def testWeightDBEntry(self):
entry = WeightDBUtilities.WeightDBEntry("a", "b", "c", "d", "e")
self.assertEqual(entry.rsid, "a")
self.assertEqual(entry.gene, "b")
self.assertEqual(entry.weight, "c")
self.assertEqual(entry.ref_allele, "d")
self.assertEqual(entry.eff_allele, "e")
def testWeightDBEntryLogic(self):
weight_db_entry_logic = WeightDBUtilities.WeightDBEntryLogic(
"tests/_td/test.db")
expected_weights = expected_weights_results()
expected_extra = expected_extra_results()
self.assertEqual(len(weight_db_entry_logic.weights_by_gene),
len(expected_extra))
self.assertEqual(len(weight_db_entry_logic.gene_data_for_gene),
len(expected_extra))
for e in expected_extra:
self.assertTrue(e.gene in weight_db_entry_logic.weights_by_gene)
self.assertTrue(e.gene in weight_db_entry_logic.gene_data_for_gene)
actual_gene_data = weight_db_entry_logic.gene_data_for_gene[e.gene]
self.assertExtra([actual_gene_data], [e])
actual_weights = [
w for k, w in weight_db_entry_logic.weights_by_gene[
e.gene].items()
]
e_w = [w for w in expected_weights if w.gene == e.gene]
self.assertWeights(actual_weights, e_w)
self.assertEqual(len(weight_db_entry_logic.genes_for_an_rsid), 6)
for rsid, genes in weight_db_entry_logic.genes_for_an_rsid.items():
expected = [w.gene for w in expected_weights if w.rsid == rsid]
self.assertEqual(expected, genes)
def run(self):
folder = os.path.split(self.output_file)[0]
if len(folder) and not os.path.exists(folder):
os.makedirs(folder)
if os.path.exists(self.output_file):
logging.info(
"Results path %s already exists, delete it if you want it to be calculated again",
self.output_file)
return
people_by_id = None
if os.path.exists(self.selected_dosage_folder):
logging.info("Loading people")
samples_path = Utilities.samplesInputPath(
self.selected_dosage_folder)
if samples_path is not None:
people = Person.Person.loadPeople(samples_path)
people_by_id = {p.id: p for p in people}
logging.info("Loading weights from database: %s" %
(self.weight_db_path))
weight_db_logic = WeightDBUtilities.WeightDBEntryLogic(
self.weight_db_path)
#Normalization is ignored at the moment. Not sure if it will return.
results = None
normalization = None
results, normalization = self.resultsFromCovarianceFile(
weight_db_logic)
self.saveEntries(self.output_file, results)
logging.info("Successfully ran MetaXcan analysis")
def run(self):
if self.args.weight_db_path:
logging.info("Loading weight model")
weight_db_logic = WeightDBUtilities.WeightDBEntryLogic(
self.weight_db_path)
else:
weight_db_logic = None
names = Utilities.contentsWithRegexpFromFolder(self.gwas_folder,
self.gwas_regexp)
if not os.path.exists(self.output_folder):
os.makedirs(self.output_folder)
if len(names) == 0:
raise Exceptions.ReportableException(
"No GWAS files found on %s with pattern %s" % (
self.gwas_folder,
self.gwas_regexp.pattern,
))
for name in names:
try:
self.buildBetas(weight_db_logic, name)
# This just means that there is some extra stuff inside that directory,
# so I'm thinking we want to ignore it.
except Exceptions.BadFilename as e:
logging.info("Wrong file name: %s, skipping", e.msg)
pass
def testGeneEntry(self):
entry = WeightDBUtilities.GeneEntry("a", "b", "c", "d", "e", "f")
self.assertEqual(entry.gene, "a")
self.assertEqual(entry.gene_name, "b")
self.assertEqual(entry.n_snps, "c")
self.assertEqual(entry.pred_perf_R2, "d")
self.assertEqual(entry.pred_perf_pval, "e")
self.assertEqual(entry.pred_perf_qval, "f")
def run(self):
if not self.correlation_output and not self.covariance_output:
logging.info("Provide --correlation_output or --covariance_output or both")
return
logging.info("Loading Weights")
weight_db_logic = WeightDBUtilities.WeightDBEntryLogic(self.db_path)
self.buildFiles(weight_db_logic)
def run(self):
start = timer()
if not self.correlation_output and not self.covariance_output:
logging.info(
"Provide --correlation_output or --covariance_output or both")
return
logging.info("Loading Weights")
weight_db_logic = WeightDBUtilities.WeightDBEntryLogic(self.db_path)
logging.info("Building files")
self.buildFiles(weight_db_logic)
end = timer()
logging.info("Ran successfully in %s seconds", str(end - start))
def run(self):
weight_db_logic = WeightDBUtilities.WeightDBEntryLogic(
self.weight_db) if self.weight_db else None
contents = Utilities.contentsWithPatternsFromFolder(
self.data_folder_phase, ["gz"])
if os.path.exists(self.output_file):
logging.info(
"Variance output already exists, delete it if you want stuff to be figured out again"
)
return
dir = os.path.dirname(self.output_file)
if not os.path.exists(dir):
os.makedirs(dir)
for content in contents:
self.buildVarianceDB(weight_db_logic, content)
def run(self):
logging.info("Loading weight model")
weight_db_logic = WeightDBUtilities.WeightDBEntryLogic(
self.weight_db_path)
names = Utilities.contentsWithRegexpFromFolder(self.gwas_folder,
self.gwas_regexp)
if not os.path.exists(self.output_folder):
os.makedirs(self.output_folder)
for name in names:
try:
self.buildBetas(weight_db_logic, name)
# This just means that there is some extra stuff inside that directory,
# so I'm thinking we want to ignore it.
except Exceptions.BadFilename as e:
logging.info("Wrong file name: %s, skipping", e.msg)
pass
def testPrediXcanLoader(self):
weight_db = WeightDBUtilities.WeightDBEntryLogic("tests/_td/test.db")
loader = PrediXcanFormatUtilities.PrediXcanFormatDosageLoader(
"tests/_td/filtered_dosage/chr1.dosage.gz", weight_db)
snps, snps_by_rsid = loader.load()
self.assertEqual(sorted(snps_by_rsid.keys()),
["rs1", "rs2", "rs3", "rs4", "rs5", "rs6"])
self.assertEqual(snps[0].ref_allele, "T")
self.assertEqual(snps[0].eff_allele, "C")
self.assertEqual(snps[0].position, 1)
self.assertEqual(snps[0].data, [0, 0, 0, 2])
self.assertEqual(snps[1].ref_allele, "A")
self.assertEqual(snps[1].eff_allele, "G")
self.assertEqual(snps[1].position, 2)
self.assertEqual(snps[1].data, [1, 1, 1, 1])
self.assertEqual(snps[2].ref_allele, "G")
self.assertEqual(snps[2].eff_allele, "A")
self.assertEqual(snps[2].position, 3)
self.assertEqual(snps[2].data, [0, 1, 0, 1])
self.assertEqual(snps[3].ref_allele, "T")
self.assertEqual(snps[3].eff_allele, "C")
self.assertEqual(snps[3].position, 4)
self.assertEqual(snps[3].data, [0, 0, 0, 0])
self.assertEqual(snps[4].ref_allele, "C")
self.assertEqual(snps[4].eff_allele, "T")
self.assertEqual(snps[4].position, 5)
self.assertEqual(snps[4].data, [0, 0, 1, 1])
self.assertEqual(snps[5].ref_allele, "C")
self.assertEqual(snps[5].eff_allele, "T")
self.assertEqual(snps[5].position, 6)
self.assertEqual(snps[5].data, [0, 0, 0, 2])
def testWeightDB(self):
#test setup
class DummyCallback():
def __init__(self):
self.entries = []
def __call__(self, weight, extra):
self.entries.append((weight, extra))
expected_weights = expected_weights_results()
expected_extra = expected_extra_results()
weight_db = WeightDBUtilities.WeightDB("tests/_td/test.db")
#load gene data
extra = weight_db.loadExtraColumnData("A")
self.assertExtra(extra, [expected_extra[0]])
extra = weight_db.loadExtraColumnData("B")
self.assertExtra(extra, [expected_extra[1]])
extra = weight_db.loadExtraColumnData("C")
self.assertExtra(extra, [expected_extra[2]])
extra = weight_db.loadExtraColumnData("D")
self.assertExtra(extra, [expected_extra[3]])
extra = weight_db.loadExtraColumnData()
self.assertExtra(extra, expected_extra)
#load db
callback = DummyCallback()
weights = weight_db.loadFromDB(callback, "A")
self.assertWeights(
weights,
[expected_weights[0], expected_weights[1], expected_weights[2]])
self.assertEqual(len(callback.entries), 3)
callback_weights = [e[0] for e in callback.entries]
self.assertEqual(callback_weights, weights)
callback = DummyCallback()
weights = weight_db.loadFromDB(callback, "B")
self.assertWeights(weights, [expected_weights[3], expected_weights[4]])
self.assertEqual(len(callback.entries), 2)
callback_weights = [e[0] for e in callback.entries]
self.assertEqual(callback_weights, weights)
callback = DummyCallback()
weights = weight_db.loadFromDB(callback, "C")
self.assertWeights(weights, [expected_weights[5]])
self.assertEqual(len(callback.entries), 1)
callback_weights = [e[0] for e in callback.entries]
self.assertEqual(callback_weights, weights)
callback = DummyCallback()
weights = weight_db.loadFromDB(callback, "D")
self.assertWeights(weights, [expected_weights[6]])
self.assertEqual(len(callback.entries), 1)
callback_weights = [e[0] for e in callback.entries]
self.assertEqual(callback_weights, weights)
callback = DummyCallback()
weights = weight_db.loadFromDB(callback)
self.assertWeights(weights, expected_weights)
self.assertEqual(len(callback.entries), 7)
callback_weights = [e[0] for e in callback.entries]
self.assertEqual(callback_weights, weights)
#gene names
gene_names = weight_db.loadGeneNamesFromDB()
self.assertEqual(gene_names, ["A", "B", "C", "D"])
def testWeightDBInvalidPath(self):
weight_db = WeightDBUtilities.WeightDB("tests/kk.db")
with self.assertRaises(RuntimeError):
weight_db.openDBIfNecessary()
def testGeneEntry(self):
entry = WeightDBUtilities.GeneEntry("a", "b", "c", "d")
self.assertEqual(entry.gene, "a")
self.assertEqual(entry.gene_name, "b")
self.assertEqual(entry.R2, "c")
self.assertEqual(entry.n_snp, "d")
def buildBetas(self, db_filename):
filebase = os.path.basename(db_filename).replace(".db", "")
output_folder = os.path.abspath(self.args.output_directory)
logging.info("Processing betas for %s" % (db_filename))
self.args.weight_db_path = os.path.abspath(db_filename)
cov_directory = self.args.covariance_directory
if cov_directory.upper() == "SAME":
cov_directory = "/".join(self.args.weight_db_path.split("/")[0:-1])
extComponents = self.args.covariance_suffix.split("..")
if len(extComponents) > 1:
covext = "..".join(extComponents[0:-1])
dbext = extComponents[-1]
filebase = db_filename.replace(dbext, "")
self.args.covariance = "%s/%s%s" % (cov_directory, filebase.split("/")[-1], covext)
else:
self.args.covariance = "%s/%s%s" % (
cov_directory, filebase.strip("/")[-1], self.args.covariance_suffix)
file_prefix = filebase.split("/")[-1].split(".")[0]
beta_output = os.path.join(output_folder, file_prefix)
logging.info("Writing betas to %s" % (beta_output))
self.args.output_folder = beta_output
logging.info("Loading weight model")
weight_db_logic = WeightDBUtilities.WeightDBEntryLogic(self.args.weight_db_path)
betaScript = M03_betas.GetBetas(self.args)
names = Utilities.contentsWithRegexpFromFolder(self.args.gwas_folder, betaScript.gwas_regexp)
if not os.path.exists(beta_output):
os.makedirs(beta_output)
betaScript.output_folder = beta_output #os.path.join(output_folder, filebase)
if not os.path.exists(betaScript.output_folder):
os.makedirs(betaScript.output_folder)
report_prefix = None
for name in names:
name = name + ".gz"
if report_prefix is None:
report_prefix = name.split("/")[-1].split(".")[0]
try:
betaScript.buildBetas(weight_db_logic,name)
# This just means that there is some extra stuff inside that directory,
# so I'm thinking we want to ignore it.
except Exceptions.BadFilename as e:
logging.info("Wrong file name: %s, skipping", e.msg)
pass
suffix = ".csv"
self.args.output_file = os.path.join(output_folder,
report_prefix + "-" + file_prefix + suffix) # output_folder #os.path.join(output_folder, file_prefix) + ".csv"
# ZScores
logging.info("Calculating ZScores for %s" % (filebase))
zscoreScript = M04_zscores.CalculateZScores(self.args)
zscoreScript.folder_beta = betaScript.output_folder
zscoreScript.run()
def run(self):
# run the main function
if not self.covariance_output:
logging.info("Provide --covariance_output or both")
return
# list all the databases in the path
for file in sorted(os.listdir(self.db_path)):
if file.endswith(".db") and not file.endswith("sqtl.db"):
self.db_file_list.append(file)
# load the database and build the separate db entry logic
logging.info("Loading Weights")
count = 0
for file in self.db_file_list:
count += 1
filename = self.db_path + file
self.db_logic_dict[file] = WeightDBUtilities.WeightDBEntryLogic(
filename)
logging.info("Building file" + str(count))
# merge the info from different databases
tmp_logic_object = self.db_logic_dict[list(
self.db_logic_dict.keys())[0]]
count = 0
for db_logic in self.db_logic_dict.values():
count += 1
logging.info("Scanning file" + str(count))
# update the weights_by_gene
count_gene = 0
num_gene = len(db_logic.weights_by_gene.keys())
for gene in db_logic.weights_by_gene.keys():
count_gene += 1
if count_gene % 150 == 0:
logging.info("Percentage of genes processed " + str(
round(float(count_gene) / float(num_gene), 2) * 100))
if gene in tmp_logic_object.weights_by_gene.keys():
for rsid in db_logic.weights_by_gene[gene].keys():
if rsid not in tmp_logic_object.weights_by_gene[
gene].keys():
tmp_logic_object.weights_by_gene[gene][
rsid] = db_logic.weights_by_gene[gene][rsid]
else:
tmp_logic_object.weights_by_gene[
gene] = db_logic.weights_by_gene[gene]
# summary of gene count and snp count
logging.info("Total Genes:" +
str(len(tmp_logic_object.weights_by_gene.keys())))
rsid_count = 0
for gene in tmp_logic_object.weights_by_gene.keys():
rsid_count += len(tmp_logic_object.weights_by_gene[gene].keys())
logging.info("Total SNPs:" + str(rsid_count))
# store the pickle file
pickle_out = open("db_weight_logic.pickle", "wb")
pickle.dump(tmp_logic_object, pickle_out)
pickle_out.close()
# store the gene info
self.saveGeneInfo(tmp_logic_object)
# whether calculate the covariance directly
# store the database entry logic as pickle file
if not self.store_pickle_only:
self.buildFiles(tmp_logic_object)
logging.info("Ran successfully")
def run(self):
# run the main function
if not self.covariance_output:
logging.info("Provide --covariance_output or both")
return
# list all the databases in the path
for file in sorted(os.listdir(self.db_path)):
if file.endswith(".db"):
self.db_file_list.append(file)
# load the database and build the separate db entry logic
logging.info("Loading Weights")
count = 0
# gene level snplist
for gene in self.gene_list:
self.gene_rsid_dict[gene] = []
# reading weight database
tissue_vec = []
#self.db_file_list = self.db_file_list[0:1]
for file in self.db_file_list:
count += 1
filename = self.db_path + file
tissue = file.split(".db")[0]
tissue_vec.append(tissue)
self.db_logic_dict[tissue] = WeightDBUtilities.WeightDBEntryLogicGene(filename, self.gene_list)
self.db_logic_tissue[tissue] = copy.deepcopy(self.db_logic_dict[tissue])
logging.info("Building file" + str(count))
print "INFO: Building file" + str(count)
#print self.db_logic_dict[tissue].weights_by_gene[self.gene_list[0]]
# same tissue, different genes
for tissue in tissue_vec:
tmp_db_logic = self.db_logic_tissue[tissue]
tmp_db_logic.weights_by_gene['merged'] = {}
for gene in self.gene_list:
if gene in tmp_db_logic.weights_by_gene.keys():
tmp_db_logic.weights_by_gene['merged'].update(tmp_db_logic.weights_by_gene[gene])
del tmp_db_logic.weights_by_gene[gene]
if len(tmp_db_logic.weights_by_gene['merged'].keys()) > 0:
rsid_merged = tmp_db_logic.weights_by_gene['merged'].keys()
for rsid in rsid_merged:
tmp_db_logic.genes_for_an_rsid[rsid] = 'merged'
# build related data
self.buildFilesTissue(tmp_db_logic, tissue, self.chr_idx)
# same gene, different tissues
for gene in self.gene_list:
self.db_logic_gene[gene] = copy.deepcopy(self.db_logic_dict[tissue_vec[0]])
tmp_db_logic = self.db_logic_gene[gene]
if not gene in tmp_db_logic.weights_by_gene.keys():
tmp_db_logic.weights_by_gene[gene] = {}
for tissue in tissue_vec:
if gene in self.db_logic_dict[tissue].weights_by_gene.keys():
tmp_db_logic.weights_by_gene[gene].update(self.db_logic_dict[tissue].weights_by_gene[gene])
for tmp_gene in self.gene_list:
if tmp_gene != gene and tmp_gene in tmp_db_logic.weights_by_gene.keys():
del tmp_db_logic.weights_by_gene[tmp_gene]
if len(tmp_db_logic.weights_by_gene[gene].keys()) > 0:
rsid_merged = tmp_db_logic.weights_by_gene[gene].keys()
for rsid in rsid_merged:
tmp_db_logic.genes_for_an_rsid[rsid] = gene
# build related data
self.buildFilesGene(tmp_db_logic, gene, self.chr_idx)
else:
print "Not coresponding SNPs for gene " + gene
return
# summary of gene count and snp count
logging.info("Total Genes:" + str(len(self.db_logic_dict[tissue].weights_by_gene.keys())))
logging.info("Preprocess successfully")
print "INFO: Preprocess complete"
