##Posted 6/15/2017

  options(digits = 11)

  #Libraries required to run the code
  library(pryr)
  library(MASS)
  library(dplyr)
  library(tidyr)
  library(readr)
  library(stringr)
  
  
  #Necessary Functions
  #1#Function for handling the changing of row names and column names
  chngrownm <- function(mat){
  	row <- dim(mat)[1]
  	col <- dim(mat)[2]
  	j <- 1
  	x <- 1
  	p <- 1	
  	a <- 1 
  	b <- 1
  	g <- 1
  	for(j in 1:col){
  		if("!Sample_source_name_ch1"==mat[1,j]){
  			colnames(mat)[j] <- "Brain_Region"	

  		} else if("!Sample_title" == mat[1,j]){

  			colnames(mat)[j] <- "Title"

  		} else if("!Sample_geo_accession" == mat[1,j]){

  			colnames(mat)[j] <- "ID_REF"
  		} else{
  			if(grepl("Sex|gender|Gender|sex",mat[2,j])==TRUE){
  				colnames(mat)[j] <- paste0("Sex",x)
  				x = x + 1
  			}
  			if(grepl("postmorteminterval|PMI|pmi",mat[2,j])==TRUE){
  				colnames(mat)[j] <- paste0("PMI",p)
  				p = p + 1
  			}
  			if(grepl("age|Age|AGE",mat[2,j])==TRUE){
  				colnames(mat)[j] <- paste0("Age",a)
  				a = a + 1
  			 }
  			if(grepl("braak|b&b",mat[2,j])==TRUE){
  				colnames(mat)[j] <- paste0("Braak",b)
  				b = b + 1
  			}

  			if(grepl("group|disease|control|AD|normal|diagnosis|Alzheimer|Control|Normal",mat[2,j])==TRUE){

  				colnames(mat)[j] <- paste0("Group",g)
  				g = g + 1
  			}
  			
  		}
  		j = j + 1
  	}
  	mat
  }			
  
  #2#Function for reorganizing information within the columns
  cinfo <- function(mat){
  	col <- dim(mat)[2]
  	j <-2
  	for(j in 2:col){
  		if(grepl("Group",colnames(mat)[j]) == TRUE){
  			mat[,j] <- gsub(".+:\\s|\\s.+;.+","",mat[,j])
  		}
  		if(grepl("Age",colnames(mat)[j])==TRUE){
  			mat[,j] <- gsub("\\D","",mat[,j])%>%
  				as.integer()
  		}
  		if(grepl("Sex",colnames(mat)[j])==TRUE){
  			mat[,j] <- gsub(".+:\\s","",mat[,j])
  		}
  		if(grepl("PMI",colnames(mat)[j])==TRUE){
  			mat[,j] <- gsub("[^0-9\\.]","",mat[,j])%>%
  				as.numeric() 
  		}
  		if(grepl("Braak",colnames(mat)[j])==TRUE){
  			mat[,j]<-gsub(".+:\\s","",mat[,j])%>%
  				as.roman()%>%
  				as.integer()
  		}
  	j=j+1
  	}
  	mat
  }
  
  #3#Function for labeling the gene IDs without names
  NAFIXING <- function(GIDNAM){
  	row <- dim(GIDNAM)[1]
  	i <- 1
  	for(i in 1:row){
  		if(grepl("^NA\\s*$",GIDNAM[i,2])==TRUE||is.na(GIDNAM[i,2])==TRUE){
  			GIDNAM[i,2] <- GIDNAM[i,1]
  		}
  		i <- i + 1
  	}
  	GIDNAM
  }	
  
  #4#Function for changing the gene ID to gene name
  cgeneID <- function(GeneName,DATA){

  	nj <- t(GeneName)
  	nq <- t(DATA)
  	colGene <- dim(nj)[2]
  	colDATA <- dim(nq)[2]
  	j <- 1
  	for(j in 1:colDATA){
  		#where is that gene id located within the GPL file
  		chngreq <- grep(paste0("^",nq[1,j],"$"),nj[1,])
  		if(is.na(sum(chngreq))==FALSE){
  			if(sum(chngreq) > 0){
  			nq[1,j] <- gsub(paste0("^",nq[1,j],"$"),nj[2,chngreq],nq[1,j])
  			}

  		}

  		j <- j + 1

  	}

  	nq

  }

  #cgeneID <- function(GeneName,DATA){
  #    colGene <- dim(GeneName)[2]
  #     j <- 1
  #     for(j in 1:colGene){
  #	chngsreq <- grep(paste0("^",GeneName[1,j],"$"),DATA[1,])
  #	if(is.na(sum(chngsreq))==FALSE){
  #		if(sum(chngsreq) > 0){
  #			DATA[1,chngsreq] <- gsub(paste0("^",GeneName[1,j]),GeneName[2,j],DATA[1,chngsreq])
  #		}
  #	}
  #		#if(sum(chngsreq) > 0){
  #		##DATA[1,chngsreq] <- gsub(GeneName[1,j],GeneName[2,j],DATA[1,chngsreq])
  #		#DATA[1,chngsreq] <- gsub(paste0("^",GeneName[1,j]),GeneName[2,j],DATA[1,chngsreq])
  #		#}
  #	j = j+1
  #	}
  #	DATA
  #}

  
  #5#Function for adjusting the gene names
  gcnames <- function(DiData,usecol=1){
  	nuruns <- dim(DiData)[2]
  	i = 1
  	nwnam <- rep("0",length.out=nuruns)
  	for(i in 1:nuruns){

  		if(length(strsplit(colnames(DiData)[i],"///|//")[[1]]) >= usecol){
  			nwnam[i]=str_trim(strsplit(colnames(DiData)[i],"///|//")[[1]][usecol])

  		} else{

  			nwnam[i]=str_trim(strsplit(colnames(DiData)[i],"///|//")[[1]][1])

  		}
  		
  	}
  	nwnam
  
  }
  
  #6# Function for discretizing the data 
  dndat <- function(NDATA){
  	rownd <- dim(NDATA)[1]
  	colnd <- dim(NDATA)[2]
  	DDATA <- matrix(0,nrow=rownd,ncol=colnd)
  	colnames(DDATA) <- colnames(NDATA)
  	i <- 1
  	for(i in 1:rownd){
  		j <- 1
  		for(j in 1:colnd){
  			if(is.na(NDATA[i,j])==FALSE){
  			
  				if(NDATA[i,j] < -1){
  					DDATA[i,j]=0L

  				} else if(NDATA[i,j] > 1){

  					DDATA[i,j]=2L

  				} else if(-1 <= NDATA[i,j] && NDATA[i,j] <= 1){

  					DDATA[i,j]=1L
  				}
  			} else{
  				DDATA[i,j] = NDATA[i,j]
  			}
  			j = j + 1			
  		}
  		i = i + 1
  	}
  	DDATA
  }
  
  
  #The Rest of this code will be used every time you want to change a data set
  
  #Getting the series matrix file
  print("Choose the series matrix file that you want to Analyze")
  alz <- file.choose()
  
  #Getting the GPL file
  print("Choose the GPL file that correlates with the above series matrix file")
  genena <- file.choose()
  
  
  #Find out if it is a soft GPL file or not
  soft <- strsplit(genena,"[\\|/]") %>%
  	.[[1]] %>%
  	.[length(.)] %>%
  	grepl("soft|annot",.)
  
  #Working with the wordy part of the document
  alzword <- alz %>%
  	read_delim(delim ="\t",comment = "!Series",col_names = FALSE)%>%
  	filter(grepl("!Sample",X1))%>%
  	filter(!grepl("!Sample_contact",X1))
  
  ##Changing row names and column names:
  ALZWORD <- t(alzword)
  rownames(ALZWORD)=NULL
  colnames(ALZWORD) <- colnames(ALZWORD,do.NULL=FALSE)
  ALZWORD <- chngrownm(ALZWORD)[-1,]
  ALZWORD <- ALZWORD%>%

  	as.data.frame(.,stringsAsFactors = FALSE)%>%

  	dplyr::select(-starts_with("col"))
  
  ##Reorganizing information within the columns
  ALZWORDF <- cinfo(ALZWORD)
  
  
  #Working with Actual Data part of file
  alzdat <- alz %>% 
  	read_delim(delim="\t",col_names=TRUE,comment = "!",skip=1)
  ALZDAT <- t(alzdat[,-1])
  rownames(ALZDAT)=NULL
  
  ##Is there a clean version of the GPL file available?
  gplnum <- strsplit(genena,"[\\|/]") %>%
  	.[[1]] %>%
  	.[length(.)] %>%
  	gsub("\\D","",.)
  clfileex <- sum(grepl(paste0("Clean_GPL",gplnum),list.files()))
  if(clfileex >= 1){
  #use the clean version
  geneIDNam <-  paste0("Clean_GPL",gplnum,".txt") %>%
  	read_delim(delim="\t",col_names = c("ID","Symbol"), comment = "!")

  } else if(clfileex == 0){

  ##Lets Create a clean version
   
  ##Gene ID to Gene Name
  	if(soft == TRUE){
  		#Check to see if there is already a file containing information on soft files
  		fileex <- sum(grepl("GPL_ID_LOC.txt",list.files()))
  		if(fileex == 1){
  			#Check to see if this GPL soft file has been used before
  			IDF <- read_delim("GPL_ID_LOC.txt",delim = "\t",col_names = TRUE) %>%
  				.$GPL_FILE_NUM%>%
  				grepl(gplnum,.) %>%
  				sum()
  			if(IDF == 1){
  				IDLOCAL <- read_delim("GPL_ID_LOC.txt",delim = "\t",col_names = TRUE) %>%
  					.$GPL_FILE_NUM%>%
  					grep(gplnum,.)
  				idlocgpl <- read_delim("GPL_ID_LOC.txt",delim = "\t",col_names = TRUE) %>%
  					.$LOC_ID %>%
  					.[IDLOCAL]
  				geneIDNam <- genena %>%
  					read_delim(delim="\t",col_names = TRUE, comment = "!", skip = idlocgpl) %>%

  					dplyr::select(.,ID,grep("Symbol|^ORF\\s*$|^gene_assignment\\s*$|^Gene symbol$",colnames(.)))

  			} else if(IDF == 0){

  				#No information on this particular GPL file
  				idLOCGPL <- genena %>%
  					read_delim(delim="\t",col_names = FALSE, comment = "!", n_max = 1000) %>%
  					t(.) %>%
  					grep("^ID\\s*$",.) %>%
  					-1
  				cbind(as.integer(gplnum),as.integer(idLOCGPL)) %>%
  					cat(file="GPL_ID_LOC.txt",sep = "\t", fill = TRUE, append = TRUE)
  				geneIDNam <- genena %>%
  					read_delim(delim="\t",col_names = TRUE, comment = "!", skip = idLOCGPL) %>%

  					dplyr::select(.,ID,grep("Symbol|^ORF\\s*$|^gene_assignment\\s*$|^Gene symbol$",colnames(.)))

  			}

  		} else if(fileex == 0){

  			#We must create a file that we can access for later use
  			idLOCGPL <- genena %>%
  				read_delim(delim="\t",col_names = FALSE, comment = "!", n_max = 1000) %>%
  				t(.) %>%
  				grep("^ID\\s*$",.) %>%
  				-1
  			Firstval <- cbind(as.integer(gplnum),as.integer(idLOCGPL))
  			colnames(Firstval) <- c("GPL_FILE_NUM","LOC_ID")
  			write.table(Firstval,file = "GPL_ID_LOC.txt", sep = "\t",row.names = FALSE, col.names = TRUE)
  			geneIDNam <- genena %>%
  				read_delim(delim="\t",col_names = TRUE, comment = "!", skip = idLOCGPL) %>%

  				dplyr::select(.,ID,grep("Symbol|^ORF\\s*$|^gene_assignment\\s*$|^Gene symbol$",colnames(.)))

  		}

  	 } else if(soft == FALSE){

  		geneIDNam <- genena %>%
  		read_delim(delim="\t",comment = "#")%>%

  		dplyr::select(.,ID,grep("Symbol|^ORF\\s*$|^gene_assignment\\s*$|^Gene symbol$",colnames(.)))

  	}
  
  	##Labeling the gene IDs without names
  	geneIDNam <- NAFIXING(geneIDNam)	
  
  	##remove the whitespace
  	geneIDNam <- t(rbind(str_trim(t(geneIDNam)[1,]),str_trim(t(geneIDNam)[2,])))
  	
  	##Here is the clean version
  	write.table(geneIDNam,file = paste0("Clean_GPL",gplnum,".txt"),sep = "\t",row.names = FALSE, col.names = FALSE)
  }
  
  
  
  ##Changing the gene ID to gene name

  ALZDAT1 <- cgeneID(geneIDNam,alzdat)

  colnames(ALZDAT) = ALZDAT1[1,]
  
  
  ##Adjusting the column names aka the gene names
  colnames(ALZDAT) <- gcnames(ALZDAT)
  
  
  #Full RAW Data
  Fullalzdwr <- ALZDAT %>%

  	as.data.frame(.,stringsAsFactors = FALSE) %>%

  	cbind(ALZWORDF,.)
  
  
  #Raw file is output
  nfnaex <- strsplit(alz,"[\\]") %>%
  	.[[1]] %>%
  	.[length(.)] %>%
  	gsub("\\D","",.) %>%
  	c("GSE",.,"aftexcel.txt") %>%
  	paste(collapse = "")
  write.table(t(Fullalzdwr), file = nfnaex, sep = "\t")
  
  
  #Now for the discretization part
  ##get the wordy part again
  rawword <- t(ALZWORDF)
  
  ##where is ID_REF located

  hereim <- grep("ID_REF",rownames(rawword))

  
  ##Subject Names GSM...
  subjnam <- rawword[hereim,]
  
  ##Getting the names for the rows
  namedarows <- rownames(rawword)[-hereim] %>%

  	as.data.frame(.,stringsAsFactors = FALSE)

  RAWWORD <- rawword[-hereim,] %>%

  	as.data.frame(.,stringsAsFactors = FALSE) %>%

  	bind_cols(namedarows,.)
  z <- 1
  naroww <- as.data.frame(rep(0,dim(RAWWORD)[1]),stringsAsFactors = FALSE)
  for(z in 1:dim(RAWWORD)[1]){

      if(sum(is.na(RAWWORD[z,])) > 0){
          naroww[z,1] <- as.integer(sum(is.na(RAWWORD[z,])))
      }
  	if(length(grep("NA",RAWWORD[z,])) > 0){
          naroww[z,1] <- as.integer(length(grep("NA",RAWWORD[z,]))) + naroww[z,1]
      }
      z <- z + 1

  }
  
  colnames(naroww) <- "ROW_NAs"
  RAWWORD <- bind_cols(RAWWORD,naroww)
  
  
  roALZna <- t(ALZDAT) %>%
  	rownames(.) %>%

  	as.data.frame(.,stringsAsFactors = FALSE)

  colnames(roALZna) <- "ID_REF"
  
  RAWDAT <- t(ALZDAT) %>%

  	as.data.frame(.,stringsAsFactors = FALSE)

  colnames(RAWDAT) <- NULL
  rownames(RAWDAT) <- NULL
  
  RAWDAT2 <- RAWDAT %>%
  	cbind(roALZna,.) %>%
  	dplyr::arrange(.,ID_REF)
  
  ##Editing the file for R processing
  RAWDATID <- RAWDAT2[,1] %>%
  	as.matrix(.)
  
  RAWDATNUM <- RAWDAT2[,-1] %>%
  	mapply(.,FUN = as.numeric) %>%
  	t(.)
  
  ##Consolidating genes with the same name
  ###create empty matrix of size equal  to tabRDATID
  tabRDATID <- table(RAWDATID)
  NuRDATN <- matrix(0, nrow = dim(RAWDATNUM)[1], ncol = length(tabRDATID))
  j <- 1
  for(j in 1:length(tabRDATID)){
  
  	##Putting the ones without duplicates in their new homes
  	if(tabRDATID[j] == 1){
  		NuRDATN[,j] <- RAWDATNUM[,which(RAWDATID==rownames(tabRDATID)[j])]

  	} else if(tabRDATID[j] > 1){

  	##Averaging duplicates and putting them in their new homes

  		NuRDATN[,j] <- rowMeans(RAWDATNUM[,which(RAWDATID==rownames(tabRDATID)[j])],na.rm = TRUE)
  	}
  	j <- j + 1
  }
  
  ##Scaling the Data
  scrawdat <- NuRDATN%>%
  	scale()
  attr(scrawdat,"scaled:center") <- NULL
  attr(scrawdat,"scaled:scale") <- NULL
  colnames(scrawdat) <- rownames(tabRDATID)

  #Outputting the Z-score file
  nfnzsc <- strsplit(alz,"[\\]") %>%
      .[[1]] %>%
      .[length(.)] %>%
      gsub("\\D","",.) %>%
      c("GSE",.,"zscore.txt") %>%
      paste(collapse = "")
  zscraw <- scrawdat %>%
      t()%>%
      as.data.frame(.,stringsAsFactors = FALSE)
  colnames(zscraw) <- subjnam
  write.table(zscraw, file = nfnzsc, sep = "\t",col.names = TRUE,row.names = TRUE)

  ##Discretized the Data
  dialzdat <- scrawdat %>%
  	dndat(.) %>%
  	t()%>%

  	as.data.frame(.,stringsAsFactors = FALSE)

  colnames(dialzdat) <- rownames(RAWDATNUM)
  
  ##setting "ID_REF" as a new variable

  geneNAM <- as.data.frame(as.matrix(rownames(dialzdat),ncol=1),stringsAsFactors = FALSE)

  colnames(geneNAM) <- "ID_REF"
  rownames(dialzdat) <- NULL
  dialzdat <-bind_cols(geneNAM,dialzdat)
  
  ##NAs in a column
  x <- 2
  nacol <- as.data.frame(t(rep(0,dim(dialzdat)[2])),stringsAsFactors = FALSE)
  nacol[1,1] = "COL_NAs"
  for(x in 2:dim(dialzdat)[2]){
  	nacol[1,x] <- as.integer(sum(is.na(dialzdat[,x])))
  	x <- x + 1
  }
  colnames(nacol) <- colnames(dialzdat)
  dialzdat<-bind_rows(dialzdat,nacol)
  
  ##NAs in a row
  y <- 1
  narowd <- as.data.frame(rep(0,dim(dialzdat)[1]),stringsAsFactors = FALSE)
  for(y in 1:dim(dialzdat)[1]){
  	narowd[y,1] <- as.integer(sum(is.na(dialzdat[y,])))
  	y <- y + 1
  }
  colnames(narowd) <- "ROW_NAs"
  dialzdat <- bind_cols(dialzdat,narowd)
  colnames(dialzdat)[2:(dim(dialzdat)[2]-1)] <- subjnam
  colnames(RAWWORD) <- colnames(dialzdat)
  ##converting to character so that the clinical can be brought together with discrete data
  k <- 2
  for(k in 2:dim(dialzdat)[2]-1){
  	dialzdat[,k] <- as.character(dialzdat[,k])
  	k <- k + 1
  }
  #The End the full data
  Dscrtalzdw <- bind_rows(RAWWORD,dialzdat)
  
  #Produces Discrete file

  nfnaex2 <- strsplit(alz,"[\\|/]") %>%

  	.[[1]] %>%
  	.[length(.)] %>%
  	gsub("\\D","",.) %>%
  	c("GSE",.,"dscrt.txt") %>%
  	paste(collapse = "")

  write.table(Dscrtalzdw, file = nfnaex2, sep = "\t",col.names = TRUE,row.names = FALSE)