def test_calch2(self):
logging.info("Leap test_calch2")
related_file = os.path.abspath(
os.path.join(self.gold_dir, 'dataset1.related'))
bed, _ = leapUtils.loadData(self.bedbase,
None,
self.phen_fn,
loadSNPs=False)
keepArr = leapUtils.loadRelatedFile(bed, related_file)
for i in range(1, 11):
h2_file = os.path.abspath(
os.path.join(self.gold_dir, 'dataset1_nochr{0}.h2'.format(i)))
eigen_file = os.path.abspath(
os.path.join(self.tempout_dir,
'dataset1_nochr{}.npz'.format(i)))
eigen = np.load(eigen_file)
extractSim = 'dataset1/extracts/nochr{0}_extract.txt'.format(i)
expected_h2 = np.loadtxt(h2_file, usecols=[0])
h2 = leapMain.calcH2(self.phen_fn,
0.001,
eigen,
keepArr=keepArr,
h2coeff=1.0)
assert np.abs(
h2 - expected_h2) < 1e-5, 'Incorrect heritability estimated'
def test_gwas(self):
logging.info("Leap test_gwas")
for i in xrange(1,11):
h2_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1_nochr{0}.h2'.format(i)))
h2 = np.loadtxt(h2_file, usecols=[0])
ref_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1_nochr{}.gwas.out.txt'.format(i)))
extractSim = 'dataset1/extracts/nochr{0}_extract.txt'.format(i)
extract = 'dataset1/extracts/chr{0}_extract.txt'.format(i)
bedSim, phe = leapUtils.loadData(self.bedbase, extractSim, self.phen_fn, loadSNPs=True)
bedTest, phe = leapUtils.loadData(self.bedbase, extract, self.phen_fn, loadSNPs=True)
output_file = os.path.abspath(os.path.join(self.tempout_dir, 'dataset1_nochr{}.gwas.out.txt'.format(i)))
liab_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1_nochr{}.liabs'.format(i)))
eigen_file = os.path.abspath(os.path.join(self.tempout_dir, 'dataset1_nochr{}.npz'.format(i)))
leapMain.leapGwas(bedSim, bedTest, liab_file, h2, output_file, eigenFile=eigen_file)
self.compare_gwas(ref_file, output_file)
def test_probit(self):
logging.info("Leap test_probit")
related_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1.related'))
bed, _ = leapUtils.loadData(self.bedbase, None, self.phen_fn, loadSNPs=False)
keepArr = leapUtils.loadRelatedFile(bed, related_file)
for i in xrange(1,11):
h2_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1_nochr{0}.h2'.format(i)))
h2 = np.loadtxt(h2_file, usecols=[0])
eigen_file = os.path.abspath(os.path.join(self.tempout_dir, 'dataset1_nochr{}.npz'.format(i)))
eigen = np.load(eigen_file)
ref_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1_nochr{}.liabs'.format(i)))
extractSim = 'dataset1/extracts/nochr{0}_extract.txt'.format(i)
bed, _ = leapUtils.loadData(self.bedbase, extractSim, self.phen_fn, loadSNPs=True)
output_file = os.path.abspath(os.path.join(self.tempout_dir, 'dataset1_nochr{}'.format(i)))
leapMain.probit(bed, self.phen_fn, h2=h2, prev=0.001, eigen=eigen, outFile=output_file, keepArr=keepArr, hess=False)
self.compare_pheno(ref_file, output_file+'.liabs')
def test_calch2(self):
logging.info("Leap test_calch2")
related_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1.related'))
bed, _ = leapUtils.loadData(self.bedbase, None, self.phen_fn, loadSNPs=False)
keepArr = leapUtils.loadRelatedFile(bed, related_file)
for i in xrange(1,11):
h2_file = os.path.abspath(os.path.join(self.gold_dir, 'dataset1_nochr{0}.h2'.format(i)))
eigen_file = os.path.abspath(os.path.join(self.tempout_dir, 'dataset1_nochr{}.npz'.format(i)))
eigen = np.load(eigen_file)
extractSim = 'dataset1/extracts/nochr{0}_extract.txt'.format(i)
expected_h2 = np.loadtxt(h2_file, usecols=[0])
h2 = leapMain.calcH2(self.phen_fn, 0.001, eigen, keepArr=keepArr, h2coeff=1.0)
assert np.abs(h2-expected_h2)<1e-5, 'Incorrect heritability estimated'
def setUpClass(self):
from fastlmm.util.util import create_directory_if_necessary
create_directory_if_necessary(self.tempout_dir, isfile=False)
self.pythonpath = os.path.abspath(os.path.join(os.path.dirname(os.path.realpath(__file__))))
self.bedbase = os.path.join(self.pythonpath, 'dataset1/dataset1')
self.phen_fn = os.path.join(self.pythonpath, 'dataset1/dataset1.phe')
#Create eigendecompositions
logging.info("Creating eigendecomposition files")
for i in xrange(1,11):
output_file = os.path.abspath(os.path.join(self.tempout_dir, 'dataset1_nochr{}.npz'.format(i)))
extractSim = 'dataset1/extracts/nochr{0}_extract.txt'.format(i)
bed, _ = leapUtils.loadData(self.bedbase, extractSim, self.phen_fn, loadSNPs=True)
leapMain.eigenDecompose(bed, output_file)
