This code combines the cleaning and discretizing processes. (UNTESTED)

Two files are output one with raw data and the other with discretized data.

This code combines the cleaning and discretizing processes. (UNTESTED)
Two files are output one with raw data and the other with discretized data.
Efrain Gonzalez
1 parent 2167ed7633
Showing 1 changed file with 445 additions and 0 deletions Show diff stats
RCleanDscret.R
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+#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"	
+		} 
+		if("!Sample_title" == mat[1,j]){
+			colnames(mat)[j] <- "Title"
+		} 
+		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",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*$",GIDNAM[i,2])==TRUE||is.na(GIDNAM[i,2])==TRUE){
+			GIDNAM[i,2] <- GIDNAM[i,1]
+		}
+		i <- i + 1
+	}
+	GIDNAM
+}	
+
+#4#Function for changing the gene ID to gene name
+cgeneID <- function(GeneName,DATA){
+    colGene <- dim(GeneName)[2]
+     j <- 1
+     for(j in 1:colGene){
+	chngsreq <- grep(paste0("^",GeneName[1,j],"$"),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
+				}
+				if(NDATA[i,j] > 1){
+					DDATA[i,j]=2L
+				}
+				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()%>%
+	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 = "!")
+
+} 
+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*$",colnames(.)))
+			}
+			if(IDF == 0){
+				#No information on this particular GPL file
+				idLOCGPL <- genena %>%
+					read_delim(delim="\t",col_names = FALSE, comment = "!", n_max = 1000) %>%
+					t(.) %>%
+					grep("^ID\\s*$",.) %>%
+					-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*$|^gene_assignment\\s*$",colnames(.)))
+			}
+		}
+		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*$",colnames(.)))
+		}
+	 }
+	if(soft == FALSE){
+		geneIDNam <- genena %>%
+		read_delim(delim="\t",comment = "#")%>%
+		dplyr::select(.,ID,grep("Symbol|^ORF\\s*$|^gene_assignment\\s*$",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(t(geneIDNam),t(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() %>%
+	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",rawword[,1])
+
+##Subject Names GSM...
+subjnam <- rawword[hereim,]
+
+##Getting the names for the rows
+namedarows <- rownames(rawword)[-hereim] %>%
+	as.data.frame()
+RAWWORD <- rawword[-hereim,] %>%
+	as.data.frame() %>%
+	bind_cols(namedarows,.)
+z <- 1
+naroww <- as.data.frame(rep(0,dim(RAWWORD)[1]),stringsAsFactors = FALSE)
+for(z in 1:dim(RAWWORD)[1]){
+	naroww[z,1] <- as.integer(sum(is.na(RAWWORD[z,])))
+	z <- z + 1
+}
+
+colnames(naroww) <- "ROW_NAs"
+RAWWORD <- bind_cols(RAWWORD,naroww)
+
+
+roALZna <- t(ALZDAT) %>%
+	rownames(.) %>%
+	as.data.frame(.)
+colnames(roALZna) <- "ID_REF"
+
+RAWDAT <- t(ALZDAT) %>%
+	as.data.frame(.)
+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])]
+	}
+	##Averaging duplicates and putting them in their new homes
+	if(tabRDATID[j] > 1){
+		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)
+
+##Discretized the Data
+dialzdat <- scrawdat %>%
+	dndat(.) %>%
+	t()%>%
+	as.data.frame(.)
+colnames(dialzdat) <- rownames(RAWDATNUM)
+
+##setting "ID_REF" as a new variable
+geneNAM <- as.data.frame(as.matrix(rownames(dialzdat),ncol=1))
+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
+nfnaex <- strsplit(rawdat,"[\\|/]") %>%
+	.[[1]] %>%
+	.[length(.)] %>%
+	gsub("\\D","",.) %>%
+	c("GSE",.,"dscrt.txt") %>%
+	paste(collapse = "")
+write.table(Dscrtalzdw, file = nfnaex, sep = "\t",col.names = TRUE,row.names = FALSE)
+
...	...	@@ -0,0 +1,445 @@
	1	+#Libraries required to run the code
	2	+library(pryr)
	3	+library(MASS)
	4	+library(dplyr)
	5	+library(tidyr)
	6	+library(readr)
	7	+library(stringr)
	8	+
	9	+
	10	+#Necessary Functions
	11	+#1#Function for handling the changing of row names and column names
	12	+chngrownm <- function(mat){
	13	+ row <- dim(mat)[1]
	14	+ col <- dim(mat)[2]
	15	+ j <- 1
	16	+ x <- 1
	17	+ p <- 1
	18	+ a <- 1
	19	+ b <- 1
	20	+ g <- 1
	21	+ for(j in 1:col){
	22	+ if("!Sample_source_name_ch1"==mat[1,j]){
	23	+ colnames(mat)[j] <- "Brain_Region"
	24	+ }
	25	+ if("!Sample_title" == mat[1,j]){
	26	+ colnames(mat)[j] <- "Title"
	27	+ }
	28	+ if("!Sample_geo_accession" == mat[1,j]){
	29	+ colnames(mat)[j] <- "ID_REF"
	30	+ } else{
	31	+ if(grepl("Sex\|gender\|Gender\|sex",mat[2,j])==TRUE){
	32	+ colnames(mat)[j] <- paste0("Sex",x)
	33	+ x = x + 1
	34	+ }
	35	+ if(grepl("postmorteminterval\|PMI\|pmi",mat[2,j])==TRUE){
	36	+ colnames(mat)[j] <- paste0("PMI",p)
	37	+ p = p + 1
	38	+ }
	39	+ if(grepl("age\|Age\|AGE",mat[2,j])==TRUE){
	40	+ colnames(mat)[j] <- paste0("Age",a)
	41	+ a = a + 1
	42	+ }
	43	+ if(grepl("braak\|b&b",mat[2,j])==TRUE){
	44	+ colnames(mat)[j] <- paste0("Braak",b)
	45	+ b = b + 1
	46	+ }
	47	+ if(grepl("group\|disease\|control\|AD\|normal\|diagnosis\|Alzheimer\|Control",mat[2,j])==TRUE){
	48	+ colnames(mat)[j] <- paste0("Group",g)
	49	+ g = g + 1
	50	+ }
	51	+
	52	+ }
	53	+ j = j + 1
	54	+ }
	55	+ mat
	56	+}
	57	+
	58	+#2#Function for reorganizing information within the columns
	59	+cinfo <- function(mat){
	60	+ col <- dim(mat)[2]
	61	+ j <-2
	62	+ for(j in 2:col){
	63	+ if(grepl("Group",colnames(mat)[j]) == TRUE){
	64	+ mat[,j] <- gsub(".+:\\s\|\\s.+;.+","",mat[,j])
	65	+ }
	66	+ if(grepl("Age",colnames(mat)[j])==TRUE){
	67	+ mat[,j] <- gsub("\\D","",mat[,j])%>%
	68	+ as.integer()
	69	+ }
	70	+ if(grepl("Sex",colnames(mat)[j])==TRUE){
	71	+ mat[,j] <- gsub(".+:\\s","",mat[,j])
	72	+ }
	73	+ if(grepl("PMI",colnames(mat)[j])==TRUE){
	74	+ mat[,j] <- gsub("[^0-9\\.]","",mat[,j])%>%
	75	+ as.numeric()
	76	+ }
	77	+ if(grepl("Braak",colnames(mat)[j])==TRUE){
	78	+ mat[,j]<-gsub(".+:\\s","",mat[,j])%>%
	79	+ as.roman()%>%
	80	+ as.integer()
	81	+ }
	82	+ j=j+1
	83	+ }
	84	+ mat
	85	+}
	86	+
	87	+#3#Function for labeling the gene IDs without names
	88	+NAFIXING <- function(GIDNAM){
	89	+ row <- dim(GIDNAM)[1]
	90	+ i <- 1
	91	+ for(i in 1:row){
	92	+ if(grepl("^NA\\s*$",GIDNAM[i,2])==TRUE\|\|is.na(GIDNAM[i,2])==TRUE){
	93	+ GIDNAM[i,2] <- GIDNAM[i,1]
	94	+ }
	95	+ i <- i + 1
	96	+ }
	97	+ GIDNAM
	98	+}
	99	+
	100	+#4#Function for changing the gene ID to gene name
	101	+cgeneID <- function(GeneName,DATA){
	102	+ colGene <- dim(GeneName)[2]
	103	+ j <- 1
	104	+ for(j in 1:colGene){
	105	+ chngsreq <- grep(paste0("^",GeneName[1,j],"$"),DATA[1,])
	106	+ if(is.na(sum(chngsreq))==FALSE){
	107	+ if(sum(chngsreq) > 0){
	108	+ DATA[1,chngsreq] <- gsub(paste0("^",GeneName[1,j]),GeneName[2,j],DATA[1,chngsreq])
	109	+ }
	110	+ }
	111	+ #if(sum(chngsreq) > 0){
	112	+ ##DATA[1,chngsreq] <- gsub(GeneName[1,j],GeneName[2,j],DATA[1,chngsreq])
	113	+ #DATA[1,chngsreq] <- gsub(paste0("^",GeneName[1,j]),GeneName[2,j],DATA[1,chngsreq])
	114	+ #}
	115	+ j = j+1
	116	+ }
	117	+ DATA
	118	+}
	119	+
	120	+#5#Function for adjusting the gene names
	121	+gcnames <- function(DiData,usecol=1){
	122	+ nuruns <- dim(DiData)[2]
	123	+ i = 1
	124	+ nwnam <- rep("0",length.out=nuruns)
	125	+ for(i in 1:nuruns){
	126	+ if(length(strsplit(colnames(DiData)[i],"///")[[1]]) >= usecol){
	127	+ nwnam[i]=str_trim(strsplit(colnames(DiData)[i],"///")[[1]][usecol])
	128	+ } else{
	129	+ nwnam[i]=str_trim(strsplit(colnames(DiData)[i],"///")[[1]][1])
	130	+ }
	131	+
	132	+ }
	133	+ nwnam
	134	+
	135	+}
	136	+
	137	+#6# Function for discretizing the data
	138	+dndat <- function(NDATA){
	139	+ rownd <- dim(NDATA)[1]
	140	+ colnd <- dim(NDATA)[2]
	141	+ DDATA <- matrix(0,nrow=rownd,ncol=colnd)
	142	+ colnames(DDATA) <- colnames(NDATA)
	143	+ i <- 1
	144	+ for(i in 1:rownd){
	145	+ j <- 1
	146	+ for(j in 1:colnd){
	147	+ if(is.na(NDATA[i,j])==FALSE){
	148	+
	149	+ if(NDATA[i,j] < -1){
	150	+ DDATA[i,j]=0L
	151	+ }
	152	+ if(NDATA[i,j] > 1){
	153	+ DDATA[i,j]=2L
	154	+ }
	155	+ if(-1 <= NDATA[i,j] && NDATA[i,j] < 1){
	156	+ DDATA[i,j]=1L
	157	+ }
	158	+ } else{
	159	+ DDATA[i,j] = NDATA[i,j]
	160	+ }
	161	+ j = j + 1
	162	+ }
	163	+ i = i + 1
	164	+ }
	165	+ DDATA
	166	+}
	167	+
	168	+
	169	+#The Rest of this code will be used every time you want to change a data set
	170	+
	171	+#Getting the series matrix file
	172	+print("Choose the series matrix file that you want to Analyze")
	173	+alz <- file.choose()
	174	+
	175	+#Getting the GPL file
	176	+print("Choose the GPL file that correlates with the above series matrix file")
	177	+genena <- file.choose()
	178	+
	179	+
	180	+#Find out if it is a soft GPL file or not
	181	+soft <- strsplit(genena,"[\\\|/]") %>%
	182	+ .[[1]] %>%
	183	+ .[length(.)] %>%
	184	+ grepl("soft\|annot",.)
	185	+
	186	+#Working with the wordy part of the document
	187	+alzword <- alz %>%
	188	+ read_delim(delim ="\t",comment = "!Series",col_names = FALSE)%>%
	189	+ filter(grepl("!Sample",X1))%>%
	190	+ filter(!grepl("!Sample_contact",X1))
	191	+
	192	+##Changing row names and column names:
	193	+ALZWORD <- t(alzword)
	194	+rownames(ALZWORD)=NULL
	195	+colnames(ALZWORD) <- colnames(ALZWORD,do.NULL=FALSE)
	196	+ALZWORD <- chngrownm(ALZWORD)[-1,]
	197	+ALZWORD <- ALZWORD%>%
	198	+ as.data.frame()%>%
	199	+ dplyr::select(-starts_with("col"))
	200	+
	201	+##Reorganizing information within the columns
	202	+ALZWORDF <- cinfo(ALZWORD)
	203	+
	204	+
	205	+#Working with Actual Data part of file
	206	+alzdat <- alz %>%
	207	+ read_delim(delim="\t",col_names=TRUE,comment = "!",skip=1)
	208	+ALZDAT <- t(alzdat[,-1])
	209	+rownames(ALZDAT)=NULL
	210	+
	211	+##Is there a clean version of the GPL file available?
	212	+gplnum <- strsplit(genena,"[\\\|/]") %>%
	213	+ .[[1]] %>%
	214	+ .[length(.)] %>%
	215	+ gsub("\\D","",.)
	216	+clfileex <- sum(grepl(paste0("Clean_GPL",gplnum),list.files()))
	217	+if(clfileex >= 1){
	218	+#use the clean version
	219	+geneIDNam <- paste0("Clean_GPL",gplnum,".txt") %>%
	220	+ read_delim(delim="\t",col_names = c("ID","Symbol"), comment = "!")
	221	+
	222	+}
	223	+if(clfileex == 0){
	224	+##Lets Create a clean version
	225	+
	226	+##Gene ID to Gene Name
	227	+ if(soft == TRUE){
	228	+ #Check to see if there is already a file containing information on soft files
	229	+ fileex <- sum(grepl("GPL_ID_LOC.txt",list.files()))
	230	+ if(fileex == 1){
	231	+ #Check to see if this GPL soft file has been used before
	232	+ IDF <- read_delim("GPL_ID_LOC.txt",delim = "\t",col_names = TRUE) %>%
	233	+ .$GPL_FILE_NUM%>%
	234	+ grepl(gplnum,.) %>%
	235	+ sum()
	236	+ if(IDF == 1){
	237	+ IDLOCAL <- read_delim("GPL_ID_LOC.txt",delim = "\t",col_names = TRUE) %>%
	238	+ .$GPL_FILE_NUM%>%
	239	+ grep(gplnum,.)
	240	+ idlocgpl <- read_delim("GPL_ID_LOC.txt",delim = "\t",col_names = TRUE) %>%
	241	+ .$LOC_ID %>%
	242	+ .[IDLOCAL]
	243	+ geneIDNam <- genena %>%
	244	+ read_delim(delim="\t",col_names = TRUE, comment = "!", skip = idlocgpl) %>%
	245	+ dplyr::select(.,ID,grep("Symbol\|^ORF\\s$\|^gene_assignment\\s$",colnames(.)))
	246	+ }
	247	+ if(IDF == 0){
	248	+ #No information on this particular GPL file
	249	+ idLOCGPL <- genena %>%
	250	+ read_delim(delim="\t",col_names = FALSE, comment = "!", n_max = 1000) %>%
	251	+ t(.) %>%
	252	+ grep("^ID\\s*$",.) %>%
	253	+ -1
	254	+ cbind(as.integer(gplnum),as.integer(idLOCGPL)) %>%
	255	+ cat(file="GPL_ID_LOC.txt",sep = "\t", fill = TRUE, append = TRUE)
	256	+ geneIDNam <- genena %>%
	257	+ read_delim(delim="\t",col_names = TRUE, comment = "!", skip = idLOCGPL) %>%
	258	+ dplyr::select(.,ID,grep("Symbol\|^ORF\\s$\|^gene_assignment\\s$",colnames(.)))
	259	+ }
	260	+ }
	261	+ if(fileex == 0){
	262	+ #We must create a file that we can access for later use
	263	+ idLOCGPL <- genena %>%
	264	+ read_delim(delim="\t",col_names = FALSE, comment = "!", n_max = 1000) %>%
	265	+ t(.) %>%
	266	+ grep("^ID\\s*$",.) %>%
	267	+ -1
	268	+ Firstval <- cbind(as.integer(gplnum),as.integer(idLOCGPL))
	269	+ colnames(Firstval) <- c("GPL_FILE_NUM","LOC_ID")
	270	+ write.table(Firstval,file = "GPL_ID_LOC.txt", sep = "\t",row.names = FALSE, col.names = TRUE)
	271	+ geneIDNam <- genena %>%
	272	+ read_delim(delim="\t",col_names = TRUE, comment = "!", skip = idLOCGPL) %>%
	273	+ dplyr::select(.,ID,grep("Symbol\|^ORF\\s$\|^gene_assignment\\s$",colnames(.)))
	274	+ }
	275	+ }
	276	+ if(soft == FALSE){
	277	+ geneIDNam <- genena %>%
	278	+ read_delim(delim="\t",comment = "#")%>%
	279	+ dplyr::select(.,ID,grep("Symbol\|^ORF\\s$\|^gene_assignment\\s$",colnames(.)))
	280	+ }
	281	+
	282	+ ##Labeling the gene IDs without names
	283	+ geneIDNam <- NAFIXING(geneIDNam)
	284	+
	285	+ ##remove the whitespace
	286	+ geneIDNam <- t(rbind(str_trim(t(geneIDNam)[1,]),str_trim(t(geneIDNam)[2,])))
	287	+
	288	+ ##Here is the clean version
	289	+ write.table(geneIDNam,file = paste0("Clean_GPL",gplnum,".txt"),sep = "\t",row.names = FALSE, col.names = FALSE)
	290	+}
	291	+
	292	+
	293	+
	294	+##Changing the gene ID to gene name
	295	+ALZDAT1 <- cgeneID(t(geneIDNam),t(alzdat))
	296	+colnames(ALZDAT) = ALZDAT1[1,]
	297	+
	298	+
	299	+##Adjusting the column names aka the gene names
	300	+colnames(ALZDAT) <- gcnames(ALZDAT)
	301	+
	302	+
	303	+#Full RAW Data
	304	+Fullalzdwr <- ALZDAT %>%
	305	+ as.data.frame() %>%
	306	+ cbind(ALZWORDF,.)
	307	+
	308	+
	309	+#Raw file is output
	310	+nfnaex <- strsplit(alz,"[\\]") %>%
	311	+ .[[1]] %>%
	312	+ .[length(.)] %>%
	313	+ gsub("\\D","",.) %>%
	314	+ c("GSE",.,"aftexcel.txt") %>%
	315	+ paste(collapse = "")
	316	+write.table(t(Fullalzdwr), file = nfnaex, sep = "\t")
	317	+
	318	+
	319	+#Now for the discretization part
	320	+##get the wordy part again
	321	+rawword <- t(ALZWORDF)
	322	+
	323	+##where is ID_REF located
	324	+hereim <- grep("ID_REF",rawword[,1])
	325	+
	326	+##Subject Names GSM...
	327	+subjnam <- rawword[hereim,]
	328	+
	329	+##Getting the names for the rows
	330	+namedarows <- rownames(rawword)[-hereim] %>%
	331	+ as.data.frame()
	332	+RAWWORD <- rawword[-hereim,] %>%
	333	+ as.data.frame() %>%
	334	+ bind_cols(namedarows,.)
	335	+z <- 1
	336	+naroww <- as.data.frame(rep(0,dim(RAWWORD)[1]),stringsAsFactors = FALSE)
	337	+for(z in 1:dim(RAWWORD)[1]){
	338	+ naroww[z,1] <- as.integer(sum(is.na(RAWWORD[z,])))
	339	+ z <- z + 1
	340	+}
	341	+
	342	+colnames(naroww) <- "ROW_NAs"
	343	+RAWWORD <- bind_cols(RAWWORD,naroww)
	344	+
	345	+
	346	+roALZna <- t(ALZDAT) %>%
	347	+ rownames(.) %>%
	348	+ as.data.frame(.)
	349	+colnames(roALZna) <- "ID_REF"
	350	+
	351	+RAWDAT <- t(ALZDAT) %>%
	352	+ as.data.frame(.)
	353	+colnames(RAWDAT) <- NULL
	354	+rownames(RAWDAT) <- NULL
	355	+
	356	+RAWDAT2 <- RAWDAT %>%
	357	+ cbind(roALZna,.) %>%
	358	+ dplyr::arrange(.,ID_REF)
	359	+
	360	+##Editing the file for R processing
	361	+RAWDATID <- RAWDAT2[,1] %>%
	362	+ as.matrix(.)
	363	+
	364	+RAWDATNUM <- RAWDAT2[,-1] %>%
	365	+ mapply(.,FUN = as.numeric) %>%
	366	+ t(.)
	367	+
	368	+##Consolidating genes with the same name
	369	+###create empty matrix of size equal to tabRDATID
	370	+tabRDATID <- table(RAWDATID)
	371	+NuRDATN <- matrix(0, nrow = dim(RAWDATNUM)[1], ncol = length(tabRDATID))
	372	+j <- 1
	373	+for(j in 1:length(tabRDATID)){
	374	+
	375	+ ##Putting the ones without duplicates in their new homes
	376	+ if(tabRDATID[j] == 1){
	377	+ NuRDATN[,j] <- RAWDATNUM[,which(RAWDATID==rownames(tabRDATID)[j])]
	378	+ }
	379	+ ##Averaging duplicates and putting them in their new homes
	380	+ if(tabRDATID[j] > 1){
	381	+ NuRDATN[,j] <- rowMeans(RAWDATNUM[,which(RAWDATID==rownames(tabRDATID)[j])],na.rm = TRUE)
	382	+ }
	383	+ j <- j + 1
	384	+}
	385	+
	386	+##Scaling the Data
	387	+scrawdat <- NuRDATN%>%
	388	+ scale()
	389	+attr(scrawdat,"scaled:center") <- NULL
	390	+attr(scrawdat,"scaled:scale") <- NULL
	391	+colnames(scrawdat) <- rownames(tabRDATID)
	392	+
	393	+##Discretized the Data
	394	+dialzdat <- scrawdat %>%
	395	+ dndat(.) %>%
	396	+ t()%>%
	397	+ as.data.frame(.)
	398	+colnames(dialzdat) <- rownames(RAWDATNUM)
	399	+
	400	+##setting "ID_REF" as a new variable
	401	+geneNAM <- as.data.frame(as.matrix(rownames(dialzdat),ncol=1))
	402	+colnames(geneNAM) <- "ID_REF"
	403	+rownames(dialzdat) <- NULL
	404	+dialzdat <-bind_cols(geneNAM,dialzdat)
	405	+
	406	+##NAs in a column
	407	+x <- 2
	408	+nacol <- as.data.frame(t(rep(0,dim(dialzdat)[2])),stringsAsFactors = FALSE)
	409	+nacol[1,1] = "COL_NAs"
	410	+for(x in 2:dim(dialzdat)[2]){
	411	+ nacol[1,x] <- as.integer(sum(is.na(dialzdat[,x])))
	412	+ x <- x + 1
	413	+}
	414	+colnames(nacol) <- colnames(dialzdat)
	415	+dialzdat<-bind_rows(dialzdat,nacol)
	416	+
	417	+##NAs in a row
	418	+y <- 1
	419	+narowd <- as.data.frame(rep(0,dim(dialzdat)[1]),stringsAsFactors = FALSE)
	420	+for(y in 1:dim(dialzdat)[1]){
	421	+ narowd[y,1] <- as.integer(sum(is.na(dialzdat[y,])))
	422	+ y <- y + 1
	423	+}
	424	+colnames(narowd) <- "ROW_NAs"
	425	+dialzdat <- bind_cols(dialzdat,narowd)
	426	+colnames(dialzdat)[2:(dim(dialzdat)[2]-1)] <- subjnam
	427	+colnames(RAWWORD) <- colnames(dialzdat)
	428	+##converting to character so that the clinical can be brought together with discrete data
	429	+k <- 2
	430	+for(k in 2:dim(dialzdat)[2]-1){
	431	+ dialzdat[,k] <- as.character(dialzdat[,k])
	432	+ k <- k + 1
	433	+}
	434	+#The End the full data
	435	+Dscrtalzdw <- bind_rows(RAWWORD,dialzdat)
	436	+
	437	+#Produces Discrete file
	438	+nfnaex <- strsplit(rawdat,"[\\\|/]") %>%
	439	+ .[[1]] %>%
	440	+ .[length(.)] %>%
	441	+ gsub("\\D","",.) %>%
	442	+ c("GSE",.,"dscrt.txt") %>%
	443	+ paste(collapse = "")
	444	+write.table(Dscrtalzdw, file = nfnaex, sep = "\t",col.names = TRUE,row.names = FALSE)
	445	+