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#bulkRNA-PCA+DEG.r
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#小周的bulkRNAseq解析
setwd("/path/to/your/own/Input")
# Import packages ---------------------------------------------------------
library(edgeR)
library(readr)
library(gplots)
library(RColorBrewer)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(ggrepel)
#数据导入
PL4 <- read.table("PL4_featureCounts_output.txt", sep = "", header = T)
PL5 <- read.table("PL5_featureCounts_output.txt", sep = "", header = T)
PL11 <- read.table("PL11_featureCounts_output.txt", sep = "", header = T)
PL12 <- read.table("PL12_featureCounts_output.txt", sep = "", header = T)
V4 <- read.table("V4_featureCounts_output.txt", sep = "", header = T)
V5 <- read.table("V5_featureCounts_output.txt", sep = "", header = T)
V10 <- read.table("V10_featureCounts_output.txt", sep = "", header = T)
V12 <- read.table("V12_featureCounts_output.txt", sep = "", header = T)
#列名を変更する
colnames(PL4) <- c("Gene_ID", "PL4")
colnames(PL5) <- c("Gene_ID", "PL5")
colnames(PL11) <- c("Gene_ID", "PL11")
colnames(PL12) <- c("Gene_ID", "PL12")
colnames(V4) <- c("Gene_ID", "V4")
colnames(V5) <- c("Gene_ID", "V5")
colnames(V10) <- c("Gene_ID", "V10")
colnames(V12) <- c("Gene_ID", "V12")
#这b还轮流merge也是真爱护环境
Data <- merge(PL4, PL5, all = T, by = "Gene_ID")
Data <- merge(Data, PL11, all = T, by = "Gene_ID")
Data <- merge(Data, PL12, all = T, by = "Gene_ID")
Data <- merge(Data, V4, all = T, by = "Gene_ID")
Data <- merge(Data, V5, all = T, by = "Gene_ID")
Data <- merge(Data, V10, all = T, by = "Gene_ID")
Data <- merge(Data, V12, all = T, by = "Gene_ID")
#这一步是把第一列提出来作为行名，然后把行名赋值给Data这个数据后把第一列删掉
row_names <- Data[,1]
rownames(Data) <- row_names
Data <- Data[,-1]
#她想把这个表现矩阵存下来
#write.csv(Data, "read count.csv") ##save csv file
#cut-offf(read count >10) ---------------------------------过滤了一下
data <- filter_all(Data, all_vars(. >=10))
#她又想把过滤完的矩阵存下来
#write.csv(data, "read count>10.csv") ##save csv file
# Calculate logCPM ---好像是把原来的raw counts给标准化为一个计数形式-------------------------------------------
logcpm <- cpm(Data, log=TRUE)
logcpm
#write.csv(Data, "logCPM.csv")
# Hierarchical clustering --计算相关性然后聚类------------------------------------
#Hierarchical clustering between samples
#use Spearman's rank correlation coefficient
rho <- cor(logcpm, method = "spearman")
#Ward-based clustering
d <- as.dist(1 - rho)
tree <- hclust(d, method = "ward.D")
plot(tree) #看聚类的树
# Sample PCA ----------主成分分析------------------------------------------
pca <- prcomp(t(logcpm), scale = FALSE)
summary(pca)
#Importance of components:
# PC1 PC2 PC3 PC4 PC5 PC6 PC7
# Standard deviation 37.6624 29.1613 28.6927 25.9188 23.4865 22.41439 22.15855
# Proportion of Variance 0.2672 0.1602 0.1551 0.1265 0.1039 0.09463 0.09249
# Cumulative Proportion 0.2672 0.4274 0.5824 0.7090 0.8129 0.90751 1.00000
# PC8
# Standard deviation 4.401e-13
# Proportion of Variance 0.000e+00
# Cumulative Proportion 1.000e+00
#plot ---可视化PCA-------------------------------------------------
color <- c("#97ffb8","#ff7b7b", "#FFC354","#85fbff", "#509DC3","#be8fff", "#fe75a2","#dcff7b")
par(mar=c(5,5,5,10))
# 有边框版本
plot(pca$x[, 1], pca$x[,2],
col = "black", # 先设定边框颜色
bg = color, # 再填充里面的颜色
pch = 21, # 要边框是21，不要就是19
cex = 2, # 点的大小
main = "PCA",
xlab = "PC1 (37.7%)",
ylab = "PC2 (29.2%)")
par(xpd=TRUE)
legend(x=par()$usr[2], y=par()$usr[4], legend = colnames(logcpm), bty = "n", pch = 19, col = color, pt.cex =1.5)
# 无边框版本
plot(pca$x[, 1], pca$x[,2],
col = color, #颜色就是设定好的颜色
pch = 19, # 要边框是21，不要就是19
cex = 2, # 点的大小
main = "PCA",
xlab = "PC1 (37.7%)",
ylab = "PC2 (29.2%)")
par(xpd=TRUE)
legend(x=par()$usr[2], y=par()$usr[4], legend = colnames(logcpm), bty = "n", pch = 19, col = color, pt.cex =1.5)
#DEG
# 把data变成矩阵
data <- as.matrix(data)
dim(data)
# 定义一下组别，就是在这里把两个V合成一个Vehicle的
Treatment <- factor(c("aPD-L1", "aPD-L1", "KO_aPD-L1", "KO_aPD-L1", "Vehicle", "Vehicle", "KO_Vehicle", "KO_Vehicle"))
data <- DGEList(data, group = Treatment)
# 过滤一下低表达基因
keep <- filterByExpr(data)
data <- data[keep, , keep.lib.sizes=FALSE]
# 标准化
data <- calcNormFactors(data)
data$samples
design <- model.matrix(~Treatment)
colnames(design) <- levels(Treatment) # 将列名设置为分组名
data <- estimateCommonDisp(data)
# 提出差异表达基因DEG-------------------------------------------------------------
Treatment
# [1] aPD-L1 aPD-L1 KO_aPD-L1 KO_aPD-L1 Vehicle Vehicle
# [7] KO_Vehicle KO_Vehicle
# Levels: aPD-L1 KO_aPD-L1 KO_Vehicle Vehicle
#切FC是1.5的话那logFC切到0.585
### aPD-L1 vs Vehicle ###
et <- exactTest(data, pair = c("aPD-L1", "Vehicle"))
topTags(et)
FC1 <- as.data.frame(topTags(et, n = nrow(count)))
FC1 <- FC1 %>%
mutate(Expression = case_when(logFC >= 0.585 & logCPM >= 0.301 & FDR < 0.05 ~ "Up-regulated",
logFC <= -0.585 & logCPM >= 0.301 & FDR < 0.05 ~ "Down-regulated",
TRUE ~ "Unchanged"))##FC>1.5, logCPM >1, FDR <0.05
# write.csv(FC1, "DEG_aPD-L1 vs Vehicle.csv")
### KO_aPD-L1 vs KO_Vehicle ###
et <- exactTest(data, pair = c("KO_aPD-L1", "KO_Vehicle"))
topTags(et)
FC2 <- as.data.frame(topTags(et, n = nrow(count)))
FC2 <- FC2 %>%
mutate(Expression = case_when(logFC >= 0.585 & logCPM >= 0.301 & FDR < 0.05 ~ "Up-regulated",
logFC <= -0.585 & logCPM >= 0.301 & FDR < 0.05 ~ "Down-regulated",
TRUE ~ "Unchanged"))##FC>1.5, logCPM >1, FDR <0.05
# write.csv(FC2, "DEG_KO_aPD-L1 vs KO_Vehicle.csv")
### KO_Vehicle vs Vehicle ###
et <- exactTest(data, pair = c("KO_Vehicle", "Vehicle"))
topTags(et)
FC3 <- as.data.frame(topTags(et, n = nrow(count)))
FC3 <- FC3 %>%
mutate(Expression = case_when(logFC >= 0.585 & logCPM >= 0.301 & FDR < 0.05 ~ "Up-regulated",
logFC <= -0.585 & logCPM >= 0.301 & FDR < 0.05 ~ "Down-regulated",
TRUE ~ "Unchanged"))##FC>1.5, logCPM >1, FDR <0.05
# write.csv(FC3, "DEG_KO_Vehicle vs Vehicle.csv")
### KO_aPD-L1 vs aPD-L1 ###
et <- exactTest(data, pair = c("KO_aPD-L1", "aPD-L1"))
topTags(et)
FC4 <- as.data.frame(topTags(et, n = nrow(count)))
FC4 <- FC4 %>%
mutate(Expression = case_when(logFC >= 0.585 & logCPM >= 0.301 & FDR < 0.05 ~ "Up-regulated",
logFC <= -0.585 & logCPM >= 0.301 & FDR < 0.05 ~ "Down-regulated",
TRUE ~ "Unchanged"))##FC>1.5, logCPM >1, FDR <0.05
# write.csv(FC4, "DEG_KO_aPD-L1 vs PD-L1.csv")
#火山图
### aPD-L1 vs Vehicle ###
options(ggrepel.max.overlaps = Inf)
MA1 <- ggplot(FC1, aes(x=logFC, y=logCPM, colour=Expression)) +
geom_point(alpha=0.8) +
ylim(-3,20) +
theme_bw() +
theme(
legend.position = "right",
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white")) +
scale_color_manual(values=c("#2166ac", "grey","#b2182b")) +
labs(colour="Differential Expression") +
ggtitle(paste0("MA Plot (aPD-L1 vs Vehicle)")) +
geom_vline(xintercept=c(-0.585,0.585),lty=2,col="black",lwd=0.4) +
geom_hline(yintercept=0.301,lty=2,col="black",lwd=0.4) +
labs(x="log2(FC)",y="log2CPM")+guides(colour = guide_legend(override.aes = list(size = 4)))
MA1
### KO_aPD-L1 vs KO_Vehicle ###
options(ggrepel.max.overlaps = Inf)
MA2 <- ggplot(FC2, aes(x=logFC, y=logCPM, colour=Expression)) +
geom_point(alpha=0.8) +
ylim(-3,20) +
theme_bw() +
theme(
legend.position = "right",
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white")) +
scale_color_manual(values=c("#2166ac", "grey","#b2182b")) +
labs(colour="Differential Expression") +
ggtitle(paste0("MA Plot (KO_aPD-L1 vs KO_Vehicle)")) +
geom_vline(xintercept=c(-0.585,0.585),lty=2,col="black",lwd=0.4) +
geom_hline(yintercept=0.301,lty=2,col="black",lwd=0.4) +
labs(x="log2(FC)",y="log2CPM")+guides(colour = guide_legend(override.aes = list(size = 4)))
MA2
### KO_Vehicle vs Vehicle ###
options(ggrepel.max.overlaps = Inf)
MA3 <- ggplot(FC3, aes(x=logFC, y=logCPM, colour=Expression)) +
geom_point(alpha=0.8) +
ylim(-3,20) +
theme_bw() +
theme(
legend.position = "right",
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white")) +
scale_color_manual(values=c("#2166ac", "grey","#b2182b")) +
labs(colour="Differential Expression") +
ggtitle(paste0("MA Plot (KO_Vehicle vs Vehicle)")) +
geom_vline(xintercept=c(-0.585,0.585),lty=2,col="black",lwd=0.4) +
geom_hline(yintercept=0.301,lty=2,col="black",lwd=0.4) +
labs(x="log2(FC)",y="log2CPM")+guides(colour = guide_legend(override.aes = list(size = 4)))
MA3
### KO_aPD-L1 vs aPD-L1 ###
options(ggrepel.max.overlaps = Inf)
MA4 <- ggplot(FC4, aes(x=logFC, y=logCPM, colour=Expression)) +
geom_point(alpha=0.8) +
ylim(-3,20) +
theme_bw() +
theme(
legend.position = "right",
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white")) +
scale_color_manual(values=c("#2166ac", "grey","#b2182b")) +
labs(colour="Differential Expression") +
ggtitle(paste0("MA Plot (KO_aPD-L1 vs aPD-L1)")) +
geom_vline(xintercept=c(-0.585,0.585),lty=2,col="black",lwd=0.4) +
geom_hline(yintercept=0.301,lty=2,col="black",lwd=0.4) +
labs(x="log2(FC)",y="log2CPM")+guides(colour = guide_legend(override.aes = list(size = 4)))
MA4
## MA plot with label(top10 gene)带标识的-------------------------------
### aPD-L1 vs Vehicle ###
top <- 10
top_genes_1 <- bind_rows(
FC1 %>%
filter(Expression == 'Up-regulated' ) %>%
arrange(desc(logFC)) %>% head(top), FC1 %>%
filter(Expression == 'Down-regulated' ) %>%
arrange(logFC) %>% head(top))
top_genes_1
# 得把这个行名转换为一列单独存在，取名 "Gene"
top_genes_1 <- top_genes_1 %>%
rownames_to_column(var = "Gene")
ma1 <- ggplot(FC1, aes(x=logFC, y=logCPM, colour=Expression)) +
geom_point(alpha=0.8) +
ylim(-3,20) +
theme_bw() +
theme(
legend.position = "right",
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white")) +
scale_color_manual(values=c("#2166ac", "grey","#b2182b")) +
labs(colour="Differential Expression") +
ggtitle(paste0("MA Plot (aPD-L1 vs Vehicle)")) +
geom_vline(xintercept=c(-0.585,0.585),lty=2,col="black",lwd=0.4) +
geom_hline(yintercept=0.301,lty=2,col="black",lwd=0.4) +
labs(x="log2(FC)",y="log2CPM")+guides(colour = guide_legend(override.aes = list(size = 4)))+ geom_text_repel(data = top_genes_1, label = (top_genes_1$Gene), box.padding = unit(.100, "lines"),hjust= 0.30, size = 3, force = 1, nudge_y = 1, color ="black")
ma1
### KO_aPD-L1 vs KO_Vehicle ###
top_genes_2 <- bind_rows(
FC2 %>%
filter(Expression == 'Up-regulated' ) %>%
arrange(desc(logFC)) %>% head(top), FC2 %>%
filter(Expression == 'Down-regulated' ) %>%
arrange(logFC) %>% head(top))
top_genes_2
top_genes_2 <- top_genes_2 %>%
rownames_to_column(var = "Gene")
ma2 <- ggplot(FC2, aes(x=logFC, y=logCPM, colour=Expression)) +
geom_point(alpha=0.8) +
ylim(-3,20) +
theme_bw() +
theme(
legend.position = "right",
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white")) +
scale_color_manual(values=c("#2166ac", "grey","#b2182b")) +
labs(colour="Differential Expression") +
ggtitle(paste0("MA Plot (KO_aPD-L1 vs KO_Vehicle)")) +
geom_vline(xintercept=c(-0.585,0.585),lty=2,col="black",lwd=0.4) +
geom_hline(yintercept=0.301,lty=2,col="black",lwd=0.4) +
labs(x="log2(FC)",y="log2CPM")+guides(colour = guide_legend(override.aes = list(size = 4))) +
geom_text_repel(data = top_genes_2, label = (top_genes_2$Gene), box.padding = unit(.100, "lines"),hjust= 0.30, size = 3, force = 1, nudge_y = 1, color ="black")
ma2
### KO_Vehicle vs Vehicle ###
top_genes_3 <- bind_rows(
FC3 %>%
filter(Expression == 'Up-regulated' ) %>%
arrange(desc(logFC)) %>% head(top), FC3 %>%
filter(Expression == 'Down-regulated' ) %>%
arrange(logFC) %>% head(top))
top_genes_3
top_genes_3 <- top_genes_3 %>%
rownames_to_column(var = "Gene")
ma3 <- ggplot(FC3, aes(x=logFC, y=logCPM, colour=Expression)) +
geom_point(alpha=0.8) +
ylim(-3,20) +
theme_bw() +
theme(
legend.position = "right",
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white")) +
scale_color_manual(values=c("#2166ac", "grey","#b2182b")) +
labs(colour="Differential Expression") +
ggtitle(paste0("MA Plot (KO_Vehicle vs Vehicle)")) +
geom_vline(xintercept=c(-0.585,0.585),lty=2,col="black",lwd=0.4) +
geom_hline(yintercept=0.301,lty=2,col="black",lwd=0.4) +
labs(x="log2(FC)",y="log2CPM")+guides(colour = guide_legend(override.aes = list(size = 4))) +
geom_text_repel(data = top_genes_3, label = (top_genes_3$Gene), box.padding = unit(.100, "lines"),hjust= 0.30, size = 3, force = 1, nudge_y = 1, color ="black")
ma3
### KO_aPD-L1 vs aPD-L1 ###
top_genes_4 <- bind_rows(
FC4 %>%
filter(Expression == 'Up-regulated' ) %>%
arrange(desc(logFC)) %>% head(top), FC4 %>%
filter(Expression == 'Down-regulated' ) %>%
arrange(logFC) %>% head(top))
top_genes_4
top_genes_4 <- top_genes_4 %>%
rownames_to_column(var = "Gene")
ma4 <- ggplot(FC4, aes(x=logFC, y=logCPM, colour=Expression)) +
geom_point(alpha=0.8) +
ylim(-3,20) +
theme_bw() +
theme(
legend.position = "right",
panel.background = element_rect(fill = "white"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
plot.background = element_rect(fill = "white")) +
scale_color_manual(values=c("#2166ac", "grey","#b2182b")) +
labs(colour="Differential Expression") +
ggtitle(paste0("MA Plot (KO_aPD-L1 vs aPD-L1)")) +
geom_vline(xintercept=c(-0.585,0.585),lty=2,col="black",lwd=0.4) +
geom_hline(yintercept=0.301,lty=2,col="black",lwd=0.4) +
labs(x="log2(FC)",y="log2CPM")+guides(colour = guide_legend(override.aes = list(size = 4))) +
geom_text_repel(data = top_genes_4, label = (top_genes_4$Gene), box.padding = unit(.100, "lines"),hjust= 0.30, size = 3, force = 1, nudge_y = 1, color ="black")
ma4
# # Session.information ---看当前环境的没啥b用不用管---------------------------------------------
# sessionInfo()