R/makeSnps_function.R

In mglR: Master Gene List

#' Make SNP list
#'
#' \code{makeSnps} returns a list with four elements summarizing the SNPs associated with each gene via GWAS results, eQTL databases, etc.  The first element (snpRes) is a list with length corresponding to the number of candidate genes and includes all associated SNPs split by source.  The second element (snpList) collpases the first so only SNPs for each gene are listed, regardless of the source.  The third element (snpTable) is a dataframe with two columns: SNPs and gene names.  The fourth element (snpCount) is a table with the number of elements corresponding to the number of unique SNPs in the genelist - reported for each SNP is the number of times it appears.  It is sorted in descending order.  The structure is similar to the \code{\link{makeGo}} and \code{\link{makePhenotypes}} functions.
#'
#' This gives a summary of all SNPs associated with the gene.  Based on all elements in the list containing SNP information: eqtl (element 14), sqtlSeek (element 15), sqtlAltrans (element 16), pqtl (element 17), dnase (element 21), gwasCatalog (element 18), grasp (element 19).
#'
#' @family output
#'
#' @param mgl List; see \code{\link{buildFromNames}}, \code{\link{buildFromRegion}}, or \code{\link{buildFromEnsgs}}
#'
#' @param saveFile A logical flag indicating whether a csv file ('Snps.csv') should be saved in the current directory. 
#'
#' @examples
#' exMgl() -> myMgl
#' myMgl <- makeSnps(myMgl, saveFile = FALSE) -> mySnps
#'
#'@export
#'@importFrom utils write.table write.csv
 
makeSnps <- function(mgl, saveFile = FALSE){

# stop process if none of the SNP based elements have been filled in
tmp <- c(unique(unlist(lapply(mgl, function(x) class(x[[13]])))), unique(unlist(lapply(mgl, function(x) class(x[[14]])))), unique(unlist(lapply(mgl, function(x) class(x[[15]])))), unique(unlist(lapply(mgl, function(x) class(x[[16]])))), unique(unlist(lapply(mgl, function(x) class(x[[17]])))), unique(unlist(lapply(mgl, function(x) class(x[[18]])))), unique(unlist(lapply(mgl, function(x) class(x[[19]])))), unique(unlist(lapply(mgl, function(x) class(x[[12]])))))

if(length(unique(tmp)) == 1){
	if(unique(tmp) == 'integer')
	stop('SNP data not available. See function listElements to check which elements are present.  See functions addDnase, addTransEqtl, addCisEqtl, addGrasp, addgwasCatalog, addPtv, addSqtlAltrans, addSqtlSeek to add pertinent information.') 
}

### get only those elements that are filled in
dane <-as.list(1:length(mgl))
for(i in 1:length(mgl)){
	dane[[i]] <- as.list(1:8)
	names(dane[[i]]) <- c('TranseQTL', 'CiseQTL', 'sqtlSeek', 'sqtlAltrans', 'Ptv', 'GwasCatalog', 'GRASP', 'DNAse')
}
names(dane) <- names(mgl)

for(i in 1:length(mgl)){
if(tmp[1] != "integer"){
	dane[[i]][[1]] <- as.character(unique(do.call(rbind,mgl[[i]][[13]])[,2]))}
if(tmp[2] != "integer"){
	a1 <- listSeparate(mgl, 14)[[i]]
	a1 <- a1[which(unlist(lapply(a1, class)) == 'data.frame')]
	if(length(unique(unlist(lapply(a1, function(x) dim(x)[1])))) != 1 ){
		a1 <- a1[which(unlist(lapply(a1, function(x) dim(x)[1])) > 0)]
		a1 <- unique(do.call(rbind,a1))
		if(dim(a1)[2] == 12){
			dane[[i]][[2]] <- a1[,2]
			message("Warning: It maybe preferable to convert SNP Ids to rs identifiers where possible.  See fixSnpIds.")}
		if(dim(a1)[2] == 13){
			dane[[i]][[2]] <- a1[,13]}}
	if(length(unique(unlist(lapply(a1, function(x) dim(x)[1])))) == 1 ) {	
	dane[[i]][[2]] <- 'None'}}
if(tmp[3] != "integer"){
	dane[[i]][[3]] <- unique(do.call(rbind,mgl[[i]][[15]])[,1])}		
if(tmp[4] != "integer"){
	dane[[i]][[4]] <- unique(do.call(rbind,mgl[[i]][[16]])[,1])}
if(tmp[5] != "integer"){
	dane[[i]][[5]] <- unique(do.call(rbind,mgl[[i]][[17]])[,1])}
if(tmp[6] != "integer"){
	dane[[i]][[6]] <- unique(mgl[[i]][[18]][,22])}
if(tmp[7] != "integer"){
	dane[[i]][[7]] <- unique(mgl[[i]][[19]][,7])}
if(tmp[8] != "integer"){
	dane[[i]][[8]] <- unique(mgl[[i]][[12]][which(mgl[[i]][[12]][,13] == 'imbalanced_(5%_FDR)' | mgl[[i]][[12]][,13] == 'imbalanced_(0.1%_FDR)'),4])}
	}

# Add message about no SNPs found for those elements that are empty
for (i in 1:length(mgl)){
	for(x in which(unlist(lapply(dane[[i]], length)) == 0)){
		dane[[i]][[x]] <- 'None'
	}
}

# Add message about data missing from mgl for those elements that are not filled in	
for(x in 1:8){
	if(tmp[x] == 'integer'){
		for(i in 1:length(mgl)){
			dane[[i]][[x]] <- 'Data missing from mgl list'
		}
	}
}

### Collapse so just get SNPs and format
daneCollapsed <- as.list(1:length(mgl))
names(daneCollapsed) <- names(mgl)
for(i in 1:length(mgl)){
unique(unlist(dane[[i]])) -> x
if (sum(is.na(x)) != 0) {
	x <- x[-c(which(is.na(x) == T))]
}
x <- x[x != ""]
if (length(grep('None', x)) != 0){
	x <- x[-c(which(x == 'None'))]
}
if (length(grep('Data missing from mgl list', x)) != 0){
	x <- x[-c(which(x == 'Data missing from mgl list'))]
}
x <- sort(x)
daneCollapsed[[i]] <- x
}

### making table with first column as phenotype and second as gene
# Removing those genes that have no go information i.e. an empty elementy five
tab <- daneCollapsed
for (x in 1:length(tab)){
	if (length(tab[[x]]) > 0) {
		tab[[x]] <- cbind(tab[[x]], names(tab)[x])
		}
		}
# Making it into a datable instead of a list 
do.call(rbind, tab) -> snpTable
# Adding names
colnames(snpTable) <- c("SNP", "Gene")
rownames(snpTable) <- NULL

### Tabling the number of times a snp appears
table(snpTable[,1]) -> snpCount
snpCount <- sort(snpCount, decreasing = T)

if(saveFile == TRUE){
write.table("SNPs", 'Snps.csv', sep = ',', col.names = FALSE, row.names = FALSE)
for(i in 1:length(dane)){
	write.table(names(mgl)[i], "Snps.csv", sep = ",", append = TRUE, col.names = FALSE, row.names = FALSE)
	for (j in 1:length(dane[[i]])){
		write.table(names(dane[[i]])[j], "Snps.csv", sep = ",", append = TRUE, col.names = FALSE, row.names = FALSE)
		write.table(dane[[i]][[j]], "Snps.csv", sep = ",", append = TRUE, col.names = FALSE, row.names = FALSE)
			write.table("", "Snps.csv", sep = ",", append = TRUE, col.names = FALSE, row.names = FALSE)
	}
	write.table("", "Snps.csv", sep = ",", append = TRUE, col.names = FALSE, row.names = FALSE)}
write.csv(snpTable, 'snpTable.csv')
write.csv(snpCount, 'snpCount.csv')
}

snp <- list(snpRes = dane, snpList = daneCollapsed, snpTable = snpTable, snpCount = snpCount)

#message('\nPlease cite datasources as appropriate. \n \n Element 12 - DNAse: \n Maurano, Matthew T, Eric Haugen, Richard Sandstrom, Jeff Vierstra, Anthony Shafer, Rajinder Kaul, and John A Stamatoyannopoulos. 2015. “Large-Scale Identification of Sequence Variants Influencing Human Transcription Factor Occupancy in Vivo.” Nature Genetics 47 (12): 1393–1401. doi:10.1038/ng.3432.\n \n Elements 13-17 GTEx: \n The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund  of the Office of the Director of the National Institutes of Health. Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCISAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171), and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute, Inc. Biorepository operations were funded through an SAIC-F subcontract to Van Andel Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by a supplements to University of Miami grants DA006227 & DA033684 and to contract N01MH000028. Statistical Methods development grants were made to the University of Geneva (MH090941 & MH101814), the University of Chicago (MH090951, MH090937, MH101820, MH101825), the University of North Carolina - Chapel Hill (MH090936 & MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University St Louis (MH101810), and the University of Pennsylvania (MH101822). The data used for the analyses described in this manuscript were obtained from: [insert, where appropriate] the GTEx Portal on MM/DD/YY and/or dbGaP  accession number phs000424.vN.pN  on MM/DD/YYYY.\n \n Element 15 - sqtlSeek: \n (1)Monlong, J. et al. Identification of genetic variants associated with alternative splicing using sQTLseekeR. Nat. Commun. 5:4698 doi: 10.1038/ncomms5698 (2014) \n (2) "Multi-tissue analysis of gene regulation in a human population sample: the Genotype-Tissue Expression (GTEx) pilot study", The GTEx Consortium, under review at Science, Dec. 2014. \n \n  Element 16 - sqtlAltrans: \n (1) "Multi-tissue analysisof gene regulation in a human population sample: the Genotype-Tissue Expression (GTEx) pilot study", The GTEx Consortium, under review at Science, Dec. 2014.\n (2) Ongen, H., & Dermitzakis, E. T. (2015). Alternative Splicing QTLs in European and African Populations. American Journal of Human Genetics, 97(4), 567–575. http://doi.org/10.1016/j.ajhg.2015.09.004 \n \n Element 17 - pqtl: \n Rivas MA, Pirinen M, Conrad DF, et al. Impact of predicted protein-truncating genetic variants on the human transcriptome. Science (New York, NY). 2015;348(6235):666-669. doi:10.1126/science.1261877. \n \n Element 18 - NHGRI-EBI GWAS Catalog: \n Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, Klemm A, Flicek P, Manolio T, Hindorff L, and Parkinson H.The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Research, 2014, Vol. 42 (Database issue): D1001-D1006 \n \n Element 19 - GRASP: \n (1) Leslie R, O’Donnell CJ, Johnson AD (2014) GRASP: analysis of genotype-phenotype results from 1,390 genome-wide association studies and corresponding open access database. Bioinformatics 30(12), i185-94. GRASP Build 2.0.0.0\n (2) Carey V (2016). grasp2db: grasp2db, sqlite wrap of GRASP 2.0. R package version 0.1.14.\n')
	
return(snp)

}