相关论文：J Adv Res. 2024 May 7:S2090-1232(24)00183-8.
library(scMetabolism)library(tidyverse)library(rsvd)library(Seurat)library(pheatmap)library(ComplexHeatmap)library(ggsci)load(file = "pbmc_demo.rda")countexp.Seurat@meta.data$celltype <- Idents(countexp.Seurat)countexp.Seurat <- sc.metabolism.Seurat(obj = countexp.Seurat,method = "VISION",imputation = F, ncores = 2,metabolism.type = "KEGG")metabolism.matrix <- countexp.Seurat@assays$METABOLISM$scoremetabolism.matrixDimPlot.metabolism(obj = countexp.Seurat,pathway = "Glycolysis / Gluconeogenesis",dimention.reduction.type = "umap",dimention.reduction.run = F, size = 1.5)
input.pathway <- rownames(countexp.Seurat[["METABOLISM"]][["score"]])[1:30]DotPlot.metabolism(obj = countexp.Seurat,pathway = input.pathway,phenotype = "celltype",norm = "y")
input.pathway <- rownames(countexp.Seurat[["METABOLISM"]][["score"]])[1:2]BoxPlot.metabolism(obj = countexp.Seurat,pathway = input.pathway,phenotype = "celltype", ncol = 1)
sce_Metal_exp = countexp.Seuratsce_Metal_exp$celltype = sce_Metal_exp$celltypemscore_data = data.frame(t(sce_Metal_exp@assays[["METABOLISM"]][["score"]]),sce_Metal_exp$celltype)avg_sM=aggregate(mscore_data[,1:ncol(mscore_data)-1],list(mscore_data$sce_Metal_exp.celltype),mean)rownames(avg_sM) = avg_sM$Group.1avg_sM=data.frame(t(avg_sM[,-1]))avg_sM$KEGG = rownames(sce_Metal_exp@assays[["METABOLISM"]][["score"]])rownames(avg_sM)=avg_sM$KEGGc_k_l = c()for(c in c(1:ncol(avg_sM))){c_k=avg_sM[order(avg_sM[,c]),]$KEGG[1:5]c_k_l=c(c_k_l,c_k)}c_k_l= unique(c_k_l)c_k_d = avg_sM[avg_sM$KEGG %in%c_k_l,]rownames(c_k_d) = c_k_d$KEGGpheatmap::pheatmap(c_k_d[,-ncol(c_k_d)],show_colnames = T,scale='row')
吴博士开发的scMetabolism还是挺好的。不过，我们在用自己的数据进行分析时，有个小bug，就是需要把counts数写入RNA。这样就可以用于单细胞数据代谢分析了。当然，最好吴博有空修正下里面的小bug！期待ing~
