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| f_pca <- function(lc_counts, lc_g){
res <- prcomp(lc_counts)
res <- data.frame(res$rotation, g = lc_g)
res$CB <- rownames(res)
res
}
library(ggplot2)
library(tidyverse)
f_pca_p <- function(lc_pca, x='PC1', y='PC2'){
ggplot(lc_pca,aes(x=!!sym(x),y=!!sym(y),color=g))+
geom_point()+
theme_bw()+
theme(panel.border=element_blank(),panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),axis.line= element_line(colour = "blue"))
}
library(umap)
f_umap <- function(lc_pca,dims=1:30){
res <- umap(lc_pca[,dims])
res <- data.frame(res$layout)
colnames(res) <- c('UMAP1', 'UMAP2')
res$CB <- rownames(res)
tp <- lc_pca[,c('g','CB')]
res <- merge(res, tp, by="CB")
res
}
f_umap_p <- function(lc_umap){
ggplot(lc_umap,aes(x=UMAP1,y=UMAP2,color=g))+
geom_point()+
theme_bw()+
theme(panel.border=element_blank(),panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),axis.line= element_line(colour = "blue"))
}
library(ggplot2)
library(reshape2)
library(plyr)
library(dplyr)
library(patchwork)
library(purrr)
f_gl_boxp <- function(lc_exp, lc_g, lc_delta_ylim){
lc_exp_L = melt(lc_exp)
colnames(lc_exp_L) = c('symbol','sample','value')
lc_exp_L$group = lc_g
p=ggplot(lc_exp_L,aes(x=sample,y=value,fill=group))+geom_boxplot(outlier.shape = NA)
p=p+theme_set(theme_set(theme_bw(base_size=20)))
p=p+theme(text=element_text(face='bold'),axis.text.x=element_text(angle=30,hjust=1),axis.title=element_blank())
lc_ylim = unlist(by(lc_exp_L,
lc_exp_L$sample,
function(x){boxplot.stats(x$value)$stats[c(1, 5)]}))
lc_ylim <- c(min(lc_ylim), max(lc_ylim)) + lc_delta_ylim
p=p + coord_cartesian(ylim = lc_ylim)
p
}
f_icg_boxp <- function(lc_exp, lc_icg, lc_g){
lc_exp_L = melt(lc_exp[lc_icg,])
lc_exp_L <- cbind(lc_exp_L, rownames(lc_exp_L))
colnames(lc_exp_L)=c('value','sample')
lc_exp_L$group = lc_g
p=ggplot(lc_exp_L,aes(x=group,y=value,fill=group))+geom_boxplot()
p=p+stat_summary(fun="mean",geom="point",shape=23,size=3,fill="red")
p=p+theme_set(theme_set(theme_bw(base_size=20)))
p=p+theme(text=element_text(face='bold'),axis.text.x=element_text(angle=30,hjust=1),axis.title=element_blank())
p=p+ggtitle(lc_icg)+theme(plot.title = element_text(hjust = 0.5))
p
}
# 若缺少sva包，则安装
# options(BioC_mirror="https://mirrors.tuna.tsinghua.edu.cn/bioconductor")
# BiocManager::install("sva", update=F)
library(sva)
library(limma)
f_removeBE <- function(lc_dat, lc_batch, lc_g, use_ComBat=F){
if(use_ComBat){
lc_mod <- model.matrix(~as.factor(lc_g))
res <- ComBat(dat = lc_dat, batch = as.factor(lc_batch), mod = lc_mod)
}else{
lc_mod <- model.matrix(~0+as.factor(lc_g))
res <- removeBatchEffect(lc_dat, batch = as.factor(lc_batch), design = lc_mod)
}
res
}
library(ggdendro)
f_DEG_hclust <- function(lc_counts){
ggdendrogram(hclust(dist(t(lc_counts))), rotate = T, size = 3)+theme(axis.text = element_text(size=14,face = "bold"))
}
|
