RCleanDscret.R
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##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)