diff options
| -rw-r--r-- | wqflask/wqflask/auwerx/phewas_analysis.py | 52 |
1 files changed, 38 insertions, 14 deletions
diff --git a/wqflask/wqflask/auwerx/phewas_analysis.py b/wqflask/wqflask/auwerx/phewas_analysis.py index c69326ed..061d2bc8 100644 --- a/wqflask/wqflask/auwerx/phewas_analysis.py +++ b/wqflask/wqflask/auwerx/phewas_analysis.py @@ -23,32 +23,56 @@ from utility.tools import locate r_library = ro.r["library"] # Map the library function r_options = ro.r["options"] # Map the options function +r_load = ro.r["load"] # Map the load function +r_write_table = ro.r["write.table"] # Map the write.table function +r_head = ro.r["head"] # Map the head function +r_colnames = ro.r["colnames"] # Map the colnames function +r_list = ro.r["list"] # Map the list function +r_c = ro.r["c"] # Map the c (combine) function +r_rep = ro.r["rep"] # Map the rep (repeat) function class PheWAS(object): def __init__(self): print("Initialization of PheWAS") - r_library("auwerx") # Load the auwerx package - Should only be done once, since it is quite expensive - r_options(stringsAsFactors = False) - # Create the aligners - r_download_BXD_geno = ro.r["download.BXD.geno"] # Map the create.Pheno_aligner function - r_create_Pheno_aligner = ro.r["create.Pheno_aligner"] # Map the create.Pheno_aligner function - r_create_SNP_aligner = ro.r["create.SNP_aligner"] # Map the create.SNP_aligner function - r_calculate_all_pvalue_parallel = ro.r["calculate.all.pvalue.parallel"] # Map the calculate.all.pvalue.parallel function - r_PheWASManhattan = ro.r["PheWASManhattan"] # Map the PheWASManhattan function + # TODO: Loading the package should only be done once, since it is quite expensive + print(r_library("auwerx")) # Load the auwerx package + self.r_download_BXD_geno = ro.r["download.BXD.geno"] # Map the create.Pheno_aligner function + self.r_create_Pheno_aligner = ro.r["create.Pheno_aligner"] # Map the create.Pheno_aligner function + self.r_create_SNP_aligner = ro.r["create.SNP_aligner"] # Map the create.SNP_aligner function + self.r_calculate_all_pvalue_parallel = ro.r["calculate.all.pvalue.parallel"] # Map the calculate.all.pvalue.parallel function + self.r_PheWASManhattan = ro.r["PheWASManhattan"] # Map the PheWASManhattan function print("Initialization of PheWAS done !") def run_analysis(self, requestform): print("Starting PheWAS analysis on dataset") - bxdgeno = r_download_BXD_geno() - snpaligner = r_create_SNP_aligner(bxdgeno) - phenoaligner = r_create_Pheno_aligner() - allpvalues = r_calculate_all_pvalue_parallel() # This needs some magic to work I think - # trait chromosome and trait positions should come from the user input - r_PheWASManhattan(None, allpvalues, phenoaligner, snpaligner, None, trait_chr, trait_pos, trait_pos ) + genofilelocation = locate("BXD.geno", "genotype") # Get the location of the BXD genotypes + precompfilelocation = locate("PheWAS_pval_EMMA_norm.RData", "auwerx") # Get the location of the pre-computed EMMA results + + parser = genofile_parser.ConvertGenoFile(genofilelocation) + parser.process_csv() + snpinfo = [] + for marker in parser.markers: + snpinfo.append(marker["name"]); + snpinfo.append(marker["chr"]); + snpinfo.append(marker["Mb"]); + + rnames = r_rep(1, len(parser.markers)) + # Create the snp aligner object out of the BXD genotypes + snpaligner = ro.r.matrix(snpinfo, nrow=len(parser.markers), dimnames = r_list(rnames, r_c("SNP", "Chr", "Pos")), ncol = 3, byrow=True) + # Create the phenotype aligner object using R + phenoaligner = self.r_create_Pheno_aligner() + + r_load(precompfilelocation) # Load the pre-computed EMMA results into R + allpvalues = ro.r['pval_all'] # Get a pointer to the pre-computed results + + # Create the PheWAS plot (The gene/probe name, chromosome and gene/probe positions should come from the user input) + # TODO: generate the PDF in the temp folder, with a unique name + self.r_PheWASManhattan("1:25", allpvalues, phenoaligner, snpaligner, "1:25", 1, 25, 25 ) print("Initialization of PheWAS done !") def process_results(self, results): print("Processing PheWAS output") + # TODO: get the PDF in the temp folder, and display it to the user template_vars = {} return(dict(template_vars)) |