之前我们发过GSVA分析(有了这个包,猪的GSEA和GSVA分析也不在话下(第一集),【后续来了】有了这个包,猪的GSEA和GSVA分析也不在话下(第二集)),接着单细胞系列,重新说一下GSVA分析。
首先是数据集的问题,通常只做人和小鼠的,想要做其他物种的,苦于没有数据集,不过这里说的这个包,即使猪的GSVA分析也都可以做。
这里我们以小鼠为示例,也是常见物种的GSVA分析,结合单细胞的数据!GSVA其实就是对功能富集的量化,然后进行差异分析,寻找感兴趣的通路在样本中的变化。不同于常规的GO、KEGG受差异基因阈值的影响,GSEA受实验分组的影响,GSVA能够对通路量化,看感兴趣通路在多组之间的变化!
首先加载和安装必要的包并加载单细胞数据。
library(Seurat)
#source("http://bioconductor.org/biocLite.R")
#biocLite("GSVA")
library(GSVA)
library(tidyverse)
library(ggplot2)
library(clusterProfiler)
library(org.Mm.eg.db)
library(dplyr)
immuneT <- subset(immune, celltype="="T" cells")#提取我们需要分析的细胞类型 immunet <- as.matrix(immunet@assays$rna@counts)#提取count矩阵 meta immunet@meta.data[,c("orig.ident", "sex", "age", "stim", "samples")]#分组信息,为了后续作图 < code></->
之前一直苦于MSigDB数据库只有人的数据集,没有小鼠和其他物种的,网上也有教程如何根据基因同源性进行转化的,但是很麻烦,也不一定成功。还好有一个新的数据包被发现了,简直是福音—msigdbr包,可以做GSEA和GSVA。
#install.packages("msigdbr")
library(msigdbr)
msigdbr_species() #可以看到,这个包涵盖了20个物种
A tibble: 20 x 2
species_name species_common_name
<chr> <chr>
1 Anolis carolinensis Carolina anole, green anole
2 Bos taurus bovine, cattle, cow, dairy cow, domestic cattle, dome~
3 Caenorhabditis elegans roundworm
4 Canis lupus familiaris dog, dogs
5 Danio rerio leopard danio, zebra danio, zebra fish, zebrafish
6 Drosophila melanogaster fruit fly
7 Equus caballus domestic horse, equine, horse
8 Felis catus cat, cats, domestic cat
9 Gallus gallus bantam, chicken, chickens, Gallus domesticus
10 Homo sapiens human
11 Macaca mulatta rhesus macaque, rhesus macaques, Rhesus monkey, rhesu~
12 Monodelphis domestica gray short-tailed opossum
13 Mus musculus house mouse, mouse
14 Ornithorhynchus anatinus duck-billed platypus, duckbill platypus, platypus
15 Pan troglodytes chimpanzee
16 Rattus norvegicus brown rat, Norway rat, rat, rats
17 Saccharomyces cerevisiae baker's yeast, brewer's yeast, S. cerevisiae
18 Schizosaccharomyces pombe 9~ NA
19 Sus scrofa pig, pigs, swine, wild boar
20 Xenopus tropicalis tropical clawed frog, western clawed frog
查看下小鼠的基因集,是否与MSigDB数据库一样
mouse <- 5 msigdbr(species="Mus musculus" ) mouse[1:5,1:5] # a tibble: x gs_cat gs_subcat gs_name gene_symbol entrez_gene <chr> <chr> <chr> <chr> <int>
1 C3 MIR:MIR_Legacy AAACCAC_MIR140 Abcc4 239273
2 C3 MIR:MIR_Legacy AAACCAC_MIR140 Abraxas2 109359
3 C3 MIR:MIR_Legacy AAACCAC_MIR140 Actn4 60595
4 C3 MIR:MIR_Legacy AAACCAC_MIR140 Acvr1 11477
5 C3 MIR:MIR_Legacy AAACCAC_MIR140 Adam9 11502
table(mouse$gs_cat) #查看目录,与MSigDB一样,包含9个数据集
###C1 C2 C3 C4 C5 C6 C7 C8 H
20049 533767 795972 92353 1248327 30556 988358 109328 7411
</int></chr></chr></chr></-></chr></chr>
本例中,我们要分析GO,因为mouse文件包含了所有的基因集,所以要查看GO在哪里,然后将需要的文件提出来。
table(mouse$gs_subcat)
CGN CGP CM CP
167344 42770 376981 49583 3881
CP:BIOCARTA CP:KEGG CP:PID CP:REACTOME CP:WIKIPATHWAYS
4860 13694 8196 98232 27923
GO:BP GO:CC GO:MF HPO IMMUNESIGDB
660368 100991 105717 381251 944068
MIR:MIR_Legacy MIR:MIRDB TFT:GTRD TFT:TFT_Legacy VAX
34118 372658 235886 153310 44290
mouse_GO_bp = msigdbr(species = "Mus musculus",
category = "C5", #GO在C5
subcategory = "GO:BP") %>%
dplyr::select(gs_name,gene_symbol)#这里可以选择gene symbol,也可以选择ID,根据自己数据需求来,主要为了方便
mouse_GO_bp_Set = mouse_GO_bp %>% split(x = .$gene_symbol, f = .$gs_name)#后续gsva要求是list,所以将他转化为list
表达矩阵数据有了,通路信息有了,就可以进行GEVA分析了,代码简单就一句!保存结果!
T_gsva <- gsva(expr="immuneT," gset.idx.list="mouse_GO_bp_Set," kcdf="Poisson" , #查看帮助函数选择合适的kcdf方法 parallel.sz="5)" write.table(t_gsva, 't_gsva.xls', row.names="T," col.names="NA," sep="\t" ) < code></->
接着差异分析可以用limma包,类似于转录组芯片数据分析流程。
group <- c(rep("control", 50), rep("test", 71)) %>% as.factor()#设置分组,对照在前
desigN <- 0 model.matrix(~ + group) #构建比较矩阵 colnames(design) <- levels(group) fit="lmFit(test_control," design) fit2 ebayes(fit) diff="topTable(fit2,adjust='fdr',coef=2,number=Inf)#校准按照需求修改" ?toptable write.csv(diff, file="Diff.csv" ) < code></-></->
最后对差异的感兴趣的通路进行可视化:
up <- c("gobp_egg_activation", "gobp_tendon_development", "gobp_somite_specification", "gobp_threonine_catabolic_process", "gobp_regulation_of_glutamate_receptor_clustering", "gobp_negative_chemotaxis", "gobp_negative_regulation_of_fat_cell_proliferation", "gobp_regulation_of_t_helper_17_cell_lineage_commitment", "gobp_regulation_of_antimicrobial_humoral_response") down <- c("gobp_determination_of_pancreatic_left_right_asymmetry", "gobp_mitotic_dna_replication", "gobp_eosinophil_chemotaxis", "gobp_neutrophil_mediated_cytotoxicity", "gobp_potassium_ion_export_across_plasma_membrane", "gobp_regulation_of_leukocyte_mediated_cytotoxicity", "gobp_regulation_of_sequestering_of_zinc_ion", "gobp_endothelin_receptor_signaling_pathway", "gobp_pre_replicative_complex_assembly_involved_in_cell_cycle_dna_replication", "gobp_establishment_of_planar_polarity_of_embryonic_epithelium") test c(up,down) diff$id rownames(diff) q diff[test,] group1 c(rep("treat", 9), rep("control", 10)) df data.frame(id="Q$ID," score="Q$t,group=group1" ) # 按照t score排序 sortdf df[order(df$score),] sortdf$id factor(sortdf$id, levels="sortdf$ID)#增加通路ID那一列" ggplot(sortdf, aes(id, score,fill="group))" + geom_bar(stat="identity" ,alpha="0.7)" coord_flip() theme_bw() #去除背景色 theme(panel.grid="element_blank())+" theme(panel.border="element_rect(size" = 0.6))+ labs(x , y="t value of GSVA score" )+ scale_fill_manual(values="c("#008020","#08519C"))#设置颜色" < code></->
整个流程就结束了,希望对你们的研究能有启发,GO、KEGG做多了,可以换着做一下GSVA分析!
Original: https://blog.csdn.net/qq_42090739/article/details/124005072
Author: TS的美梦
Title: 跟着Cell学单细胞转录组分析(十三):单细胞GSVA分析|这个包涵盖大多数物种
原创文章受到原创版权保护。转载请注明出处:https://www.johngo689.com/640354/
转载文章受原作者版权保护。转载请注明原作者出处!