def test_c_reader_pheno(self):
snpdata1 = Pheno(self.currentFolder + "/examples/toydata.phe").read()
self.assertEqual(np.float64, snpdata1.val.dtype)
snpdata1.val[1,0] = np.NaN # Inject a missing value to test writing and reading missing values
output = "tempdir/snpreader/toydata.phe"
create_directory_if_necessary(output)
Pheno.write(output, snpdata1)
snpreader = Pheno(output)
_fortesting_JustCheckExists().input(snpreader)
s = str(snpreader)
snpdata2 = snpreader.read()
np.testing.assert_array_almost_equal(snpdata1.val, snpdata2.val, decimal=10)
snpdata1 = Pheno(self.currentFolder + "/examples/toydata.phe").read()
import pysnptools.util.pheno as pstpheno
dict = pstpheno.loadOnePhen(self.currentFolder + "/examples/toydata.phe",missing="")
snpdata3 = Pheno(dict).read()
np.testing.assert_array_almost_equal(snpdata1.val, snpdata3.val, decimal=10)
dict = pstpheno.loadOnePhen(self.currentFolder + "/examples/toydata.phe",missing="",vectorize=True)
assert len(dict['vals'].shape)==1, "test 1-d array of values"
snpdata3 = Pheno(dict).read()
np.testing.assert_array_almost_equal(snpdata1.val, snpdata3.val, decimal=10)
snpdata4 = Pheno(None,iid_if_none=snpdata1.iid)
assert (snpdata4.row == snpdata1.row).all() and snpdata4.col_count == 0
snpdata5 = Pheno(self.currentFolder + "/examples/toydata.id.phe").read()
np.testing.assert_array_almost_equal(snpdata1.val, snpdata5.val, decimal=10)
snpdata6 = Pheno(self.currentFolder + "/examples/toydata.fid.phe").read()
np.testing.assert_array_almost_equal(snpdata1.val, snpdata6.val, decimal=10)
def test_c_reader_pheno(self):
snpdata1 = Pheno(self.currentFolder + "/examples/toydata.phe").read()
self.assertEqual(np.float64, snpdata1.val.dtype)
snpdata1.val[
1,
0] = np.NaN # Inject a missing value to test writing and reading missing values
output = "tempdir/snpreader/toydata.phe"
create_directory_if_necessary(output)
Pheno.write(output, snpdata1)
snpreader = Pheno(output)
_fortesting_JustCheckExists().input(snpreader)
s = str(snpreader)
snpdata2 = snpreader.read()
np.testing.assert_array_almost_equal(snpdata1.val,
snpdata2.val,
decimal=10)
snpdata1 = Pheno(self.currentFolder + "/examples/toydata.phe").read()
import pysnptools.util.pheno as pstpheno
dict = pstpheno.loadOnePhen(self.currentFolder +
"/examples/toydata.phe",
missing="")
snpdata3 = Pheno(dict).read()
np.testing.assert_array_almost_equal(snpdata1.val,
snpdata3.val,
decimal=10)
dict = pstpheno.loadOnePhen(self.currentFolder +
"/examples/toydata.phe",
missing="",
vectorize=True)
assert len(dict['vals'].shape) == 1, "test 1-d array of values"
snpdata3 = Pheno(dict).read()
np.testing.assert_array_almost_equal(snpdata1.val,
snpdata3.val,
decimal=10)
snpdata4 = Pheno(None, iid_if_none=snpdata1.iid)
assert (snpdata4.row == snpdata1.row).all() and snpdata4.col_count == 0
snpdata5 = Pheno(self.currentFolder +
"/examples/toydata.id.phe").read()
np.testing.assert_array_almost_equal(snpdata1.val,
snpdata5.val,
decimal=10)
snpdata6 = Pheno(self.currentFolder +
"/examples/toydata.fid.phe").read()
np.testing.assert_array_almost_equal(snpdata1.val,
snpdata6.val,
decimal=10)
def load_data(self):
"""load data
"""
tt0 = time.time()
logging.info("loading data...")
if self.num_snps_in_memory <= self.snpreader.iid_count : raise Exception("Expect self.num_snps_in_memory, {0} > self.snpreader.iid_count, {1}".format(self.num_snps_in_memory, self.total_num_ind))
self.sid = pd.Series(self.snpreader.sid)
# load phenotype
pheno = pstpheno.loadOnePhen(self.pheno_fn,self.mpheno, vectorize=True)
self.ind_iid = pheno['iid'] #!!LATER: bug? It looks like we record the pre-intersect iids only to write out the pcs later? Why?
# load covariates
self.X, cov_iid = self.load_covariates(pheno)
# Set up the snps
# G is the standardized snps. The GClass.factory will either load them into memory or will note their file and read them as needed.
self.G = GClass.factory(self.snpreader, self.num_snps_in_memory, self.standardizer, self.blocksize)
#!!LATER Should we give preference to self.G since reordering it is the most expensive?
(self.y, yiid), (self.X, xiid), self.G = pstutil.intersect_apply([(pheno['vals'], pheno['iid']), (self.X, cov_iid), self.G], sort_by_dataset=False)
# make sure input data isn't modified
self.X.flags.writeable = False
self.y.flags.writeable = False
logging.info("...done. Loading time %.2f s" % (float(time.time() - tt0)))
def setUpClass(self):
currentFolder = os.path.dirname(os.path.realpath(__file__))
self.snp_fn = currentFolder + "/../../tests/datasets/mouse/alldata"
self.pheno_fn = currentFolder + "/../../tests/datasets/mouse/pheno_10_causals.txt"
#self.cov_fn = currentFolder + "/examples/toydata.cov"
# load data
###################################################################
snp_reader = Bed(self.snp_fn)
pheno = pstpheno.loadOnePhen(self.pheno_fn)
#cov = pstpheno.loadPhen(self.cov_fn)
# intersect sample ids
snp_reader, pheno = pysnptools.util.intersect_apply([snp_reader, pheno])
self.G = snp_reader.read(order='C').val
self.G = stdizer.Unit().standardize(self.G)
self.G.flags.writeable = False
self.y = pheno['vals'][:,0]
self.y.flags.writeable = False
# load pcs
#self.G_cov = cov['vals']
self.G_cov = np.ones((len(self.y), 1))
self.G_cov.flags.writeable = False
def load_data(self):
"""load data
"""
with patch.dict('os.environ', {'ARRAY_MODULE': 'numpy'}) as _:
tt0 = time.time()
logging.info("loading data...")
if self.num_snps_in_memory <= self.snpreader.iid_count : raise Exception("Expect self.num_snps_in_memory, {0} > self.snpreader.iid_count, {1}".format(self.num_snps_in_memory, self.total_num_ind))
self.sid = pd.Series(self.snpreader.sid)
# load phenotype
pheno = pstpheno.loadOnePhen(self.pheno_fn,self.mpheno, vectorize=True)
self.ind_iid = pheno['iid'] #!!LATER: bug? It looks like we record the pre-intersect iids only to write out the pcs later? Why?
# load covariates
self.X, cov_iid = self.load_covariates(pheno)
# Set up the snps
# G is the standardized snps. The GClass.factory will either load them into memory or will note their file and read them as needed.
self.G = GClass.factory(self.snpreader, self.num_snps_in_memory, self.standardizer, self.blocksize,count_A1=self.count_A1)
#!!LATER Should we give preference to self.G since reordering it is the most expensive?
(self.y, yiid), (self.X, xiid), self.G = pstutil.intersect_apply([(pheno['vals'], pheno['iid']), (self.X, cov_iid), self.G], sort_by_dataset=False)
# make sure input data isn't modified
self.X.flags.writeable = False
self.y.flags.writeable = False
logging.info("...done. Loading time %.2f s" % (float(time.time() - tt0)))
def loadPheno(bed, phenoFile, missingPhenotype="-9", keepDict=False):
pheno = phenoUtils.loadOnePhen(phenoFile, missing=missingPhenotype, vectorize=True)
checkIntersection(bed, pheno, "phenotypes")
bed, pheno = pstutil.intersect_apply([bed, pheno])
if not keepDict:
pheno = pheno["vals"]
return bed, pheno
def setUpClass(self):
currentFolder = os.path.dirname(os.path.realpath(__file__))
self.snp_fn = currentFolder + "/../../tests/datasets/mouse/alldata"
self.pheno_fn = currentFolder + "/../../tests/datasets/mouse/pheno_10_causals.txt"
#self.cov_fn = currentFolder + "/examples/toydata.cov"
# load data
###################################################################
snp_reader = Bed(self.snp_fn)
pheno = pstpheno.loadOnePhen(self.pheno_fn)
#cov = pstpheno.loadPhen(self.cov_fn)
# intersect sample ids
snp_reader, pheno = pysnptools.util.intersect_apply(
[snp_reader, pheno])
self.G = snp_reader.read(order='C').val
self.G = stdizer.Unit().standardize(self.G)
self.G.flags.writeable = False
self.y = pheno['vals'][:, 0]
self.y.flags.writeable = False
# load pcs
#self.G_cov = cov['vals']
self.G_cov = np.ones((len(self.y), 1))
self.G_cov.flags.writeable = False
def load_snp_data(snpreader,
pheno_fn,
cov_fn=None,
offset=True,
mpheno=0,
standardizer=Unit()):
"""Load plink files
----------
snpreader : snpreader object
object to read in binary SNP file
pheno_fn : str
File name of phenotype file
cov_fn : str
File name of covariates file
offset : bool, default=True
Adds offset to the covariates specified in cov_fn, if neccesssary
Returns
-------
G : array, shape = [n_samples, n_features]
SNP matrix
X : array, shape = [n_samples, n_covariates]
Matrix of covariates (e.g. age, gender)
y : array, shape = [n_samples]
Phenotype (target) vector
"""
#TODO: completely remove this
pheno = pstpheno.loadOnePhen(pheno_fn, mpheno, vectorize=True)
geno = snpreader.read(order='C').standardize(standardizer)
# sanity check
#assert np.testing.assert_array_equal(ind_iid, pheno['iid'][indarr[:,0]])
# load covariates or generate vector of ones (for bias)
if cov_fn == None:
cov = {'vals': np.ones((len(pheno['iid']), 1)), 'iid': pheno['iid']}
else:
cov = pstpheno.loadPhen(cov_fn)
(y, yiid), G, (X, xiid) = pstutil.intersect_apply(
[(pheno['vals'], pheno['iid']), geno, (cov['vals'], cov['iid'])],
sort_by_dataset=False)
G = G.read(order='C', view_ok=True)
# add bias column if not present
if offset and sp.all(X.std(0) != 0):
offset = sp.ones((len(indarr), 1))
X = sp.hstack((X, offset))
return G, X, y
def loadPheno(bed, phenoFile, missingPhenotype='-9', keepDict=False):
pheno = phenoUtils.loadOnePhen(phenoFile,
missing=missingPhenotype,
vectorize=True)
checkIntersection(bed, pheno, 'phenotypes')
bed, pheno = pstutil.intersect_apply([bed, pheno])
if (not keepDict): pheno = pheno['vals']
return bed, pheno
def loadCovars(bed, covarFile):
covarsDict = phenoUtils.loadOnePhen(covarFile, vectorize=False)
checkIntersection(bed, covarsDict, "covariates", checkSuperSet=True)
_, covarsDict = pstutil.intersect_apply([bed, covarsDict])
covar = covarsDict["vals"]
covar -= np.mean(covar, axis=0)
covar /= np.std(covar, axis=0)
return covar
def loadCovars(bed, covarFile):
covarsDict = phenoUtils.loadOnePhen(covarFile, vectorize=False)
checkIntersection(bed, covarsDict, 'covariates', checkSuperSet=True)
_, covarsDict = pstutil.intersect_apply([bed, covarsDict])
covar = covarsDict['vals']
covar -= np.mean(covar, axis=0)
covar /= np.std(covar, axis=0)
return covar
def loadRelatedFile(bed, relFile):
relatedDict = phenoUtils.loadOnePhen(relFile, vectorize=True)
checkIntersection(bed, relatedDict, "relatedness", checkSuperSet=True)
_, relatedDict = pstutil.intersect_apply([bed, relatedDict])
related = relatedDict["vals"]
keepArr = related < 0.5
print np.sum(~keepArr), "individuals will be removed due to high relatedness"
return keepArr
def loadRelatedFile(bed, relFile):
relatedDict = phenoUtils.loadOnePhen(relFile, vectorize=True)
checkIntersection(bed, relatedDict, 'relatedness', checkSuperSet=True)
_, relatedDict = pstutil.intersect_apply([bed, relatedDict])
related = relatedDict['vals']
keepArr = (related < 0.5)
print np.sum(
~keepArr), 'individuals will be removed due to high relatedness'
return keepArr
def main():
"""
example that compares output to fastlmmc
"""
# set up data
phen_fn = "../feature_selection/examples/toydata.phe"
snp_fn = "../feature_selection/examples/toydata.5chrom.bed"
#chrom_count = 5
# load data
###################################################################
snp_reader = Bed(snp_fn)
pheno = pstpheno.loadOnePhen(phen_fn)
cov = None
#cov = pstpheno.loadPhen(self.cov_fn)
snp_reader, pheno, cov = intersect_apply([snp_reader, pheno, cov])
G = snp_reader.read(order='C').val
G = stdizer.Unit().standardize(G)
G.flags.writeable = False
y = pheno['vals'][:, 0]
y.flags.writeable
# load pcs
#G_pc = cov['vals']
#G_pc.flags.writeable = False
delta = 2.0
gwas = WindowingGwas(G, y, delta=delta)
pv = gwas.run_gwas()
from fastlmm.association.tests.test_gwas import GwasTest
REML = False
snp_pos_sim = snp_reader.sid
snp_pos_test = snp_reader.sid
os.environ["FastLmmUseAnyMklLib"] = "1"
gwas_c = GwasTest(snp_fn,
phen_fn,
snp_pos_sim,
snp_pos_test,
delta,
REML=REML,
excludeByPosition=0)
gwas_c.run_gwas()
import pylab
pylab.plot(np.log(pv), np.log(gwas_c.p_values), "+")
pylab.plot(np.arange(-18, 0), np.arange(-18, 0), "-k")
pylab.show()
np.testing.assert_array_almost_equal(np.log(pv),
np.log(gwas_c.p_values),
decimal=3)
simple_manhattan_plot(pv)
def _fixup_pheno(pheno, bed=None, missingPhenotype='-9'): if (isinstance(pheno, str)): if (bed is not None): bed, pheno = loadPheno(bed, pheno, missingPhenotype, keepDict=True) return bed, pheno else: phenoDict = phenoUtils.loadOnePhen(pheno, missing=missingPhenotype, vectorize=True) return phenoDict else: if (bed is not None): return bed, pheno else: return pheno
def load_snp_data(snpreader, pheno_fn, cov_fn=None, offset=True, mpheno=0, standardizer=Unit()):
"""Load plink files
----------
snpreader : snpreader object
object to read in binary SNP file
pheno_fn : str
File name of phenotype file
cov_fn : str
File name of covariates file
offset : bool, default=True
Adds offset to the covariates specified in cov_fn, if neccesssary
Returns
-------
G : array, shape = [n_samples, n_features]
SNP matrix
X : array, shape = [n_samples, n_covariates]
Matrix of covariates (e.g. age, gender)
y : array, shape = [n_samples]
Phenotype (target) vector
"""
#TODO: completely remove this
pheno = pstpheno.loadOnePhen(pheno_fn,mpheno, vectorize=True)
geno = snpreader.read(order='C').standardize(standardizer)
# sanity check
#assert np.testing.assert_array_equal(ind_iid, pheno['iid'][indarr[:,0]])
# load covariates or generate vector of ones (for bias)
if cov_fn == None:
cov = {'vals': np.ones((len(pheno['iid']), 1)), 'iid':pheno['iid']}
else:
cov = pstpheno.loadPhen(cov_fn)
(y, yiid), G, (X, xiid) = pstutil.intersect_apply([(pheno['vals'],pheno['iid']), geno, (cov['vals'],cov['iid'])], sort_by_dataset=False)
G = G.read(order='C', view_ok=True)
# add bias column if not present
if offset and sp.all(X.std(0)!=0):
offset = sp.ones((len(indarr),1))
X = sp.hstack((X,offset))
return G, X, y
def test_preload_files(self):
logging.info("TestSingleSnp test_preload_files")
test_snps = self.bedbase
pheno = pstpheno.loadOnePhen(self.phen_fn,vectorize=True)
covar = pstpheno.loadPhen(self.cov_fn)
bed = Bed(test_snps, count_A1=False)
output_file_name = self.file_name("preload_files")
frame = single_snp(test_snps=bed[:,:10], pheno=pheno, G0=test_snps, mixing=0,leave_out_one_chrom=False,
covar=covar, output_file_name=output_file_name,count_A1=False
)
self.compare_files(frame,"one")
def _fixup_pheno(pheno, bed=None, missingPhenotype='-9'):
if (isinstance(pheno, str)):
if (bed is not None):
bed, pheno = loadPheno(bed, pheno, missingPhenotype, keepDict=True)
return bed, pheno
else:
phenoDict = phenoUtils.loadOnePhen(pheno,
missing=missingPhenotype,
vectorize=True)
return phenoDict
else:
if (bed is not None): return bed, pheno
else: return pheno
def main():
"""
example that compares output to fastlmmc
"""
# set up data
phen_fn = "../feature_selection/examples/toydata.phe"
snp_fn = "../feature_selection/examples/toydata.5chrom"
#chrom_count = 5
# load data
###################################################################
snp_reader = Bed(snp_fn)
pheno = pstpheno.loadOnePhen(phen_fn)
cov = None
#cov = pstpheno.loadPhen(self.cov_fn)
snp_reader, pheno, cov = intersect_apply([snp_reader, pheno, cov])
G = snp_reader.read(order='C').val
G = stdizer.Unit().standardize(G)
G.flags.writeable = False
y = pheno['vals'][:,0]
y.flags.writeable
# load pcs
#G_pc = cov['vals']
#G_pc.flags.writeable = False
delta = 2.0
gwas = WindowingGwas(G, y, delta=delta)
pv = gwas.run_gwas()
from fastlmm.association.tests.test_gwas import GwasTest
REML = False
snp_pos_sim = snp_reader.sid
snp_pos_test = snp_reader.sid
os.environ["FastLmmUseAnyMklLib"] = "1"
gwas_c = GwasTest(snp_fn, phen_fn, snp_pos_sim, snp_pos_test, delta, REML=REML, excludeByPosition=0)
gwas_c.run_gwas()
import pylab
pylab.plot(np.log(pv), np.log(gwas_c.p_values), "+")
pylab.plot(np.arange(-18, 0), np.arange(-18,0), "-k")
pylab.show()
np.testing.assert_array_almost_equal(np.log(pv), np.log(gwas_c.p_values), decimal=3)
simple_manhattan_plot(pv)
def test_preload_files(self):
logging.info("TestSingleSnp test_preload_files")
from pysnptools.snpreader import Bed
test_snps = self.bedbase
pheno = pstpheno.loadOnePhen(self.phen_fn,vectorize=True)
covar = pstpheno.loadPhen(self.cov_fn)
bed = Bed(test_snps)
output_file_name = self.file_name("preload_files")
frame = single_snp(test_snps=bed[:,:10], pheno=pheno, G0=test_snps, mixing=0,
covar=covar, output_file_name=output_file_name
)
self.compare_files(frame,"one")
def load_intersect(snp_reader, pheno_fn_or_none, snp_set=AllSnps()):
"""
load SNPs and phenotype, intersect ids
----------------------------------------------------------------------
Input:
bed_reader : SnpReader object (e.g. BedReader)
pheno_fn : str, file name of phenotype file, defa
----------------------------------------------------------------------
Output:
G : numpy array containing SNP data
y : numpy (1d) containing phenotype
----------------------------------------------------------------------
"""
standardizer = stdizer.Unit()
geno = snp_reader.read(order='C', snp_set=snp_set)
G = geno['snps']
G = standardizer.standardize(G)
snp_names = geno['rs']
chr_ids = geno['pos'][:, 0]
if not pheno_fn_or_none is None:
# load phenotype
pheno = pstpheno.loadOnePhen(pheno_fn_or_none, 0)
y = pheno['vals'][:, 0]
# load covariates and intersect ids
import warnings
warnings.warn(
"This intersect_ids is deprecated. Pysnptools includes newer versions of intersect_ids",
DeprecationWarning)
indarr = util.intersect_ids([pheno['iid'], snp_reader.original_iids])
#print "warning: random phen"
#y = np.random.random_sample(len(y))
if not (indarr[:, 0] == indarr[:, 1]).all():
assert False, "ERROR: this code assumes the same order for snp and phen file"
print "reindexing"
y = y[indarr[:, 0]]
G = G[indarr[:, 1]]
else:
y = None
return G, y, snp_names, chr_ids
def load_intersect(snp_reader, pheno_fn_or_none,snp_set=AllSnps()):
"""
load SNPs and phenotype, intersect ids
----------------------------------------------------------------------
Input:
bed_reader : SnpReader object (e.g. BedReader)
pheno_fn : str, file name of phenotype file, defa
----------------------------------------------------------------------
Output:
G : numpy array containing SNP data
y : numpy (1d) containing phenotype
----------------------------------------------------------------------
"""
standardizer = stdizer.Unit()
geno = snp_reader.read(order='C',snp_set=snp_set)
G = geno['snps']
G = standardizer.standardize(G)
snp_names = geno['rs']
chr_ids = geno['pos'][:,0]
if not pheno_fn_or_none is None:
# load phenotype
pheno = pstpheno.loadOnePhen(pheno_fn_or_none, 0)
y = pheno['vals'][:,0]
# load covariates and intersect ids
import warnings
warnings.warn("This intersect_ids is deprecated. Pysnptools includes newer versions of intersect_ids", DeprecationWarning)
indarr = util.intersect_ids([pheno['iid'], snp_reader.original_iids])
#print "warning: random phen"
#y = np.random.random_sample(len(y))
if not (indarr[:,0] == indarr[:,1]).all():
assert False, "ERROR: this code assumes the same order for snp and phen file"
print "reindexing"
y = y[indarr[:,0]]
G = G[indarr[:,1]]
else:
y = None
return G, y, snp_names, chr_ids
def test_SNC(self):
logging.info("TestSNC")
test_snps = self.bedbase
pheno = pstpheno.loadOnePhen(self.phen_fn,vectorize=True)
covar = pstpheno.loadPhen(self.cov_fn)
bed = Bed(test_snps, count_A1=False)
snc = bed.read()
snc.val[:,2] = [0] * snc.iid_count # make SNP #2 have constant values (aka a SNC)
output_file_name = self.file_name("snc")
frame = single_snp(test_snps=snc[:,:10], pheno=pheno, G0=snc, mixing=0,leave_out_one_chrom=False,
covar=covar, output_file_name=output_file_name,count_A1=False
)
self.compare_files(frame,"snc")
def test_cid_intersect(self):
logging.info("TestSingleSnp test_cid_intersect")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = pstpheno.loadOnePhen(self.phen_fn,vectorize=True)
pheno['iid'] = np.vstack([pheno['iid'][::-1],[['Bogus','Bogus']]])
pheno['vals'] = np.hstack([pheno['vals'][::-1],[-34343]])
covar = self.cov_fn
output_file_name = self.file_name("cid_intersect")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, G0=test_snps, leave_out_one_chrom=False,
covar=covar, mixing=0,
output_file_name=output_file_name,count_A1=False
)
self.compare_files(frame,"one")
def test_preload_files(self):
logging.info("TestSingleSnp test_preload_files")
test_snps = self.bedbase
pheno = pstpheno.loadOnePhen(self.phen_fn, vectorize=True)
covar = pstpheno.loadPhen(self.cov_fn)
bed = Bed(test_snps)
output_file_name = self.file_name("preload_files")
frame = single_snp(test_snps=bed[:, :10],
pheno=pheno,
G0=test_snps,
mixing=0,
leave_out_one_chrom=False,
covar=covar,
output_file_name=output_file_name)
self.compare_files(frame, "one")
def test_preload_files(self):
logging.info("TestEpistasis test_preload_files")
from pysnptools.snpreader import Bed
test_snps = self.bedbase
pheno = pstpheno.loadOnePhen(self.phen_fn,vectorize=True)
covar = pstpheno.loadPhen(self.cov_fn)
bed = Bed(test_snps)
output_file = self.file_name("preload_files")
frame = epistasis(test_snps, pheno, G0=test_snps,
covar=covar,
sid_list_0=bed.sid[:10], #first 10 snps
sid_list_1=bed.sid[5:15], #Skip 5 snps, use next 10
output_file_name=output_file
)
sid0,sid1,pvalue_list =np.array(frame['SNP0']),np.array(frame['SNP1']),np.array(frame['PValue'])
self.compare_files(sid0,sid1,pvalue_list,"one")
def test_cid_intersect(self):
logging.info("TestSingleSnp test_cid_intersect")
test_snps = Bed(self.bedbase)
pheno = pstpheno.loadOnePhen(self.phen_fn, vectorize=True)
pheno['iid'] = np.vstack([pheno['iid'][::-1], [['Bogus', 'Bogus']]])
pheno['vals'] = np.hstack([pheno['vals'][::-1], [-34343]])
covar = self.cov_fn
output_file_name = self.file_name("cid_intersect")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps,
leave_out_one_chrom=False,
covar=covar,
mixing=0,
output_file_name=output_file_name)
self.compare_files(frame, "one")
def _run_once(self):
if self._ran_once:
return
self._ran_once = None
if isinstance(self.test_snps, str):
self.test_snps = Bed(self.test_snps)
if isinstance(self.G0, str):
self.G0 = Bed(self.G0)
if isinstance(self.pheno, str):
self.pheno = pstpheno.loadOnePhen(self.pheno,
vectorize=True,
missing='NaN')
if self.covar is not None and isinstance(self.covar, str):
self.covar = pstpheno.loadPhen(self.covar, missing='NaN')
if self.G1_or_none is not None and isinstance(self.G1_or_none, str):
self.G1_or_none = Bed(self.G1_or_none)
if self.sid_list_0 is None:
self.sid_list_0 = self.test_snps.sid
if self.sid_list_1 is None:
self.sid_list_1 = self.test_snps.sid
self.set_sid_sets()
#!!Should fix up to add only of no constant columns - will need to add a test case for this
if self.covar is None:
self.covar = np.ones((self.test_snps.iid_count, 1))
else:
self.covar = np.hstack(
(self.covar['vals'], np.ones((self.test_snps.iid_count, 1))))
self.n_cov = self.covar.shape[1]
if self.output_file_or_none is None:
self.__tempdirectory = ".working"
else:
self.__tempdirectory = self.output_file_or_none + ".working"
self._ran_once = True
def _run_once(self):
if self._ran_once:
return
self._ran_once = None
if isinstance(self.test_snps, str):
self.test_snps = Bed(self.test_snps)
if isinstance(self.G0, str):
self.G0 = Bed(self.G0)
if isinstance(self.pheno, str):
self.pheno = pstpheno.loadOnePhen(self.pheno,vectorize=True) #!! what about missing=-9?
if self.covar is not None and isinstance(self.covar, str):
self.covar = pstpheno.loadPhen(self.covar)#!! what about missing=-9?
if self.G1_or_none is not None and isinstance(self.G1_or_none, str):
self.G1_or_none = Bed(self.G1_or_none)
if self.sid_list_0 is None:
self.sid_list_0 = self.test_snps.sid
if self.sid_list_1 is None:
self.sid_list_1 = self.test_snps.sid
self.set_sid_sets()
#!!Should fix up to add only of no constant columns - will need to add a test case for this
if self.covar is None:
self.covar = np.ones((self.test_snps.iid_count, 1))
else:
self.covar = np.hstack((self.covar['vals'],np.ones((self.test_snps.iid_count, 1))))
self.n_cov = self.covar.shape[1]
if self.output_file_or_none is None:
self.__tempdirectory = ".working"
else:
self.__tempdirectory = self.output_file_or_none + ".working"
self._ran_once = True
def test_SNC(self):
logging.info("TestSNC")
test_snps = self.bedbase
pheno = pstpheno.loadOnePhen(self.phen_fn, vectorize=True)
covar = pstpheno.loadPhen(self.cov_fn)
bed = Bed(test_snps, count_A1=False)
snc = bed.read()
snc.val[:, 2] = 0 # make SNP #2 have constant values (aka a SNC)
output_file_name = self.file_name("snc")
frame = single_snp(test_snps=snc[:, :10],
pheno=pheno,
G0=snc,
mixing=0,
leave_out_one_chrom=False,
covar=covar,
output_file_name=output_file_name,
count_A1=False)
self.compare_files(frame, "snc")
def test_cid_intersect(self):
logging.info("TestEpistasis test_cid_intersect")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = pstpheno.loadOnePhen(self.phen_fn,vectorize=True)
pheno['iid'] = np.vstack([pheno['iid'][::-1],[['Bogus','Bogus']]])
pheno['vals'] = np.hstack([pheno['vals'][::-1],[-34343]])
covar = self.cov_fn
output_file = self.file_name("cid_intersect")
frame = epistasis(test_snps, pheno, G0=test_snps,
covar=covar,
sid_list_0=test_snps.sid[:10], #first 10 snps
sid_list_1=test_snps.sid[5:15], #Skip 5 snps, use next 10
output_file_name=output_file
)
sid0,sid1,pvalue_list =np.array(frame['SNP0']),np.array(frame['SNP1']),np.array(frame['PValue'])
self.compare_files(sid0,sid1,pvalue_list,"one")
def test_SNC(self):
logging.info("TestSNC")
from pysnptools.snpreader import Bed
test_snps = self.bedbase
pheno = pstpheno.loadOnePhen(self.phen_fn, vectorize=True)
covar = pstpheno.loadPhen(self.cov_fn)
bed = Bed(test_snps)
snc = bed.read()
snc.val[:, 2] = [
0
] * snc.iid_count # make SNP #2 have constant values (aka a SNC)
output_file_name = self.file_name("snc")
frame = single_snp(test_snps=snc[:, :10],
pheno=pheno,
G0=snc,
mixing=0,
covar=covar,
output_file_name=output_file_name)
self.compare_files(frame, "snc")
def run_fastlmmc(dataset,
output_dir,
pheno_index,
covFile=None,
species='mouse',
maxthreads=1,
featsel=False,
exclude=False,
condition=None):
# commands from fastlmmc:
# maxthreads
# condition
# exclude by position
# if condition:
# condition = '-SnpId1 %s' % condition[0]
# else:
# condition = ''
# temporary kludge because -excludeByPosition option is slow (at least for v2.05 and v2.06)
bfile = dataset
filtered_snp_reader = Bed('%s.FILTERED' % bfile)
full_snp_reader = Bed('%s.FULL' % bfile)
pheno_file = loadOnePhen('%s.pheno.txt' % dataset, i_pheno=pheno_index)
phenotype_name = pheno_file['header'][0]
v = globals()
chroms = map(str, range(1, species_chroms[species] + 1))
v.update(locals())
# loop through chromosomes and run
for i, chrom in enumerate(chroms):
# separate by chromosome for LOOCV
test_snps = filtered_snp_reader[:, filtered_snp_reader.pos[:, 0] ==
int(chrom)]
matrix_snps = full_snp_reader[:,
full_snp_reader.pos[:, 0] != int(chrom)]
# run snp with covar
if covFile:
df = single_snp(test_snps=test_snps,
pheno=pheno_file,
K0=matrix_snps,
covar=covFile)
else:
df = single_snp(test_snps=test_snps,
pheno=pheno_file,
K0=matrix_snps)
# format outputs
out_df = df.loc[:, ['SNP', 'Chr', 'ChrPos', 'PValue', 'SnpWeight']]
out_df.columns = ['SNP', 'CHR', 'BP', 'P', 'Beta']
# save results into data frame
if i == 0:
final = out_df
else:
final = final.append(out_df)
# output to csv
v.update(locals())
final.to_csv('%(output_dir)s/%(phenotype_name)s.gwas' % v,
sep='\t',
index=False)
def loadPheno(bed, phenoFile, missingPhenotype='-9', keepDict=False): pheno = phenoUtils.loadOnePhen(phenoFile, missing=missingPhenotype, vectorize=True) checkIntersection(bed, pheno, 'phenotypes') bed, pheno = pstutil.intersect_apply([bed, pheno]) if (not keepDict): pheno = pheno['vals'] return bed, pheno
