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zzov_Fig1_BC.Rmd
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---
title: "ZZOV"
author: "Yan Zhou"
date: "2024-03-31"
output: html_document
---
```{r setup, include=FALSE}
rm(list = ls())
pacman::p_load(readxl, openxlsx, #Load One or More Packages
reshape2, magrittr, tidyverse, Hmisc,
ggrepel,ggplot2,
ggpubr, ggsignif, ggridges,
RColorBrewer, randomcoloR,
gridExtra, ggplotify, Rtsne,corrplot)
```
```{r import data}
pinfo = read.xlsx("20210411ZZOV_patient_sample_info.xlsx", sheet = 1)
sinfo = read.xlsx("20210411ZZOV_patient_sample_info.xlsx", sheet = 2)
pm = read.table("20210408ZZOV_1064sample_withNA_diann_prot.txt") # updated matrix
```
```{r tsne - Fig1 C, FigS1 B}
outliers = read.xlsx("20210409tsne_1064_outliers.xlsx",sheet = 2, colNames = F)
outliers = outliers$X1
pm_2 = pm[, !names(pm) %in% outliers]
names(pm_2) = gsub("P216HSab", "P216HSbb", names(pm_2))
### normalization
pm_2 = 2^pm_2
###quantile normalize
pm2_QuanNor = preprocessCore::normalize.quantiles(as.matrix(pm_2), copy = F)
## NA imputation by min
pm2_QuanNor[is.na(pm2_QuanNor)] = 0.8*min(na.omit(pm2_QuanNor))
pm2_QuanNor = as.data.frame(pm2_QuanNor)
## log2
pm2_QuanNor = log2(pm2_QuanNor)
nm = names(pm2_QuanNor)
nm1 = str_sub(nm, end = -5)
pinfo1 = pinfo[pinfo$Patient_ID %in% nm1,]
#test if all samples in the matrix are in the patient info
sum(nm1 %in% pinfo$Patient_ID)
# different labels (normal, benign, borderline, carcinoma)
nm3 = pinfo1$Group[match(nm1,pinfo1$Patient_ID)]
# different labels (batch)
nm4 = sinfo$batchId[match(nm1,sinfo$Patient_ID)]
temp1 = sapply(strsplit(nm4, "_", perl = T), '[', 1)
temp2 = sapply(strsplit(nm4, "_", perl = T), '[', 2)
nm5 = paste(temp1,temp2, sep = "_")
df10 <- pm2_QuanNor
set.seed(2021)
df11.tsne <- Rtsne(t(df10), dims = 2, perplexity = 10, verbose = T , check_duplicates = FALSE)
df.tsne = cbind(df11.tsne$Y,
nm3,
nm5) %>%
data.frame(row.names = nm,
stringsAsFactors = F)
names(df.tsne) = c("tsne1",
"tsne2",
"Histology",
"batch")
## very important step!! or you will get a shit
df.tsne[,c(1,2)] = apply(df.tsne[,c(1,2)],
2,
as.numeric)
df.tsne$batch = factor(df.tsne$batch, levels = unique(nm5))
fig_tsne_patient = ggplot(df.tsne,
aes(tsne1,tsne2)) +
geom_point(aes(color = Histology),
size = 3) +
# scale_color_manual(name = sort("Histology6"),
# # values = organ_fig_color,
# values = c("#E6AB02",
# "#1B9E77",
# # "#D95F02",
# "#7570B3",
# "#E7298A"
# )) +
labs(title = "tsne") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.text = element_text(size = 12,
color = "black"),
axis.title = element_text(size = 14),
# axis.title.y = element_text(size = 16,),
plot.title = element_text(hjust=0.5,size = 14),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12))
ggsave("1041_histology_TSNE_20210410.pdf",
fig_tsne_patient,
height = 6,
width = 8)
fig_tsne_patient = ggplot(df.tsne,
aes(tsne1,tsne2)) +
geom_point(aes(color = batch),
size = 3) +
# scale_color_manual(name = sort("Histology"),
# # values = organ_fig_color,
# values = c("#E6AB02",
# "#1B9E77",
# # "#D95F02",
# "#7570B3",
# "#E7298A"
# )) +
labs(title = "tsne") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.text = element_text(size = 12,
color = "black"),
axis.title = element_text(size = 14),
# axis.title.y = element_text(size = 16,),
plot.title = element_text(hjust=0.5,size = 14),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12))
ggsave("1041_prot_TSNE_batch_20210408.pdf",
fig_tsne_patient,
height = 6,
width = 12)
```
```{r PCA - FigS1 C,D,E}
ge.split <- function(data,split,which=1){
sapply(data,function(v){strsplit(v,split)[[1]][which]})
}
pm_1041 = read.table("20210409ZZOV_1041sample_withNA_diann_prot.txt")
pm_2 = 2^pm_1041
###quantile normalize
pm2_QuanNor = preprocessCore::normalize.quantiles(as.matrix(pm_2), copy = F)
## NA imputation by min
pm2_QuanNor[is.na(pm2_QuanNor)] = 0.8*min(na.omit(pm2_QuanNor))
pm2_QuanNor = as.data.frame(pm2_QuanNor)
## log2
pm2_QuanNor = log2(pm2_QuanNor)
sinfo2 = sinfo[sinfo$Sample_name %in% names(pm2_QuanNor),]
### PCA
set.seed(2021)
pca = prcomp(t(pm2_QuanNor))
#head(pca$x)
screeplot(pca, type = "line")
# the variance explained by each component
var_explained <- pca$sdev^2/sum(pca$sdev^2)
var_explained[1:5]
nm = names(pm2_QuanNor)
nm1 = sinfo2$batchId[match(nm, sinfo2$Sample_name)]
nm2 = paste0(ge.split(nm1,"_",1),"_",ge.split(nm1,"_",2))
nm3 = ge.split(nm1,"_",2) %>% str_sub(start = 2) %>% as.numeric()
df.pca = cbind(pca$x[,1:2],
nm2,
nm3) %>%
data.frame(row.names = nm,
stringsAsFactors = F)
names(df.pca) = c("PC1",
"PC2",
"Batch",
"#batch")
# df.pca$Histology[df.pca$Histology == " carcinoma"] = "carcinoma"
df.pca$Batch = factor(df.pca$Batch, levels = unique(df.pca$Batch))
## very important step!! or you will get a shit
df.pca[,c(1,2)] = apply(df.pca[,c(1,2)],
2,
as.numeric)
df.pca$`#batch` = as.numeric(df.pca$`#batch`)
for (i in 1:nrow(df.pca)){
if (grepl("OVA", df.pca$Batch[i])) {df.pca$label[i] = (df.pca$`#batch`[i])}
if (grepl("OVB", df.pca$Batch[i])) {df.pca$label[i] = (df.pca$`#batch`[i] + 49)}
if (grepl("OVC", df.pca$Batch[i])) {df.pca$label[i] = (df.pca$`#batch`[i] + 55)}
}
### 2021.11.22 color legend 改成gradient的形式
library(scales)
library(RColorBrewer)
library(viridis)
fig_PCA_patient = ggplot(df.pca,
aes(PC1,PC2)) +
geom_point(aes(color = label),
size = 1.5) +
# scale_colour_gradientn(colours = "Spectral") +
scale_color_viridis(option = "D") +
labs(title = "PCA",
x=paste0("PC1: ",round(var_explained[1]*100,1),"%"),
y=paste0("PC2: ",round(var_explained[2]*100,1),"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.text = element_text(size = 12,
color = "black"),
axis.title = element_text(size = 14),
# axis.title.y = element_text(size = 16,),
plot.title = element_text(hjust=0.5,size = 14),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12))
ggsave("1041_prot_PCA_batch_20211122.pdf",
fig_PCA_patient,
height = 6,
width = 8)
write.xlsx(df.pca, "PCA_1041.xlsx", row.names = T)
### Batch A:
## label 1: column
df.pca.A = df.pca[grepl("A",df.pca$Batch),]
df.pca.A$Batch = factor(df.pca.A$Batch, levels = unique(df.pca.A$Batch))
temp1 = ge.split(nm1,"_",1)
temp2 = ge.split(nm1,"_",2)
a = paste("b",1:9, sep = "")
b = paste("b",10:13, sep = "")
c = paste("b",14:22, sep = "")
e = paste("b",24:49, sep = "")
for (i in 1:nrow(df.pca.A)){
if (temp1[i] == "OVA" & temp2[i] %in% a) {df.pca.A$column[i] = "a"}
if (temp1[i] == "OVA" & temp2[i] %in% b) {df.pca.A$column[i] = "b"}
if (temp1[i] == "OVA" & temp2[i] %in% c) {df.pca.A$column[i] = "c"}
if (temp1[i] == "OVA" & temp2[i] == "b23") {df.pca.A$column[i] = "d"}
if (temp1[i] == "OVA" & temp2[i] %in% e) {df.pca.A$column[i] = "e"}
#if (temp1[i] == "OVB" | temp1[i] == "OVC") {df.pca.A$column[i] = "f"}
}
df.pca.A$column = paste0("Column ",df.pca.A$column)
fig_PCA_patient = ggplot(df.pca.A,
aes(PC1,PC2)) +
geom_point(aes(color = column),
size = 1.5) +
labs(title = "PCA",
x=paste0("PC1: ",round(var_explained[1]*100,1),"%"),
y=paste0("PC2: ",round(var_explained[2]*100,1),"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.text = element_text(size = 12,
color = "black"),
axis.title = element_text(size = 14),
# axis.title.y = element_text(size = 16,),
plot.title = element_text(hjust=0.5,size = 14),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12))
ggsave("1041_prot_PCA_batchA_column_20211217.pdf",
fig_PCA_patient,
height = 6,
width = 8)
## label 2: peptide amount
df.pca.A$"#batch" = df.pca.A$"#batch" %>% as.numeric()
for (i in 1:nrow(df.pca.A)){
if (df.pca.A$`#batch`[i] <= 16 ) {df.pca.A$peptide_amount[i] = "500 ng"}
if (df.pca.A$`#batch`[i] > 16) {df.pca.A$peptide_amount[i] = "250 ng"}
}
fig_PCA_patient = ggplot(df.pca.A,
aes(PC1,PC2)) +
geom_point(aes(color = peptide_amount),
size = 1.5) +
# scale_color_manual(name = sort("Histology"),
# # values = organ_fig_color,
# values = c("#E6AB02",
# "#1B9E77",
# # "#D95F02",
# "#7570B3",
# "#E7298A"
# )) +
labs(title = "PCA",
x=paste0("PC1: ",round(var_explained[1]*100,1),"%"),
y=paste0("PC2: ",round(var_explained[2]*100,1),"%")) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.text = element_text(size = 12,
color = "black"),
axis.title = element_text(size = 14),
# axis.title.y = element_text(size = 16,),
plot.title = element_text(hjust=0.5,size = 14),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12))
ggsave("1041_prot_PCA_batchA_pep_20211217.pdf",
fig_PCA_patient,
height = 6,
width = 8)
```
```{r protein number boxplot - Fig1 B}
sinfo$histology = pinfo$Group[match(sinfo$Patient_ID, pinfo$Patient_ID)]
sinfo$histology[grepl("arcinoma",sinfo$histology )] = "Carcinoma"
sinfo1 = sinfo[sinfo$Sample_name %in% names(pm_1041),]
prot_num = data.frame(sample = names(pm_1041),
prot_num = apply(pm_1041, 2, function(x) sum(!is.na(x))),
Type = factor(sinfo1$histology,
levels = c("Normal","Benign",
"Borderline","Carcinoma")),
stringsAsFactors = F)
pdf("ZZOV_prot_num_histology_boxplot_20210412.pdf", width = 6, height = 4)
ggplot(prot_num,
aes(Type, prot_num,
colour = Type)) +
geom_jitter(alpha = 0.5,size=1) +
geom_boxplot(alpha = 0.5,size=1) +
ylab("Number of preotein identifications") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
axis.line = element_line(colour = "black"),
axis.text = element_text(size = 12, colour = "black"),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
plot.title = element_text(size = 20),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12))
dev.off()
```
```{r correlation - FigS1 F}
pm_2samples = pm_1041[,(grepl("P335", names(pm_1041)) | grepl("P518", names(pm_1041))) ]
pm_2samples = pm_2samples[,c("P335HSaa", "P335HSab", "P335HSba", "P335HSbb",
"P518HSaa", "P518HSab", "P518HSba", "P518HSbb")]
mycolor = colorRampPalette(c("FFCCCC", "#FF0000"))
df = cor(pm_2samples,use = "pairwise.complete.obs")
df1 = df+0.5
pdf("correlation_8samples_20211116_withnumbers.pdf")
corrplot(df1,
type = "full",
method = "color",
is.corr = F,
tl.col = "black",
# tl.pos = 'd',
tl.cex = 0.7,
#col.lim = c(1,1.5),
col = COL1('Reds'),
cl.length = 6,
addCoef.col = 'black'
# mar=c(0,0,1,0)
)
dev.off()
```
```{r violin - FigS1 G}
# mouse liver
pm_ml <- read.delim("20210129ZZOV_QCML_liverlib_excludedFromAssay_prot_matrix.txt")
pm_ml = data.frame((pm_ml[,-1]),
row.names = pm_ml$prot)
names(pm_ml) = names(pm_ml) %>%
sapply(function(x) {
x = str_split(x,
"_")[[1]]
return(paste0(str_sub(nth(x,-5),
5),
"_",
nth(x,-3)))
})
pm_ml <- pm_ml[apply(pm_ml, 1, function(x){sum(!is.na(x))})>0,]
pm_ml = 2^pm_ml
###quantile normalize
pm_QuanNor = preprocessCore::normalize.quantiles(as.matrix(pm_ml), copy = F)
pm_QuanNor = as.data.frame(pm_QuanNor)
pm_QuanNor[is.na(pm_QuanNor)] = 0.8*min(na.omit(pm_QuanNor))
pm_QuanNor = log2(pm_QuanNor)
# Calculate the cor value between any two samples
cor_value_ml = combn(1:ncol(pm_QuanNor),2) %>%
apply(2,
function(x) {
cor(pm_QuanNor[,x[1]],
pm_QuanNor[,x[2]],
use = "pairwise.complete.obs")
})
## pool
pm_pool <- read.delim("20210118ZZOV_68pool_diann_prot.txt")
#pm_pool = data.frame(log2(pm_pool[,-1]),
# row.names = pm_pool$prot)
names(pm_pool) = names(pm_pool) %>%
str_sub(3,-6)
pm_pool <- pm_pool[apply(pm_pool, 1, function(x){sum(!is.na(x))})>0,]
df_describe(pm_pool)
pm_pool = 2^pm_pool
###quantile normalize
pm_QuanNor = preprocessCore::normalize.quantiles(as.matrix(pm_pool), copy = F)
pm_QuanNor = as.data.frame(pm_QuanNor)
pm_QuanNor[is.na(pm_QuanNor)] = 0.8*min(na.omit(pm_QuanNor))
pm_QuanNor = log2(pm_QuanNor)
# Calculate the cor value between any two samples
cor_value_pool = combn(1:ncol(pm_QuanNor),2) %>%
apply(2,
function(x) {
cor(pm_QuanNor[,x[1]],
pm_QuanNor[,x[2]],
use = "pairwise.complete.obs")
})
#calculate correlation of technical replicates
tech_rep = read.xlsx("20210412ZZOV_replicates.xlsx", sheet = 2)
identical(str_sub(tech_rep$technical.replicates_1, end = -5), str_sub(tech_rep$technical.replicates_2, end = -5))
tech_names = as.vector(t(tech_rep))
tech_names = tech_names[tech_names %in% sinfo1$Sample_name]
test1 = str_sub(tech_names, end = -5)
test1[!(duplicated(test1)|duplicated(test1, fromLast=TRUE))]
tech_names = tech_names[-grep("P216",tech_names)]
Tech_rep_no = match(tech_names,sinfo1$Sample_name)
cor_Tech_rep = matrix(Tech_rep_no,
ncol = 2,
byrow = T) %>%
apply(1,
function(x) {
cor(pm2_QuanNor[,x[1]],
pm2_QuanNor[,x[2]],
use = "pairwise.complete.obs")
})
#calculate correlation of biological replicates
bio_rep = read.xlsx("20210412ZZOV_replicates.xlsx", sheet = 1)
identical(str_sub(bio_rep$bio_rep_1, end = -5), str_sub(bio_rep$bio_rep_2, end = -5))
bio_rep_name = as.vector(t(bio_rep))
bio_rep_name = bio_rep_name[bio_rep_name %in% sinfo1$Sample_name]
test1 = str_sub(bio_rep_name, end = -5)
test2 = test1[!(duplicated(test1)|duplicated(test1, fromLast=TRUE))]
for (i in test2) {
bio_rep_name = bio_rep_name[-grep(i,bio_rep_name)]
}
bio_rep_no = match(bio_rep_name,sinfo1$Sample_name)
cor_bio_rep = matrix(bio_rep_no,
ncol = 2,
byrow = T) %>%
apply(1,
function(x) {
cor(pm2_QuanNor[,x[1]],
pm2_QuanNor[,x[2]],
use = "pairwise.complete.obs")
})
#### plot
data_violin = data.frame(y = c(cor_value_ml,cor_value_pool, cor_Tech_rep,cor_bio_rep),
x = c(rep("Mouse liver", 2278), rep("Pool", 2278),
rep("Technical replicates", 118),rep("Biological replicates", 109)))
data_violin$x = factor(data_violin$x, levels = c("Mouse liver", "Pool", "Technical replicates", "Biological replicates"))
pdf("4violin_plots_20211118.pdf")
ggplot(data_violin, aes(x, y, fill=x)) +
geom_violin(fill="transparent",
draw_quantiles = c(0.25, 0.75),
linetype = "dashed",
show.legend = F
) +
geom_violin(fill="transparent",draw_quantiles = 0.5) +
# geom_boxplot(width = 0.2)+
stat_summary(fun=mean, geom="point", shape=20, size=3, color="red", fill="red")+
theme_bw() +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))
dev.off()
```