R 动态定义变量名 assign

rm(list=ls())
 
library(GSVA)
library(GSEABase)
library(GSVAdata)
library(msigdbr)
library(org.Hs.eg.db)
library(Seurat)
library(Rtsne)
setwd("/heartdata8t_A/zhangpeng/Final.results/Final_Project_III/GSVA")
 
### Merging the cannicalc2BroadSets
data(c2BroadSets)
canonicalC2BroadSets <- c2BroadSets[c(grep("^KEGG", names(c2BroadSets)),
                                      grep("^REACTOME", names(c2BroadSets)),
                                      grep("^BIOCARTA", names(c2BroadSets)))]
## Adding Hallmark genesets
m_df = msigdbr(species = "Homo sapiens", category = "H")
gset.all <- unique(m_df$gs_name)
 
for(geneset_i in :length(gset.all)){
  names_geneset_i <- gset.all[geneset_i]
  entrez_gene_geneset_i <- as.character(unique(m_df$entrez_gene[which(m_df$gs_name == gset.all[geneset_i])]))
  HallMarks <- GeneSet(entrez_gene_geneset_i, geneIdType=EntrezIdentifier(),
                 collectionType=BroadCollection(category="c2"), setName=names_geneset_i)
  canonicalC2BroadSets <- GeneSetCollection(c(canonicalC2BroadSets, HallMarks))
}
 
## Adding human-curated genesets (GEO/Cancer)
load("GC.signature.Rdata")
GC.signature <- as.character(mapIds(org.Hs.eg.db, GC.signature, 'ENTREZID', 'SYMBOL'))
GC.signature <- GeneSet(GC.signature, geneIdType=EntrezIdentifier(),
                     collectionType=BroadCollection(category="c2"), setName="GC.signature")
canonicalC2BroadSets <- GeneSetCollection(c(canonicalC2BroadSets, GC.signature))
 
load("GC.signature.literature.Rdata")
GC.signature.literature <- as.character(mapIds(org.Hs.eg.db, GC.signature.literature, 'ENTREZID', 'SYMBOL'))
GC.signature.literature <- GeneSet(GC.signature.literature, geneIdType=EntrezIdentifier(),
                        collectionType=BroadCollection(category="c2"), setName="GC.signature.literature")
canonicalC2BroadSets <- GeneSetCollection(c(canonicalC2BroadSets, GC.signature.literature))
 
# Computing the GSVA score based the above curated datasets
load("subset.epithelial.cells.gc.signature.Rdata")
processed.data <- as.matrix(epithelial.cells.re.subset.GC.signature@assays$SCT@counts)
 
rownames.processed.data <- rownames(processed.data)
entrez.rownames.processed.data <- as.character(mapIds(org.Hs.eg.db, rownames.processed.data , 'ENTREZID', 'SYMBOL'))
 
invalid.index <- which(is.na(entrez.rownames.processed.data))
processed.data <- processed.data[-invalid.index,]
rownames(processed.data) <- entrez.rownames.processed.data[-invalid.index]
 
esmicro.processed <- gsva(processed.data, canonicalC2BroadSets, min.sz=, max.sz=,
                          mx.diff=TRUE, verbose=FALSE, parallel.sz=, kcdf = "Poisson")
## Results
# library(pheatmap)
# esmicro.processed <- esmicro.processed[grep("KEGG",rownames(esmicro.processed)),]
# pheatmap(esmicro.processed)
 
save(esmicro.processed,file = "esmicro.processed.Rdata")
tsne <- Rtsne(esmicro.processed,dims = )
plot(tsne$Y)
 
epi<-epithelial.cells.re@meta.data
bb<-epi$SCT_snn_res.[which(epi$SCT_snn_res. ==)]  
 
bb<-epi[which(epi[,]==),]
cc<-epi[which(epi[,]==),]
aa<-rbind(bb,cc)
g1.index<-rownames(aa)
pro<- esmicro.processed
g1<-pro[ , which(colnames(pro) %in% g1.index )]
g2<-pro[ , -which(colnames(pro) %in% g1.index )]
 
heatmap(pro)
 
pathway_name = rownames(g1)
tm <- list('P-value' = c(), 'Pathway_name' = c())
 
cell.types <- unique(Idents(epithelial.cells.re))
 
library(pryr)
#setClass("All_results", slots = list(C0_results = "data.frame"))
#all_results <- new("All_results", C0_results = tm)
 
cell.types
 
#for(i in :length(cell.types)){
#  print((as.numeric(cell.types[i])))
  #name = paste0("tm_",as.numeric(cell.types[i]))
  #eval(parse( name )   )<- list('P-value' = c(), 'Pathway_name' = c())
  #append(all_ans,list(name = c()))
 
#}
 
for(j in cell.types){
  cc<-epi[which(epi[,]==j),]
  index<-rownames(cc)
  g1<-pro[ , which(colnames(pro) %in% index )]
  pathway_name = rownames(g1)
  g2<-pro[ , -which(colnames(pro) %in% index )]
  tm <- list('P-value' = c(), 'Pathway_name' = c())
 ## t.test
 for(i in :dim(g1)[]){
   results<- t.test(g1[i,],g2[i,])$p.value
   tm$`P-value`<-append(tm$`P-value`,results)
   tm$Pathway_name<- append(tm$Pathway_name,pathway_name[i])
 }
  assign(paste("tm_",j,sep = ""),tm)
}
 
print(results)
 
  data.frame()
  p.val,pathway_name
 
  result[[celltype_i]] <- data.frame 
 
dftm<- data.frame(tm)
dftm <- dftm[sort(dftm$P.value,index.return=TRUE)$ix,]
 
down <- sample(colnames(pro),round(nrow(pro)/))
a<-pro[,down]
heatmap(a)
 
raw.data
 
result <- list()
 
cell.types <- unique(Idents(epithelial.cells.re))
 
for(celltype_i in cell.types){
 
  ## t.test
 
   data.frame()
  p.val,pathway_name
 
  result[[celltype_i]] <- data.frame 
 
}

主要部分“

 for(j in cell.types){
   cc<-epi[which(epi[,]==j),]
   index<-rownames(cc)
   g1<-pro[ , which(colnames(pro) %in% index )]
   pathway_name = rownames(g1)
   g2<-pro[ , -which(colnames(pro) %in% index )]
   tm <- list('P-value' = c(), 'Pathway_name' = c())
  ## t.test
  for(i in :dim(g1)[]){
    results<- t.test(g1[i,],g2[i,])$p.value
    tm$`P-value`<-append(tm$`P-value`,results)
    tm$Pathway_name<- append(tm$Pathway_name,pathway_name[i])
  }
   assign(paste("tm_",j,sep = ""),tm)
 }
 
 print(results)