scMetabolism
参考：GitHub - wu-yc/scMetabolism: Quantifying metabolism activity at the single-cell resolution
1.包的安装
install.packages(c("devtools", "data.table", "wesanderson", "Seurat", "devtools", "AUCell", "GSEABase", "GSVA", "ggplot2","rsvd"))
devtools::install_github("YosefLab/VISION@v2.1.0") #Please note that the version would be v2.1.
devtools::install_github("wu-yc/scMetabolism")
2.计算代谢激活分数
subcell_sce是已经注释过的Seurat对象。
load("sce.Rdata")
countexp.Seurat<-sc.metabolism.Seurat(obj = subcell_sce,
method = "AUCell",
imputation = F, ncores = 2,
metabolism.type = "KEGG")
method有4种：VISION, AUCell, ssgsea和gsva。作者推荐VISION。
MedBioInfoCloud: rownames(countexp.Seurat@assays[["METABOLISM"]][["score"]])[1:6]
[1] "Glycolysis / Gluconeogenesis"
[2] "Citrate cycle (TCA cycle)"
[3] "Pentose phosphate pathway"
[4] "Pentose and glucuronate interconversions"
[5] "Fructose and mannose metabolism"
[6] "Galactose metabolism"
3.可视化
DimPlot.metabolism(obj = countexp.Seurat,
pathway = "Pentose phosphate pathway",
dimention.reduction.type = "umap",
dimention.reduction.run = F, size = 1)
DimPlot.metabolism(obj = countexp.Seurat,
pathway = "Citrate cycle (TCA cycle)",
dimention.reduction.type = "tsne",
dimention.reduction.run = F, size = 1)
input.pathway<-rownames(countexp.Seurat@assays[["METABOLISM"]][["score"]])[1:6]
DotPlot.metabolism(obj = countexp.Seurat, pathway = input.pathway,
phenotype = "seurat_clusters", norm = "y")
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