def run_analysis(self, requestform):
print("Starting ePheWAS analysis on dataset")
genofilelocation = locate(
"BXD.geno", "genotype") # Get the location of the BXD genotypes
tissuealignerloc = locate(
"Tissue_color_aligner.csv",
"auwerx") # Get the location of the Tissue_color_aligner
# Get user parameters, trait_id and dataset, and store/update them in self
self.trait_id = requestform["trait_id"]
self.datasetname = requestform["dataset"]
self.dataset = data_set.create_dataset(self.datasetname)
# Print some debug
print "self.trait_id:" + self.trait_id + "\n"
print "self.datasetname:" + self.datasetname + "\n"
print "self.dataset.type:" + self.dataset.type + "\n"
# Load in the genotypes file *sigh* to make the markermap
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_seq(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()
print("Initialization of ePheWAS done !")
def run_analysis(self, requestform):
print("Starting CTL analysis on dataset")
self.trait_db_list = [trait.strip() for trait in requestform['trait_list'].split(',')]
self.trait_db_list = [x for x in self.trait_db_list if x]
print("strategy:", requestform.get("strategy"))
strategy = requestform.get("strategy")
print("nperm:", requestform.get("nperm"))
nperm = int(requestform.get("nperm"))
print("parametric:", requestform.get("parametric"))
parametric = bool(requestform.get("parametric"))
print("significance:", requestform.get("significance"))
significance = float(requestform.get("significance"))
# Get the name of the .geno file belonging to the first phenotype
datasetname = self.trait_db_list[0].split(":")[1]
dataset = data_set.create_dataset(datasetname)
genofilelocation = locate(dataset.group.name + ".geno", "genotype")
parser = genofile_parser.ConvertGenoFile(genofilelocation)
parser.process_csv()
print(dataset.group)
# Create a genotype matrix
individuals = parser.individuals
markers = []
markernames = []
for marker in parser.markers:
markernames.append(marker["name"])
markers.append(marker["genotypes"])
genotypes = list(itertools.chain(*markers))
print(len(genotypes) / len(individuals), "==", len(parser.markers))
rGeno = r_t(ro.r.matrix(r_unlist(genotypes), nrow=len(markernames), ncol=len(individuals), dimnames = r_list(markernames, individuals), byrow=True))
# Create a phenotype matrix
traits = []
for trait in self.trait_db_list:
print("retrieving data for", trait)
if trait != "":
ts = trait.split(':')
gt = TRAIT.GeneralTrait(name = ts[0], dataset_name = ts[1])
gt = TRAIT.retrieve_sample_data(gt, dataset, individuals)
for ind in individuals:
if ind in gt.data.keys():
traits.append(gt.data[ind].value)
else:
traits.append("-999")
rPheno = r_t(ro.r.matrix(r_as_numeric(r_unlist(traits)), nrow=len(self.trait_db_list), ncol=len(individuals), dimnames = r_list(self.trait_db_list, individuals), byrow=True))
print(rPheno)
# Use a data frame to store the objects
rPheno = r_data_frame(rPheno, check_names = False)
rGeno = r_data_frame(rGeno, check_names = False)
# Debug: Print the genotype and phenotype files to disk
#r_write_table(rGeno, "~/outputGN/geno.csv")
#r_write_table(rPheno, "~/outputGN/pheno.csv")
# Perform the CTL scan
res = self.r_CTLscan(rGeno, rPheno, strategy = strategy, nperm = nperm, parametric = parametric, ncores = 6)
# Get significant interactions
significant = self.r_CTLsignificant(res, significance = significance)
# Create an image for output
self.results = {}
self.results['imgurl1'] = webqtlUtil.genRandStr("CTLline_") + ".png"
self.results['imgloc1'] = GENERATED_IMAGE_DIR + self.results['imgurl1']
self.results['ctlresult'] = significant
self.results['requestform'] = requestform # Store the user specified parameters for the output page
# Create the lineplot
r_png(self.results['imgloc1'], width=1000, height=600, type='cairo-png')
self.r_lineplot(res, significance = significance)
r_dev_off()
n = 2 # We start from 2, since R starts from 1 :)
for trait in self.trait_db_list:
# Create the QTL like CTL plots
self.results['imgurl' + str(n)] = webqtlUtil.genRandStr("CTL_") + ".png"
self.results['imgloc' + str(n)] = GENERATED_IMAGE_DIR + self.results['imgurl' + str(n)]
r_png(self.results['imgloc' + str(n)], width=1000, height=600, type='cairo-png')
self.r_plotCTLobject(res, (n-1), significance = significance, main='Phenotype ' + trait)
r_dev_off()
n = n + 1
# Flush any output from R
sys.stdout.flush()
# Create the interactive graph for cytoscape visualization (Nodes and Edges)
print(type(significant))
if not type(significant) == ri.RNULLType:
for x in range(len(significant[0])):
print(significant[0][x], significant[1][x], significant[2][x]) # Debug to console
tsS = significant[0][x].split(':') # Source
tsT = significant[2][x].split(':') # Target
gtS = TRAIT.GeneralTrait(name = tsS[0], dataset_name = tsS[1]) # Retrieve Source info from the DB
gtT = TRAIT.GeneralTrait(name = tsT[0], dataset_name = tsT[1]) # Retrieve Target info from the DB
self.addNode(gtS)
self.addNode(gtT)
self.addEdge(gtS, gtT, significant, x)
significant[0][x] = gtS.symbol + " (" + gtS.name + ")" # Update the trait name for the displayed table
significant[2][x] = gtT.symbol + " (" + gtT.name + ")" # Update the trait name for the displayed table
self.elements = json.dumps(self.nodes_list + self.edges_list)
def run_analysis(self, requestform):
logger.info("Starting PheWAS analysis on dataset")
genofilelocation = locate(
"BXD.geno", "genotype") # Get the location of the BXD genotypes
precompfile = locate_phewas(
"PheWAS_pval_EMMA_norm.RData",
"auwerx") # Get the location of the pre-computed EMMA results
# Get user parameters, trait_id and dataset, and store/update them in self
self.trait_id = requestform["trait_id"]
self.datasetname = requestform["dataset"]
self.dataset = data_set.create_dataset(self.datasetname)
self.region = int(requestform["num_region"])
self.mtadjust = str(requestform["sel_mtadjust"])
# Logger.Info some debug
logger.info("self.trait_id:" + self.trait_id + "\n")
logger.info("self.datasetname:" + self.datasetname + "\n")
logger.info("self.dataset.type:" + self.dataset.type + "\n")
# GN Magic ?
self.this_trait = GeneralTrait(dataset=self.dataset,
name=self.trait_id,
get_qtl_info=False,
get_sample_info=False)
logger.info(vars(self.this_trait))
# Set the values we need
self.chr = str(self.this_trait.chr)
self.mb = int(self.this_trait.mb)
# logger.info some debug
logger.info("location:" + self.chr + ":" + str(self.mb) + "+/-" +
str(self.region) + "\n")
# Load in the genotypes file *sigh* to make the markermap
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_seq(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()
self.results = {}
self.results['imgurl1'] = webqtlUtil.genRandStr("phewas_") + ".png"
self.results['imgloc1'] = GENERATED_IMAGE_DIR + self.results['imgurl1']
self.results['mtadjust'] = self.mtadjust
logger.info("IMAGE AT:", self.results['imgurl1'])
logger.info("IMAGE AT:", self.results['imgloc1'])
# 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
assert (precompfile)
assert (phenoaligner)
assert (snpaligner)
phewasres = self.r_PheWASManhattan("Test", precompfile, phenoaligner,
snpaligner, "None", self.chr,
self.mb, self.region,
self.results['imgloc1'],
self.mtadjust)
self.results['phewas1'] = phewasres[0]
self.results['phewas2'] = phewasres[1]
self.results['tabulardata'] = phewasres[2]
self.results['R_debuglog'] = phewasres[3]
#self.r_PheWASManhattan(allpvalues)
#self.r_Stop()
logger.info("Initialization of PheWAS done !")