALSE)
anova <- c(anova,aov)
}
anova.adjust <-p.adjust(anova, method="BH")
newdf <- data.frame(prot=names(tmp)[-1],anova,anova.adjust)
newdf2 <- newdf[newdf$anova.adjust<0.05,]
write.csv(newdf,paste0(filename,"/mfuzz_anova_",ii,".csv"),row.names = F) #给每个cluster在time label上加上一个annova分析，并存储结果
}
}
}
#############################################示例：linda.tsne(matrix,differ_grade,"Gradenew")################################################################
linda.tsne <- function(data,type,shape=24,label=F,ellipse=F,col=c("#33709F","#CC6C5E"),size=5,width=10,height=7,title=""){
df10 <- data
df10[is.na(df10)] <- 0
df10 <- t(apply(df10, 1, scale))
df10[is.na(df10)] <- 0
df10<-data.frame(df10)
colnames(df10) <- type
#set.seed(10)
df11.tsne <- Rtsne(t(df10),
dims = 2,
perplexity = (ncol(data)-1)/3-1,
verbose = T ,
check_duplicates = FALSE)
#
colnames(df11.tsne$Y) <- c("TSNE1","TSNE2")
tsne.data <- data.frame(sample=colnames(data),Type=type,df11.tsne$Y,samplename=names(data))
if(label){
p <- ggscatter(tsne.data,x= "TSNE1",y="TSNE2",
color="Type",
palette = col,
# palette = c("blue","red"), 修改颜色
#label = "samplename",repel = T, #显示每个点表示的样本名
label = "samplename",repel = T,
ellipse = ellipse, #分组圈图
shape = shape, #形状为三角形24,圆是19
fill = "Type", #三角形的填充颜色
#fill = c("#33709F","#CC6C5E"),
size = size,
main=paste0(nrow(df10),"features"))+
theme(axis.title.x =element_text(size=15), axis.title.y=element_text(size=15),
axis.text = element_text(colour ="black", size = 10),
axis.line.x = element_line(size = 0.5),
axis.line.y = element_line(size = 0.5),
panel.background = element_blank(),
panel.grid =element_blank(),
axis.ticks = element_blank())+
theme_base()#调整图里位置在图的右侧，没有theme_base() 则在上方且图形没有闭合的黑框（上和右）
}else{
p <- ggscatter(tsne.data,x= "TSNE1",y="TSNE2",
color="Type",
palette = col,
# palette = c("blue","red"), 修改颜色
#label = "samplename",repel = T, #显示每个点表示的样本名
ellipse = F, #分组圈图
shape = shape, #形状为三角形24,圆是19
fill = "Type", #三角形的填充颜色
#fill = c("#33709F","#CC6C5E"),
size = size,
main="")+