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 

}

 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)