生信学习之通路富集二（通路打分）

生信学习之通路富集二（通路打分）：

生信学习之通路富集一（GO分析）

对富集通路打分：

AUC打分代码。

// 用来看AUC对某一个通路的评分

// An highlighted block
Sys.setenv(LANGUAGE = "en")
##禁止转化为因子
options(stringsAsFactors = FALSE)
##清空环境
rm(list=ls())

###加载所需要的包
library(Seurat)
library(tidyverse)
library(dplyr)
library(patchwork)
library(harmony)
library(cowplot)
library(ggplot2)
load("scRNA1.rdata")


# BiocManager::install("AUCell")
library(AUCell)

# BiocManager::install("clusterProfiler")
library(clusterProfiler)

sc.id=sample(colnames(scRNA1),1500)
sc2=scRNA1[,sc.id]
# install.packages("doParallel")
# install.packages("doRNG")
cells_rankings <- AUCell_buildRankings(sc2@assays$RNA@data, splitByBlocks=TRUE, nCores=6, plotStats=TRUE) 

cells_rankings

c2 <- read.gmt("c2.cp.kegg.v2023.1.Hs.symbols.gmt") 
geneSets <- lapply(unique(c2$term), function(x){print(x);c2$gene[c2$term == x]})
names(geneSets) <- unique(c2$term)
?AUCell_calcAUC
cells_AUC <- AUCell_calcAUC(geneSets, cells_rankings,nCores =1, aucMaxRank=nrow(cells_rankings)*0.1)

# install.packages("msigdbr")
library(msigdbr)
# msigdbr_species()
# 
# m_df<- msigdbr(species = "Mus musculus",  category = "C2", subcategory = "KEGG")
# fgsea_sets<- m_df %>% split(x = .$gene_symbol, f = .$gs_name)
# 
# 
# cells_AUC <- AUCell_calcAUC(fgsea_sets, cells_rankings,nCores =1, aucMaxRank=nrow(cells_rankings)*0.1)
# grep("OX",rownames(cells_AUC@assays@data$AUC),value = T)

geneSet <- "KEGG_OXIDATIVE_PHOSPHORYLATION"
aucs <- as.numeric(getAUC(cells_AUC)[geneSet, ])
sc2$AUC <- aucs
df<- data.frame(sc2@meta.data, sc2@reductions$umap@cell.embeddings)
colnames(df)
class_avg <- df %>%
  group_by(seurat_clusters) %>%
  summarise(
    UMAP_1 = median(UMAP_1),
    UMAP_2 = median(UMAP_2)
  )

ggplot(df, aes(UMAP_1, UMAP_2))  +
  geom_point(aes(colour  = AUC)) + viridis::scale_color_viridis(option="A") +
  ggrepel::geom_label_repel(aes(label = seurat_clusters),
                            data = class_avg,
                            size = 3,
                            label.size = 1,
                            segment.color = NA
  )+   theme(legend.position = "none") + theme_bw() + facet_grid(.~orig.ident)