AUCell
1.提取指定GOterm的基因
在这篇文章里提到，Reactive oxygen species (ROS) 活性氧相关的17个GO条目,可以把这些基因提取出来。使用msigdbr包搞定。Data_Sheet_2.XLSX是文章的附件
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7606976/bin/Data_Sheet_2.XLSX
rm(list = ls())
library(tidyverse)
dat = rio::import("Data_Sheet_2.XLSX")
g = dat[-1,1]
g
## [1] "GO_REACTIVE_OXYGEN_SPECIES_BIOSYNTHETIC_PROCESS"
## [2] "GO_REACTIVE_OXYGEN_SPECIES_METABOLIC_PROCESS"
## [3] "GO_POSITIVE_REGULATION_OF_REACTIVE_OXYGEN_SPECIES_BIOSYNTHETIC_PROCESS"
## [4] "GO_NEGATIVE_REGULATION_OF_REACTIVE_OXYGEN_SPECIES_BIOSYNTHETIC_PROCESS"
## [5] "GO_NEGATIVE_REGULATION_OF_REACTIVE_OXYGEN_SPECIES_METABOLIC_PROCESS"
## [6] "GO_POSITIVE_REGULATION_OF_REACTIVE_OXYGEN_SPECIES_METABOLIC_PROCESS"
## [7] "GO_MITOCHONDRIAL_ELECTRON_TRANSPORT_CYTOCHROME_C_TO_OXYGEN"
## [8] "GO_SUPEROXIDE_GENERATING_NADPH_OXIDASE_ACTIVITY"
## [9] "GO_HYDROGEN_PEROXIDE_METABOLIC_PROCESS"
## [10] "GO_HYDROGEN_PEROXIDE_CATABOLIC_PROCESS"
## [11] "GO_PENTOSE_METABOLIC_PROCESS"
## [12] "GO_RESPONSE_TO_REACTIVE_OXYGEN_SPECIES"
## [13] "GO_REGULATION_OF_RESPONSE_TO_REACTIVE_OXYGEN_SPECIES"
## [14] "GO_CELLULAR_RESPONSE_TO_REACTIVE_OXYGEN_SPECIES"
## [15] "GO_REGULATION_OF_REACTIVE_OXYGEN_SPECIES_BIOSYNTHETIC_PROCESS"
## [16] "GO_REGULATION_OF_REACTIVE_OXYGEN_SPECIES_METABOLIC_PROCESS"
## [17] "GO_NEGATIVE_REGULATION_OF_RESPONSE_TO_REACTIVE_OXYGEN_SPECIES"
17条term👆
2.从msigdbr查找这些term
library(msigdbr)
GO_df = msigdbr(species = "Homo sapiens",category = "C5") %>%
dplyr::select(gene_symbol,gs_name,gs_subcat)
dim(GO_df)
## [1] 1424471 3
table(GO_df$gs_subcat)
##
## GO:BP GO:CC GO:MF HPO
## 721379 115769 122674 464649
GO_df = GO_df[GO_df$gs_subcat!="HPO",]
table(g %in% GO_df$gs_name)
##
## FALSE
## 17
全部匹配失败啦？
其实是因为前缀不同
head(GO_df$gs_name,3)
## [1] "GOBP_10_FORMYLTETRAHYDROFOLATE_METABOLIC_PROCESS"
## [2] "GOBP_10_FORMYLTETRAHYDROFOLATE_METABOLIC_PROCESS"
## [3] "GOBP_10_FORMYLTETRAHYDROFOLATE_METABOLIC_PROCESS"
head(g,3)
## [1] "GO_REACTIVE_OXYGEN_SPECIES_BIOSYNTHETIC_PROCESS"
## [2] "GO_REACTIVE_OXYGEN_SPECIES_METABOLIC_PROCESS"
## [3] "GO_POSITIVE_REGULATION_OF_REACTIVE_OXYGEN_SPECIES_BIOSYNTHETIC_PROCESS"
把前缀去掉来匹配，顺便把大写变成小写，变得易读一点。
sn = GO_df$gs_name %>%
str_to_lower()%>%
str_replace("_","///") %>%
str_split("///",simplify = T)%>%
.[,2] %>%
str_replace_all("_"," ")
head(sn)
## [1] "10 formyltetrahydrofolate metabolic process"
## [2] "10 formyltetrahydrofolate metabolic process"
## [3] "10 formyltetrahydrofolate metabolic process"
## [4] "10 formyltetrahydrofolate metabolic process"
## [5] "10 formyltetrahydrofolate metabolic process"
## [6] "10 formyltetrahydrofolate metabolic process"
GO_df = mutate(GO_df,short_name = sn)
g = str_to_lower(g)%>%
str_replace("_","///") %>%
str_split("///",simplify = T)%>%
.[,2] %>%
str_replace_all("_"," ")
head(g)
## [1] "reactive oxygen species biosynthetic process"
## [2] "reactive oxygen species metabolic process"
## [3] "positive regulation of reactive oxygen species biosynthetic process"
## [4] "negative regulation of reactive oxygen species biosynthetic process"
## [5] "negative regulation of reactive oxygen species metabolic process"
## [6] "positive regulation of reactive oxygen species metabolic process"
table(g %in% GO_df$short_name)
##
## FALSE TRUE
## 1 16
经过数据框搜索，发现匹配不上的那个其实是因为少了俩空格。。。
所以直接改一下就好。
“superoxide generating nadph oxidase activity”
“superoxide generating nad p h oxidase activity”
g[!(g %in% GO_df$short_name)] = "superoxide generating nad p h oxidase activity"
table(g %in% GO_df$short_name)
##
## TRUE
## 17
rosgene = GO_df %>%
filter(short_name %in% g) %>%
pull(gene_symbol) %>%
unique()
length(rosgene)
## [1] 402
2.AUCell
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9897923/
用这个文章里的方法，将单细胞亚群的marker基因与ros相关基因取交集，用作AUCell的基因集
The intersection of marker genes was selected based on strong population specificity (adj_p < 0.05 & |avg_log2FoldChange| > 1.5 & pct.1 > 0.5 & pct.2 < 0.5) from each cell subgroup and factors related to OS responses.
sce.all是seurat+singleR得出的对象，markers是findallmarkers得到的数据框。
load("sce.all.Rdata")
load("makers.Rdata")
k = markers$p_val_adj < 0.05 &
abs(markers$avg_log2FC) > 1.5 &
markers$pct.1 > 0.5 &
markers$pct.2 < 0.5
table(k)
## k
## FALSE TRUE
## 13100 1483
gi = intersect(markers[k,"gene"],rosgene)
gi = factor(gi,levels = gi)
library(Seurat)
DotPlot(sce.all,features = gi,cols = "RdYlBu",
group.by = "RNA_snn_res.0.5")+RotatedAxis()
AUCell用于计算每个细胞中特定基因集的活性程度。用上面的gi作为基因集来计算。
AUCell的三个步骤:
Build the rankings：矩阵中的每个细胞里，给基因进行排序。 Calculate the Area Under the Curve (AUC)：计算每个细胞的AUC值 Set the assignment thresholds：计算活性区分的阈值
library(GSEABase)
geneSets <- GeneSet(as.character(gi), setName="ROS")
geneSets
## setName: ROS
## geneIds: ACP5, SOD3, ..., PMAIP1 (total: 32)
## geneIdType: Null
## collectionType: Null
## details: use 'details(object)'
library(AUCell)
exprMatrix = sce.all@assays$RNA@data
cells_rankings <- AUCell_buildRankings(exprMatrix)
## min 1% 5% 10% 50% 100%
## 474 716 794 859 1409 5996
cells_AUC <- AUCell_calcAUC(geneSets, cells_rankings)
set.seed(333)
cells_assignment <- AUCell_exploreThresholds(cells_AUC, plotHist=TRUE, assign=TRUE)
auc_thr = cells_assignment$ROS$aucThr$selected
auc_thr
## L_k2
## 0.07917315
3.FeaturePlot可视化
标准版：
library(Seurat)
a = as.numeric(getAUC(cells_AUC))
sce.all$auc_score = a
FeaturePlot(sce.all,features = "auc_score")
定制版：
dat<- data.frame(sce.all@meta.data,
sce.all@reductions$umap@cell.embeddings,
sce.all@active.ident)
colnames(dat)[ncol(dat)] = "seurat_annotation"
class_avg <- dat %>%
group_by(seurat_annotation) %>%
summarise(
UMAP_1 = median(UMAP_1),
UMAP_2 = median(UMAP_2)
)
library(ggpubr)
ggplot(dat, aes(UMAP_1, UMAP_2)) +
geom_point(aes(colour = auc_score)) + viridis::scale_color_viridis(option="A") +
ggrepel::geom_label_repel(aes(label = seurat_annotation),
data = class_avg,
label.size = 0,
segment.color = NA)+
theme(legend.position = "none") + theme_bw()
dat$auc_group = ifelse(dat$auc_score>auc_thr,"active","non_active")
4.活跃和不活跃组的细胞比例条形图
ggplot(dat,aes(auc_group,fill = seurat_annotation))+
geom_bar(position = "fill", alpha = 0.9)+
scale_fill_brewer(palette = "Set1")+
theme_classic()
网友评论