library("Seurat")
library("ggplot2")
library("reshape2")
library("tidyr")
library(data.table)
library(Matrix)

options(future.globals.maxSize=100000000000)
args=c("")
base=basename(args[1])
dir=dirname(args[1])
prefix=sub(".txt","",base)
genes=args[2]
genes=as.matrix(strsplit(genes,",")[[1]])
color=c(rgb(230/256,70/256,53/256),rgb(77/256,187/256,213/256),rgb(60/256,84/256,136/256),rgb(247/256,155/256,127/256),"#CDD788","#2B5F82","#246B47","#9E6ECF","#8E522F","#A7E2A8","#74C95E","#BBA23E","#297A6A","#85B3D6","#431D58","#4754C2","#9CDED6","#A16836","#85D6B1","#B08D3B","#C65753","#A63BB0","#3DB89F","#329130","#DE9D9C","#C590DA","#CC66AA","#822E2B","#C15D44","#8FC247","#173745","#605320","#5BC899","#D2AD79","#C9C95E","royalblue1","royalblue","olivedrab4","mintcream","turquoise2","saddlebrown","rosybrown2","steelblue4","midnightblue","paleturquoise4","wheat2")
read<-function(mat,bin){
  data<-fread(mat,header=T)
  data$cellID<-paste(as.character(round(data$x/bin, digits = 0)),"_",as.character(round(data$y/bin, digits = 0)),sep="")
  gene=unique(data$geneID)
  cell=unique(data$cellID)
  gene_idx=c(1:length(gene))
  cell_idx=c(1:length(cell))
  names(gene_idx)=gene
  names(cell_idx)=cell
  print(head(gene_idx[data$geneID]))
  mat=sparseMatrix(i=gene_idx[data$geneID],j=cell_idx[data$cellID],x=data$MIDCount)
  rownames(mat)=gene
  colnames(mat)=cell
  return(mat)
}

analyze <- function(mat,bin){
  data=mat
  data[is.na(data)]=0
  SeuObj<-CreateSeuratObject(counts = data,names.delim = "-", project = "SeuObj")
  SeuObj[["percent.mt"]] <- PercentageFeatureSet(SeuObj, pattern = "^mt-|^MT-|^Mt-")
  p <-  VlnPlot(SeuObj,features=c("nCount_RNA","nFeature_RNA","percent.mt"),group.by="orig.ident")
  ggsave(file=paste0(prefix,".VlnPlot.pdf"),plot=p)
 
  SeuObj <- SCTransform(SeuObj, vars.to.regress = "percent.mt", verbose = FALSE)
  
  SeuObj <- RunPCA(SeuObj, verbose = FALSE)
  
  SeuObj <- RunUMAP(SeuObj, dims = 1:30, verbose = FALSE)
  
  SeuObj <- FindNeighbors(SeuObj, dims = 1:30, verbose = FALSE)
  
  SeuObj <- FindClusters(SeuObj, resolution = 0.3, verbose = FALSE)
  
  SeuObj@meta.data$coor_x=sub(rownames(SeuObj@meta.data),pattern = "_.*",replacement = "")
  SeuObj@meta.data$coor_y=sub(rownames(SeuObj@meta.data),pattern = ".*_",replacement = "")
  SeuObj@meta.data$coor_x=sub(SeuObj@meta.data$coor_x,pattern = "X",replacement = "")
  SeuObj@meta.data$coor_x=as.integer(SeuObj@meta.data$coor_x)
  SeuObj@meta.data$coor_y=as.integer(SeuObj@meta.data$coor_y)
  AllMG <<- FindAllMarkers(SeuObj)
  SeuObj <<- SeuObj
  p<-ggplot(SeuObj@meta.data,aes(x=coor_x,y=coor_y,color=nCount_RNA))+geom_point()+coord_fixed()+theme_void()+scale_color_viridis_c(option="B")
  ggsave(file=paste0(prefix,".nCount_RNA.pdf"),plot=p)
  p<-ggplot(SeuObj@meta.data,aes(x=coor_x,y=coor_y,color=nFeature_RNA))+geom_point()+coord_fixed()+theme_void()+scale_color_viridis_c(option="B")
  ggsave(file=paste0(prefix,".nFeature_RNA.pdf"),plot=p)
  
  apply(
    X=genes,
    MARGIN=1,
    FUN=function(x){
      if(x %in% rownames(SeuObj@assays$SCT@data)){
        name=paste0(prefix,"-",x,".pdf")
        p<-ggplot(SeuObj@meta.data,aes(x=coor_x,y=coor_y,fill=SeuObj@assays$SCT@data[x,]))+geom_tile()+oord_fixed()+ theme_void()+scale_color_viridis_c(option = "B",direction=-1)+labs(title=x,color="")
        ggsave(file=name, plot=p)
      }
    })
  
  filename=paste(dir,"/bin",bin,".MG",sep="")
  write.table(AllMG,file=filename)
  save(SeuObj,file=paste(dir,"/bin",bin,".Rdata",sep=""))
}

bins=c(50)
for(bin in bins){
  mat<-read(args[1],bin)
  analyze(mat,bin)
}