def test_G0_has_reader(self):
logging.info("TestSingleSnp test_G0_has_reader")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G0_has_reader")
frame0 = 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(frame0, "one")
frame1 = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=KernelIdentity(test_snps.iid),
G1=test_snps,
leave_out_one_chrom=False,
covar=covar,
mixing=1,
output_file_name=output_file_name)
self.compare_files(frame1, "one")
def test_file_cache(self):
logging.info("TestSingleSnp test_file_cache")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G1")
cache_file = self.file_name("cache_file")+".npz"
if os.path.exists(cache_file):
os.remove(cache_file)
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,G0=test_snps[:,10:100],
covar=covar, G1=test_snps[:,100:200],
mixing=.5,
output_file_name=output_file_name,
cache_file = cache_file
)
self.compare_files(frame,"G1")
frame2 = single_snp(test_snps=test_snps[:,:10], pheno=pheno,G0=None,
covar=covar, G1=None,
mixing=.5,
output_file_name=output_file_name,
cache_file = cache_file
)
self.compare_files(frame2,"G1")
def test_file_cache(self):
logging.info("TestSingleSnp test_file_cache")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G1")
cache_file = self.file_name("cache_file") + ".npz"
if os.path.exists(cache_file):
os.remove(cache_file)
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps[:, 10:100],
leave_out_one_chrom=False,
covar=covar,
G1=test_snps[:, 100:200],
mixing=.5,
output_file_name=output_file_name,
cache_file=cache_file)
self.compare_files(frame, "G1")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps[:, 10:100],
leave_out_one_chrom=False,
covar=covar,
G1=test_snps[:, 100:200],
mixing=.5,
output_file_name=output_file_name,
cache_file=cache_file)
self.compare_files(frame, "G1")
def test_gb_goal(self):
logging.info("TestSingleSnp test_gb_goal")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("gb_goal")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
mixing=0,
leave_out_one_chrom=False,
G0=test_snps,
covar=covar,
GB_goal=0,
output_file_name=output_file)
self.compare_files(frame, "one")
output_file = self.file_name("gb_goal2")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
mixing=0,
leave_out_one_chrom=False,
G0=test_snps,
covar=covar,
GB_goal=.12,
output_file_name=output_file)
self.compare_files(frame, "one")
def test_file_cache(self):
logging.info("TestSingleSnp test_file_cache")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G1")
cache_file = self.file_name("cache_file") + ".npz"
if os.path.exists(cache_file):
os.remove(cache_file)
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps[:, 10:100],
covar=covar,
G1=test_snps[:, 100:200],
mixing=.5,
output_file_name=output_file_name,
cache_file=cache_file)
self.compare_files(frame, "G1")
frame2 = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=None,
covar=covar,
G1=None,
mixing=.5,
output_file_name=output_file_name,
cache_file=cache_file)
self.compare_files(frame2, "G1")
def test_match_cpp(self):
'''
match
FaSTLMM.207\Data\DemoData>..\.cd.\bin\windows\cpp_mkl\fastlmmc -bfile snps -extract topsnps.txt -bfileSim snps -extractSim ASout.snps.txt -pheno pheno.txt -covar covariate.txt -out topsnps.singlesnp.txt -logDelta 0 -verbose 100
'''
logging.info("TestSingleSnp test_match_cpp")
snps = Bed(os.path.join(self.pythonpath, "tests/datasets/selecttest/snps"), count_A1=False)
pheno = os.path.join(self.pythonpath, "tests/datasets/selecttest/pheno.txt")
covar = os.path.join(self.pythonpath, "tests/datasets/selecttest/covariate.txt")
sim_sid = ["snp26250_m0_.19m1_.19","snp82500_m0_.28m1_.28","snp63751_m0_.23m1_.23","snp48753_m0_.4m1_.4","snp45001_m0_.26m1_.26","snp52500_m0_.05m1_.05","snp75002_m0_.39m1_.39","snp41253_m0_.07m1_.07","snp11253_m0_.2m1_.2","snp86250_m0_.33m1_.33","snp3753_m0_.23m1_.23","snp75003_m0_.32m1_.32","snp30002_m0_.25m1_.25","snp26252_m0_.19m1_.19","snp67501_m0_.15m1_.15","snp63750_m0_.28m1_.28","snp30001_m0_.28m1_.28","snp52502_m0_.35m1_.35","snp33752_m0_.31m1_.31","snp37503_m0_.37m1_.37","snp15002_m0_.11m1_.11","snp3751_m0_.34m1_.34","snp7502_m0_.18m1_.18","snp52503_m0_.3m1_.3","snp30000_m0_.39m1_.39","isnp4457_m0_.11m1_.11","isnp23145_m0_.2m1_.2","snp60001_m0_.39m1_.39","snp33753_m0_.16m1_.16","isnp60813_m0_.2m1_.2","snp82502_m0_.34m1_.34","snp11252_m0_.13m1_.13"]
sim_idx = snps.sid_to_index(sim_sid)
test_sid = ["snp26250_m0_.19m1_.19","snp63751_m0_.23m1_.23","snp82500_m0_.28m1_.28","snp48753_m0_.4m1_.4","snp45001_m0_.26m1_.26","snp52500_m0_.05m1_.05","snp75002_m0_.39m1_.39","snp41253_m0_.07m1_.07","snp86250_m0_.33m1_.33","snp15002_m0_.11m1_.11","snp33752_m0_.31m1_.31","snp26252_m0_.19m1_.19","snp30001_m0_.28m1_.28","snp11253_m0_.2m1_.2","snp67501_m0_.15m1_.15","snp3753_m0_.23m1_.23","snp52502_m0_.35m1_.35","snp30000_m0_.39m1_.39","snp30002_m0_.25m1_.25"]
test_idx = snps.sid_to_index(test_sid)
for G0,G1 in [(snps[:,sim_idx],KernelIdentity(snps.iid)),(KernelIdentity(snps.iid),snps[:,sim_idx])]:
frame_h2 = single_snp(test_snps=snps[:,test_idx], pheno=pheno, G0=G0,G1=G1, covar=covar,h2=.5,leave_out_one_chrom=False,count_A1=False)
frame_log_delta = single_snp(test_snps=snps[:,test_idx], pheno=pheno, G0=G0,G1=G1, covar=covar,log_delta=0,leave_out_one_chrom=False,count_A1=False)
for frame in [frame_h2, frame_log_delta]:
referenceOutfile = TestFeatureSelection.reference_file("single_snp/topsnps.single.txt")
reference = pd.read_table(referenceOutfile,sep="\t") # We've manually remove all comments and blank lines from this file
assert len(frame) == len(reference)
for _, row in reference.iterrows():
sid = row.SNP
pvalue = frame[frame['SNP'] == sid].iloc[0].PValue
reldiff = abs(row.Pvalue - pvalue)/row.Pvalue
assert reldiff < .035, "'{0}' pvalue_list differ too much {4} -- {2} vs {3}".format(sid,None,row.Pvalue,pvalue,reldiff)
def execute_fs(test_snps, pheno, G0, covar):
"""
run feature selection
"""
result = {}
fs_result = {}
# fs unconditioned
########################
tmp_uuid = str(uuid.uuid4())[0:13]
out_fn = "tmp_pheno_%s.txt" % (tmp_uuid)
out_data = pd.DataFrame({
"id1": G0.iid[:, 0],
"id2": G0.iid[:, 1],
"y": pheno["vals"]
})
out_data.to_csv(out_fn, sep=" ", header=False, index=False)
# write out covariates
items = [
('id1', G0.iid[:, 0]),
('id2', G0.iid[:, 1]),
]
items += [("pc_%i" % i, covar["vals"][:, i])
for i in xrange(covar["vals"].shape[1])]
cov_df = pd.DataFrame.from_items(items)
cov_fn = "tmp_cov_%s.txt" % (tmp_uuid)
cov_df.to_csv(cov_fn, sep=" ", header=False, index=False)
#TODO: fix include_all!!
fsd = create_feature_selection_distributable(G0,
out_fn,
None,
0,
"fs_out",
include_all=False,
cov_fn=cov_fn)
fs_result["result_uncond_all"] = Local().run(fsd)
best_k, best_delta, best_obj, best_snps = fs_result["result_uncond_all"]
fs_idx = argintersect_left(G0.sid, best_snps)
G_fs = G0[:, fs_idx]
result["fs_all"] = single_snp(test_snps, pheno,
G0=G_fs).sort(["Chr", "ChrPos"
])["PValue"].as_matrix()
result["fs_all_cov"] = single_snp(
test_snps, pheno, G0=G_fs,
covar=covar).sort(["Chr", "ChrPos"])["PValue"].as_matrix()
return result, fs_result
def test_leave_one_out_with_prekernels(self):
logging.info(
"TestSingleSnpLeaveOutOneChrom test_leave_one_out_with_prekernels")
from pysnptools.kernelstandardizer import DiagKtoN
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
chrom_to_kernel = {}
with patch.dict('os.environ', {'ARRAY_MODULE': 'numpy'}) as _:
for chrom in np.unique(test_snps.pos[:, 0]):
other_snps = test_snps[:, test_snps.pos[:, 0] != chrom]
kernel = other_snps.read_kernel(
standardizer=Unit(), block_size=500
) #Create a kernel from the SNPs not used in testing
chrom_to_kernel[chrom] = kernel.standardize(
DiagKtoN()
) #improves the kernel numerically by making its diagonal sum to iid_count
output_file = self.file_name("one_looc_prekernel")
frame = single_snp(test_snps,
pheno,
covar=covar,
K0=chrom_to_kernel,
output_file_name=output_file,
count_A1=False)
self.compare_files(frame, "one_looc")
def execute_dual_fs(test_snps, pheno, G0, covar):
"""
implementation of dual-kernel feature selection
"""
result = {}
fs_result = {}
# extract data
G_test = test_snps.read().standardize().val
G_train_unnorm = G0.read().standardize().val
# fs conditioned on full kernel
select = FeatureSelectionInSample(max_log_k=7, order_by_lmm=True)
fs_result["insample_cond_full"] = select.run_select(G_train_unnorm,
G_train_unnorm,
pheno["vals"],
cov=covar["vals"])
best_k, fs_idx, best_mix, best_delta = fs_result["insample_cond_full"]
print "best_k:", best_k, ", best_mix:", best_mix
# set up foreground kernel
G1 = G0[:, fs_idx]
result["full_fs_low"] = single_snp(test_snps,
pheno,
G0=G0,
covar=covar,
G1=G1,
mixing=best_mix).sort(
["Chr",
"ChrPos"])["PValue"].as_matrix()
return result, fs_result
def test_linreg(self):
logging.info("TestSingleSnp test_linreg")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("linreg")
frame1 = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
mixing=0,
leave_out_one_chrom=False,
G0=KernelIdentity(iid=test_snps.iid),
covar=covar,
output_file_name=output_file)
frame1 = frame1[[
'sid_index', 'SNP', 'Chr', 'GenDist', 'ChrPos', 'PValue'
]]
self.compare_files(frame1, "linreg")
frame2 = single_snp_linreg(test_snps=test_snps[:, :10],
pheno=pheno,
covar=covar,
output_file_name=output_file)
self.compare_files(frame2, "linreg")
def test_single_snp(args):
import fastlmm
from pysnptools.snpreader import SnpData, Pheno, SnpReader
from fastlmm.association import single_snp
from utils import read_hdf5_dataset
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import fastlmm.util.util as flutil
logger.info('read phenotypes from file: ' + args.phenotype_file)
phenotypes = pd.read_table(args.phenotype_file)
iid = np.repeat(phenotypes['id'].values.astype('S')[:, np.newaxis],
2,
axis=1)
if args.sample_indices_file is not None:
logger.info('read indices from file: ' + args.sample_indices_file)
sample_indices = read_hdf5_dataset(args.sample_indices_file)
else:
sample_indices = np.nonzero(
(phenotypes['type'] == 'training').values)[0]
logger.info('read SNP file (for test): ' + args.snp_file)
test_snps = get_snpdata(iid, args.snp_file, sample_indices=sample_indices)
logger.info('read SNP file (for K0): ' + args.k0_file)
K0 = get_snpdata(iid, args.k0_file)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
df_pheno = phenotypes[phenotypes['type'] == 'training'].copy()
df_pheno['fid'] = df_pheno['id']
df_pheno['iid'] = df_pheno['id']
traits = ('trait1', 'trait2', 'trait3')
for trait in traits:
pheno_file = os.path.join(args.output_dir, 'pheno.%s.txt' % trait)
logger.info('create Pheno file: ' + pheno_file)
df_pheno[['fid', 'iid', trait]].to_csv(pheno_file,
index=False,
sep='\t',
header=False)
pheno = Pheno(pheno_file)
logger.info('run FastLMM for single SNP test for %s' % trait)
results_df = single_snp(test_snps,
pheno,
K0=K0,
count_A1=True,
GB_goal=args.GB_goal)
result_file = os.path.join(args.output_dir, 'single_snp.' + trait)
logger.info('save results to file: ' + result_file)
results_df.to_hdf(result_file, trait)
if args.manhattan:
plot_file = os.path.join(args.output_dir,
'manhattan.%s.pdf' % trait)
logger.info('create Manhattan plot: ' + plot_file)
plt.clf()
flutil.manhattan_plot(results_df.as_matrix(
["Chr", "ChrPos", "PValue"]),
pvalue_line=1e-5,
xaxis_unit_bp=False)
plt.savefig(plot_file)
def execute_dual_fs(test_snps, pheno, G0, covar):
"""
implementation of dual-kernel feature selection
"""
result = {}
fs_result = {}
# extract data
G_test = test_snps.read().standardize().val
G_train_unnorm = G0.read().standardize().val
# fs conditioned on full kernel
select = FeatureSelectionInSample(max_log_k=7, order_by_lmm=True)
fs_result["insample_cond_full"] = select.run_select(G_train_unnorm, G_train_unnorm, pheno["vals"], cov=covar["vals"])
best_k, fs_idx, best_mix, best_delta = fs_result["insample_cond_full"]
print "best_k:", best_k, ", best_mix:", best_mix
# set up foreground kernel
G1 = G0[:,fs_idx]
result["full_fs_low"] = single_snp(test_snps, pheno, G0=G0, covar=covar, G1=G1, mixing=best_mix).sort(["Chr", "ChrPos"])["PValue"].as_matrix()
return result, fs_result
def execute_lmm(test_snps, pheno, G0, covar):
result = {}
fs_result = {}
result["full"] = single_snp(test_snps, pheno, G0=G0, covar=covar).sort(["Chr", "ChrPos"])["PValue"].as_matrix()
return result, fs_result
def execute_lmm(test_snps, pheno, G0, covar):
result = {}
fs_result = {}
result["full"] = single_snp(test_snps, pheno, G0=G0,
covar=covar).sort(["Chr", "ChrPos"
])["PValue"].as_matrix()
return result, fs_result
def test_old(self):
logging.info("test_old")
output_file = self.file_name("old")
results_df = single_snp(self.bed,
self.phen_fn,
covar=self.cov_fn,
count_A1=True,
output_file_name=output_file)
self.compare_files(results_df, "old")
def test_G0_has_reader(self):
logging.info("TestSingleSnp test_G0_has_reader")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G0_has_reader")
frame0 = 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(frame0,"one")
frame1 = single_snp(test_snps=test_snps[:,:10], pheno=pheno, G0=KernelIdentity(test_snps.iid), G1=test_snps, leave_out_one_chrom=False,
covar=covar, mixing=1,
output_file_name=output_file_name,count_A1=False
)
self.compare_files(frame1,"one")
def test_no_cov(self):
logging.info("TestSingleSnp test_no_cov")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
output_file_name = self.file_name("no_cov")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, G0=test_snps, mixing=0,leave_out_one_chrom=False,
output_file_name=output_file_name,count_A1=False
)
self.compare_files(frame,"no_cov")
def mapper_single_snp_2K_given_chrom(test_chr):
logging.info("Working on chr={0}".format(test_chr))
test_snps_chrom = test_snps[:,test_snps.pos[:,0]==test_chr]
G_for_chrom = _K_per_chrom(G, test_chr, G.iid).snpreader
chrom_index = chrom_list.index(test_chr)
best_sid = chrom_index_to_best_sid[chrom_index]
K1 = G_for_chrom[:,G_for_chrom.sid_to_index(best_sid)]
result = single_snp(test_snps=test_snps_chrom, K0=G_for_chrom, K1=K1, pheno=pheno,
covar=covar, leave_out_one_chrom=False,
GB_goal=GB_goal, force_full_rank=force_full_rank, force_low_rank=force_low_rank,mixing=mixing,h2=h2)
return result
def test_gb_goal(self):
logging.info("TestSingleSnp test_gb_goal")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("gb_goal")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, mixing=0,leave_out_one_chrom=False,
G0=test_snps, covar=covar, GB_goal=0,
output_file_name=output_file,count_A1=False
)
self.compare_files(frame,"one")
output_file = self.file_name("gb_goal2")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, mixing=0,leave_out_one_chrom=False,
G0=test_snps, covar=covar, GB_goal=.12,
output_file_name=output_file,count_A1=False
)
self.compare_files(frame,"one")
def test_no_cov(self):
logging.info("TestSingleSnp test_no_cov")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
output_file_name = self.file_name("no_cov")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, G0=test_snps, mixing=0,
output_file_name=output_file_name
)
self.compare_files(frame,"no_cov")
def mapper_single_snp_2K_given_chrom(test_chr):
logging.info("Working on chr={0}".format(test_chr))
test_snps_chrom = test_snps[:,test_snps.pos[:,0]==test_chr]
G_for_chrom = _K_per_chrom(G, test_chr, G.iid).snpreader
chrom_index = chrom_list.index(test_chr)
best_sid = chrom_index_to_best_sid[chrom_index]
K1 = G_for_chrom[:,G_for_chrom.sid_to_index(best_sid)]
result = single_snp(test_snps=test_snps_chrom, K0=G_for_chrom, K1=K1, pheno=pheno,
covar=covar, leave_out_one_chrom=False,
GB_goal=GB_goal, force_full_rank=force_full_rank, force_low_rank=force_low_rank,mixing=mixing,h2=h2)
return result
def test_other(self):
logging.info("TestSingleSnp test_other")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("other")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,leave_out_one_chrom=False,
K1=test_snps, covar=covar,
output_file_name=output_file,count_A1=False
)
self.compare_files(frame,"one")
def test_interact(self):
logging.info("TestSingleSnp test_interact")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("interact")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, mixing=0,leave_out_one_chrom=False,
G0=test_snps, covar=covar, interact_with_snp=1,
output_file_name=output_file,count_A1=False
)
self.compare_files(frame,"interact")
def test_mixid(self):
logging.info("TestSingleSnp test_mixid")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("mixid")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,G0=test_snps[:,10:100], leave_out_one_chrom=False,
covar=covar, K1=KernelIdentity(test_snps.iid),mixing=.25,
output_file_name=output_file_name,count_A1=False
)
self.compare_files(frame,"mixid")
def test_mixingKs(self):
logging.info("TestSingleSnp test_mixingKs")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("mixingKs")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,K0=SnpKernel(test_snps[:,10:100],Unit()),leave_out_one_chrom=False,
covar=covar, K1=SnpKernel(test_snps[:,100:200],Unit()),mixing=None,
output_file_name=output_file_name,count_A1=False
)
self.compare_files(frame,"mixing")
def test_interact_looc(self):
logging.info("TestSingleSnpLeaveOutOneChrom test_interact_looc")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("interact_looc")
frame = single_snp(test_snps, pheno,
covar=covar, mixing=0, interact_with_snp=0,
output_file_name=output_file,count_A1=False
)
self.compare_files(frame,"interact_looc")
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 test_noK0(self):
logging.info("TestSingleSnp test_noK0")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("noK0")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, mixing=1,leave_out_one_chrom=False,
G1=test_snps, covar=covar,
output_file_name=output_file
)
self.compare_files(frame,"one")
def test_two_looc(self):
logging.info("TestSingleSnpLeaveOutOneChrom test_two_looc")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("two_looc")
frame = single_snp(test_snps[:,::10], pheno,
covar=covar,
output_file_name=output_file,count_A1=False
)
self.compare_files(frame,"two_looc")
def test_none(self):
logging.info("TestSingleSnp test_none")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("none")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, mixing=0,leave_out_one_chrom=False,
K0=KernelIdentity(test_snps.iid), covar=covar,
output_file_name=output_file
)
self.compare_files(frame,"none")
def test_one_looc(self):
logging.info("TestSingleSnpLeaveOutOneChrom test_one_looc")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("one_looc")
frame = single_snp(test_snps, pheno,
covar=covar, mixing=0,
output_file_name=output_file,
)
self.compare_files(frame,"one_looc")
def test_mixing(self):
logging.info("TestSingleSnp test_mixing")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("mixing")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,G0=test_snps[:,10:100], leave_out_one_chrom=False,
covar=covar, G1=test_snps[:,100:200],mixing=None,
output_file_name=output_file_name
)
self.compare_files(frame,"mixing")
def execute_fs(test_snps, pheno, G0, covar):
"""
run feature selection
"""
result = {}
fs_result = {}
# fs unconditioned
########################
tmp_uuid = str(uuid.uuid4())[0:13]
out_fn = "tmp_pheno_%s.txt" % (tmp_uuid)
out_data = pd.DataFrame({"id1": G0.iid[:,0], "id2": G0.iid[:,1], "y": pheno["vals"]})
out_data.to_csv(out_fn, sep=" ", header=False, index=False)
# write out covariates
items = [
('id1', G0.iid[:,0]),
('id2', G0.iid[:,1]),
]
items += [("pc_%i" % i, covar["vals"][:,i]) for i in xrange(covar["vals"].shape[1])]
cov_df = pd.DataFrame.from_items(items)
cov_fn = "tmp_cov_%s.txt" % (tmp_uuid)
cov_df.to_csv(cov_fn, sep=" ", header=False, index=False)
#TODO: fix include_all!!
fsd = create_feature_selection_distributable(G0, out_fn, None, 0, "fs_out", include_all=False, cov_fn=cov_fn)
fs_result["result_uncond_all"] = Local().run(fsd)
best_k, best_delta, best_obj, best_snps = fs_result["result_uncond_all"]
fs_idx = argintersect_left(G0.sid, best_snps)
G_fs = G0[:,fs_idx]
result["fs_all"] = single_snp(test_snps, pheno, G0=G_fs).sort(["Chr", "ChrPos"])["PValue"].as_matrix()
result["fs_all_cov"] = single_snp(test_snps, pheno, G0=G_fs, covar=covar).sort(["Chr", "ChrPos"])["PValue"].as_matrix()
return result, fs_result
def test_unknown_sid(self):
logging.info("TestSingleSnp test_unknown_sid")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
try:
frame = single_snp(test_snps=test_snps,G0=test_snps,pheno=pheno,leave_out_one_chrom=False,mixing=0,covar=covar,sid_list=['1_4','bogus sid','1_9'],count_A1=False)
failed = False
except:
failed = True
assert(failed)
def test_two_looc(self):
logging.info("TestSingleSnpLeaveOutOneChrom test_two_looc")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("two_looc")
frame = single_snp(test_snps[:, ::10],
pheno,
covar=covar,
output_file_name=output_file,
count_A1=False)
self.compare_files(frame, "two_looc")
def test_G0_has_reader(self):
logging.info("TestSingleSnp test_G0_has_reader")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G0_has_reader")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno, G0=test_snps,
covar=covar, mixing=0,
output_file_name=output_file_name
)
self.compare_files(frame,"one")
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 test_mixing(self):
logging.info("TestSingleSnp test_mixing")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("mixing")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,G0=test_snps[:,10:100],
covar=covar, G1=test_snps[:,100:200],mixing=None,
output_file_name=output_file_name
)
self.compare_files(frame,"mixing")
def test_no_cov(self):
logging.info("TestSingleSnp test_no_cov")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
output_file_name = self.file_name("no_cov")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps,
mixing=0,
leave_out_one_chrom=False,
output_file_name=output_file_name)
self.compare_files(frame, "no_cov")
def test_no_cov(self):
logging.info("TestSingleSnp test_no_cov")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
output_file_name = self.file_name("no_cov")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps,
mixing=0,
output_file_name=output_file_name)
self.compare_files(frame, "no_cov")
def mapper_gather_lots(i_fold_and_pair):
i_fold, (train_idx, test_idx) = i_fold_and_pair
logging.info("Working on GWAS_1K and k search, chrom={0}, i_fold={1}".format(test_chr, i_fold))
G_train = G_for_chrom[train_idx,:]
#Precompute whole x whole standardized on train
from fastlmm.association.single_snp import _internal_determine_block_size, _block_size_from_GB_goal
min_count = _internal_determine_block_size(G_for_chrom, None, None, force_full_rank, force_low_rank)
block_size = _block_size_from_GB_goal(GB_goal, G_for_chrom.iid_count, min_count)
K_whole_unittrain = _SnpWholeWithTrain(whole=G_for_chrom,train_idx=train_idx, standardizer=Unit(), block_size=block_size).read()
assert np.array_equal(K_whole_unittrain.iid,G_for_chrom.iid),"real assert"
K_train = K_whole_unittrain[train_idx]
single_snp_result = single_snp(test_snps=G_train, K0=K_train, pheno=pheno, #iid intersection means when can give the whole covariate and pheno
covar=covar, leave_out_one_chrom=False,
GB_goal=GB_goal, force_full_rank=force_full_rank, force_low_rank=force_low_rank, mixing=mixing, h2=h2)
is_all = (i_fold == n_folds) if n_folds > 1 else True
k_list_in = [0] + [int(k) for k in k_list if 0 < k and k < len(single_snp_result)]
if is_all:
top_snps = list(single_snp_result.SNP[:max_k])
else:
top_snps = None
if i_fold == n_folds:
k_index_to_nLL = None
else:
k_index_to_nLL = []
for k in k_list_in:
top_k = G_for_chrom[:,G_for_chrom.sid_to_index(single_snp_result.SNP[:k])]
logging.info("Working on chr={0}, i_fold={1}, and K_{2}".format(test_chr,i_fold,k))
top_k_train = top_k[train_idx,:] if k > 0 else None
fastlmm = FastLMM(force_full_rank=force_full_rank, force_low_rank=force_low_rank,GB_goal=GB_goal)
fastlmm.fit(K0_train=K_train, K1_train=top_k_train, X=covar, y=pheno,mixing=mixing,h2raw=h2) #iid intersection means when can give the whole covariate and pheno
top_k_test = top_k[test_idx,:] if k > 0 else None
K0_whole_test = K_whole_unittrain[:,test_idx]
nLL = fastlmm.score(K0_whole_test=K0_whole_test,K1_whole_test=top_k_test,X=covar,y=pheno) #iid intersection means when can give the whole covariate and pheno
k_index_to_nLL.append(nLL)
if i_fold > 0:
k_list_in = None
return k_list_in, top_snps, k_index_to_nLL
def mapper_gather_lots(i_fold_and_pair):
i_fold, (train_idx, test_idx) = i_fold_and_pair
logging.info("Working on GWAS_1K and k search, chrom={0}, i_fold={1}".format(test_chr, i_fold))
G_train = G_for_chrom[train_idx,:]
#Precompute whole x whole standardized on train
from fastlmm.association.single_snp import _internal_determine_block_size, _block_size_from_GB_goal
min_count = _internal_determine_block_size(G_for_chrom, None, None, force_full_rank, force_low_rank)
block_size = _block_size_from_GB_goal(GB_goal, G_for_chrom.iid_count, min_count)
K_whole_unittrain = _SnpWholeWithTrain(whole=G_for_chrom,train_idx=train_idx, standardizer=Unit(), block_size=block_size).read()
assert np.array_equal(K_whole_unittrain.iid,G_for_chrom.iid),"real assert"
K_train = K_whole_unittrain[train_idx]
single_snp_result = single_snp(test_snps=G_train, K0=K_train, pheno=pheno, #iid intersection means when can give the whole covariate and pheno
covar=covar, leave_out_one_chrom=False,
GB_goal=GB_goal, force_full_rank=force_full_rank, force_low_rank=force_low_rank, mixing=mixing, h2=h2)
is_all = (i_fold == n_folds) if n_folds > 1 else True
k_list_in = [0] + [int(k) for k in k_list if 0 < k and k < len(single_snp_result)]
if is_all:
top_snps = list(single_snp_result.SNP[:max_k])
else:
top_snps = None
if i_fold == n_folds:
k_index_to_nLL = None
else:
k_index_to_nLL = []
for k in k_list_in:
top_k = G_for_chrom[:,G_for_chrom.sid_to_index(single_snp_result.SNP[:k])]
logging.info("Working on chr={0}, i_fold={1}, and K_{2}".format(test_chr,i_fold,k))
top_k_train = top_k[train_idx,:] if k > 0 else None
fastlmm = FastLMM(force_full_rank=force_full_rank, force_low_rank=force_low_rank,GB_goal=GB_goal)
fastlmm.fit(K0_train=K_train, K1_train=top_k_train, X=covar, y=pheno,mixing=mixing,h2=h2) #iid intersection means when can give the whole covariate and pheno
top_k_test = top_k[test_idx,:] if k > 0 else None
K0_whole_test = K_whole_unittrain[:,test_idx]
nLL = fastlmm.score(K0_whole_test=K0_whole_test,K1_whole_test=top_k_test,X=covar,y=pheno) #iid intersection means when can give the whole covariate and pheno
k_index_to_nLL.append(nLL)
if i_fold > 0:
k_list_in = None
return k_list_in, top_snps, k_index_to_nLL
def test_file_cache(self):
logging.info("TestSingleSnp test_file_cache")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G1")
cache_file = self.file_name("cache_file")+".npz"
if os.path.exists(cache_file):
os.remove(cache_file)
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,G0=test_snps[:,10:100], leave_out_one_chrom=False,
covar=covar, G1=test_snps[:,100:200],
mixing=.5,
output_file_name=output_file_name,
cache_file = cache_file,count_A1=False
)
self.compare_files(frame,"G1")
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,G0=test_snps[:,10:100], leave_out_one_chrom=False,
covar=covar, G1=test_snps[:,100:200],
mixing=.5,
output_file_name=output_file_name,
cache_file = cache_file,count_A1=False
)
self.compare_files(frame,"G1")
def test_G1(self):
logging.info("TestSingleSnp test_G1")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G1")
for force_full_rank,force_low_rank in [(False,True),(False,False),(True,False)]:
logging.info("{0},{1}".format(force_full_rank,force_low_rank))
frame = single_snp(test_snps=test_snps[:,:10], pheno=pheno,G0=test_snps[:,10:100], leave_out_one_chrom=False,
covar=covar, G1=test_snps[:,100:200],
mixing=.5,force_full_rank=force_full_rank,force_low_rank=force_low_rank,
output_file_name=output_file_name,count_A1=False
)
self.compare_files(frame,"G1")
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_covar_by_chrom_mixing(self):
logging.info("TestSingleSnpLeaveOutOneChrom test_covar_by_chrom_mixing")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
covar = Pheno(self.cov_fn).read()
covar = SnpData(iid=covar.iid,sid=["pheno-1"],val=covar.val)
covar_by_chrom = {chrom:self.cov_fn for chrom in xrange(1,6)}
output_file = self.file_name("covar_by_chrom_mixing")
frame = single_snp(test_snps, pheno,
covar=covar,
covar_by_chrom=covar_by_chrom,
output_file_name=output_file,count_A1=False
)
self.compare_files(frame,"covar_by_chrom_mixing")
def test_other(self):
logging.info("TestSingleSnp test_other")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("other")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
leave_out_one_chrom=False,
K1=test_snps,
covar=covar,
output_file_name=output_file)
self.compare_files(frame, "one")
def test_interact_looc(self):
logging.info("TestSingleSnpLeaveOutOneChrom test_interact_looc")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("interact_looc")
frame = single_snp(test_snps,
pheno,
covar=covar,
mixing=0,
interact_with_snp=0,
output_file_name=output_file)
self.compare_files(frame, "interact_looc")
def test_old_one(self):
logging.info("test_old_one")
output_file = self.file_name("old_one")
test_snps3 = self.bed[:, self.bed.pos[:, 0] ==
3] # Test only on chromosome 3
results_df = single_snp(
test_snps=test_snps3,
K0=self.bed,
pheno=self.phen_fn,
covar=self.cov_fn,
count_A1=True,
output_file_name=output_file,
)
self.compare_files(results_df, "old_one")
def test_none(self):
logging.info("TestSingleSnp test_none")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("none")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
mixing=0,
leave_out_one_chrom=False,
K0=KernelIdentity(test_snps.iid),
covar=covar,
output_file_name=output_file)
self.compare_files(frame, "none")
def test_covar_by_chrom_mixing(self):
logging.info(
"TestSingleSnpLeaveOutOneChrom test_covar_by_chrom_mixing")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
covar = Pheno(self.cov_fn).read()
covar = SnpData(iid=covar.iid, sid=["pheno-1"], val=covar.val)
covar_by_chrom = {chrom: self.cov_fn for chrom in xrange(1, 6)}
output_file = self.file_name("covar_by_chrom_mixing")
frame = single_snp(test_snps,
pheno,
covar=covar,
covar_by_chrom=covar_by_chrom,
output_file_name=output_file)
self.compare_files(frame, "covar_by_chrom_mixing")
def test_G0_has_reader(self):
logging.info("TestSingleSnp test_G0_has_reader")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G0_has_reader")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps,
covar=covar,
mixing=0,
output_file_name=output_file_name)
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_noK0(self):
logging.info("TestSingleSnp test_noK0")
test_snps = Bed(self.bedbase, count_A1=False)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("noK0")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
mixing=1,
leave_out_one_chrom=False,
G1=test_snps,
covar=covar,
output_file_name=output_file,
count_A1=False)
self.compare_files(frame, "one")
def test_G1(self):
logging.info("TestSingleSnp test_G1")
from pysnptools.snpreader import Bed
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G1")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps[:, 10:100],
covar=covar,
G1=test_snps[:, 100:200],
mixing=.5,
output_file_name=output_file_name)
self.compare_files(frame, "G1")
def test_mixingKs(self):
logging.info("TestSingleSnp test_mixingKs")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("mixingKs")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
K0=SnpKernel(test_snps[:, 10:100], Unit()),
leave_out_one_chrom=False,
covar=covar,
K1=SnpKernel(test_snps[:, 100:200], Unit()),
mixing=None,
output_file_name=output_file_name)
self.compare_files(frame, "mixing")
def test_mixid(self):
logging.info("TestSingleSnp test_mixid")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("mixid")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps[:, 10:100],
leave_out_one_chrom=False,
covar=covar,
K1=KernelIdentity(test_snps.iid),
mixing=.25,
output_file_name=output_file_name)
self.compare_files(frame, "mixid")
def test_interact(self):
logging.info("TestSingleSnp test_interact")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file = self.file_name("interact")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
mixing=0,
leave_out_one_chrom=False,
G0=test_snps,
covar=covar,
interact_with_snp=1,
output_file_name=output_file)
self.compare_files(frame, "interact")
def test_G1_mixing(self):
logging.info("TestSingleSnp test_G1_mixing")
test_snps = Bed(self.bedbase)
pheno = self.phen_fn
covar = self.cov_fn
output_file_name = self.file_name("G1_mixing")
frame = single_snp(test_snps=test_snps[:, :10],
pheno=pheno,
G0=test_snps,
leave_out_one_chrom=False,
covar=covar,
G1=test_snps[:, 100:200],
mixing=0,
output_file_name=output_file_name)
self.compare_files(frame, "one")
def epi_reml(pair_snps,
pheno,
covar=None,
kernel_snps=None,
output_dir='results',
part_count=33,
runner=None,
override=False):
from pysnptools.kernelreader import SnpKernel
from pysnptools.standardizer import Unit
import datetime
from fastlmm.association import single_snp
part_list = list(split_on_sids(pair_snps, part_count))
part_pair_count = (part_count * part_count + part_count) / 2
part_pair_index = -1
print("part_pair_count={0:,}".format(part_pair_count))
K0 = SnpKernel(kernel_snps or pair_snps,
standardizer=Unit()).read() #Precompute the similarity
if not os.path.exists(output_dir): os.makedirs(output_dir)
start_time = datetime.datetime.now()
for i in range(part_count):
part_i = part_list[i]
for j in range(i, part_count):
part_pair_index += 1
pairs = _Pairs2(part_i) if i == j else _Pairs2(
part_i, part_list[j])
print("Looking at pair {0},{1} which is {2} of {3}".format(
i, j, part_pair_index, part_pair_count))
output_file = '{0}/result.{1}.{2}.tsv'.format(
output_dir, part_pair_index, part_pair_count)
if override or not os.path.exists(output_file):
result_df_ij = single_snp(pairs,
K0=K0,
pheno=pheno,
covar=covar,
leave_out_one_chrom=False,
count_A1=True,
runner=runner)
result_df_ij.to_csv(output_file, sep="\t", index=False)
print(result_df_ij[:1])
time_so_far = datetime.datetime.now() - start_time
total_time_estimate = time_so_far * part_pair_count / (
part_pair_index + 1)
print(total_time_estimate)