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 main(args):
print('reading seeed snps')
seed_snps = pd.read_csv(args.seed_snps, header=None, names=['SNP'], index_col='SNP')
seed_snps['ibs_length'] = 0
seed_snps['ibd'] = 0
print('reading typed snps')
typed_snps = pd.read_csv(args.typed_snps, header=None, names=['SNP'])
print('reading genotypes')
data = Bed(args.bfile)
X = data.read().val
typed_snps_indices = np.sort(data.sid_to_index(typed_snps.SNP))
typed_snps_bp = data.col_property[typed_snps_indices,2]
print(len(seed_snps), 'snps in list')
print(data.iid_count, data.sid_count, 'are dimensions of X')
def analyze_snp(i):
# find first typed snp after query snp
snp_bp = data.col_property[i,2]
v = np.where(typed_snps_bp > snp_bp)[0]
if len(v) > 0:
typed_i = v[0]
else:
typed_i = len(typed_snps_indices)-1
n1, n2 = np.where(X[:,i] == 1)[0]
if (X[n1,typed_snps_indices[typed_i]] - X[n2, typed_snps_indices[typed_i]])**2 == 4:
return 0, 0
typed_il, typed_ir = fis.find_boundaries(
X[n1,typed_snps_indices],
X[n2,typed_snps_indices],
typed_i)
typed_ir -= 1
il = typed_snps_indices[typed_il]
ir = typed_snps_indices[typed_ir]
cM = data.col_property[ir, 1] - \
data.col_property[il, 1]
ibd = (np.mean(X[n1,il:ir] == X[n2,il:ir]) > 0.99)
return cM, int(ibd)
for (i, snp) in iter.show_progress(
it.izip(data.sid_to_index(seed_snps.index), seed_snps.index),
total=len(seed_snps)):
# total=10):
seed_snps.ix[snp, ['ibs_length', 'ibd']] = analyze_snp(i)
print(seed_snps.iloc[:100])
seed_snps.to_csv(args.outfile, sep='\t')
def __init__(self,args):
if args.window_type not in ['BP','SNP']:
raise ValueError('Window type not supported')
bed_1 = Bed(args.bfile) #
af1 = self.get_allele_frequency(bed_1,args) #
print(len(af1), "SNPs in file 1")
snps_1 = (af1>args.maf)&(af1<1-args.maf) #
print(np.sum(snps_1), "SNPs in file 1 after MAF filter")
if (args.from_bp is not None) and (args.to_bp is not None):
k = (bed_1.pos[:,2]>args.from_bp)&(bed_1.pos[:,2]<args.to_bp)
snps_1 = snps_1&k
snps_to_use = bed_1.sid[snps_1]
if args.extract is not None:
keep = np.array([l.strip() for l in open(args.extract,'r')])
snps_to_use = np.intersect1d(snps_to_use,keep)
print(len(snps_to_use),"SNPs remaining after extraction")
bed_1_index = np.sort(bed_1.sid_to_index(snps_to_use)) #
pos = bed_1.pos[bed_1_index] #
bim_1=pd.read_table(bed_1.filename+'.bim',header=None,
names=['chm','id','pos_mb','pos_bp','a1','a2'])
af = af1[bed_1_index] #
if args.afile is not None:
a1 = pd.read_table(args.afile,header=None,sep='\s*',
names=['id1','id2','theta'])
else:
a1 = None
self.af = af
self.M = len(bed_1_index) #
self.windows = self.get_windows(pos,args) #
self.chr = pos[:,0]
self.pos = pos[:,2]
self.id = bed_1.sid[bed_1_index]
self.A1 = bim_1['a1'].loc[bed_1_index]
self.A2 = bim_1['a2'].loc[bed_1_index]
self.scores = self.compute(bed_1,bed_1_index,af,a1,args) #
def __init__(self,args):
if args.window_type not in ['KBP','SNP']:
raise ValueError('Window type not supported')
bed_1 = Bed(args.bfile,count_A1=False) #
af1 = self.get_allele_frequency(bed_1,args) #
print(len(af1), "SNPs in file 1")
snps_1 = (af1>args.maf)&(af1<1-args.maf) #
print(np.sum(snps_1), "SNPs in file 1 after MAF filter")
if (args.from_bp is not None) and (args.to_bp is not None):
k = (bed_1.pos[:,2]>args.from_bp)&(bed_1.pos[:,2]<args.to_bp)
snps_1 = snps_1&k
snps_to_use = bed_1.sid[snps_1]
if args.extract is not None:
keep = np.array([l.strip() for l in open(args.extract,'r')])
snps_to_use = np.intersect1d(snps_to_use,keep)
print(len(snps_to_use),"SNPs remaining after extraction")
bed_1_index = np.sort(bed_1.sid_to_index(snps_to_use)) #
pos = bed_1.pos[bed_1_index] #
bim_1=pd.read_table(bed_1.filename+'.bim',header=None,
names=['chm','id','pos_mb','pos_bp','a1','a2'])
af = af1[bed_1_index] #
# if args.afile is not None:
# a1 = pd.read_table(args.afile,header=None,sep='\s*',
# names=['id1','id2','theta'])
# else:
a1 = None
self.af = af
self.M = len(bed_1_index) #
self.windows = self.get_windows(pos,args) #
self.chr = pos[:,0]
self.pos = pos[:,2]
self.id = bed_1.sid[bed_1_index]
self.A1 = bim_1['a1'].loc[bed_1_index]
self.A2 = bim_1['a2'].loc[bed_1_index]
self.scores = self.compute(bed_1,bed_1_index,af,a1,args) #
def __init__(self, args):
if args.window_type not in ['KBP', 'SNP']:
raise ValueError('Window type not supported')
bed_1 = Bed(args.bfile, count_A1=False) #
af1 = self.get_allele_frequency(bed_1, args) #
print(len(af1), "SNPs in file 1")
snps_1 = (af1 > args.maf) & (af1 < 1 - args.maf) #
print(np.sum(snps_1), "SNPs in file 1 after MAF filter")
# Omit SNPs with NA values for h2weight
if args.h2weight:
data = pd.read_table(args.bfile + '.h2weight',
header=None,
names=['SNP', 'h2weight'],
index_col=False)
if (len(data['SNP']) != len(bed_1.sid)
or (data['SNP'] == bed_1.sid).min() == False):
raise ValueError(
'SNPs disagree between bed/bim/fam and h2weight files')
h2weight = data['h2weight']
snps_1 = snps_1 & ~h2weight.isnull().values
print(np.sum(snps_1),
"SNPs in file 1 after extracting non-NA h2weight")
del (data)
if (args.from_bp is not None) and (args.to_bp is not None):
k = (bed_1.pos[:, 2] > args.from_bp) & (bed_1.pos[:, 2] <
args.to_bp)
snps_1 = snps_1 & k
snps_to_use = bed_1.sid[snps_1]
if args.extract is not None:
keep = np.array([l.strip() for l in open(args.extract, 'r')])
snps_to_use = np.intersect1d(snps_to_use, keep)
print(len(snps_to_use), "SNPs remaining after extraction")
bed_1_index = np.sort(bed_1.sid_to_index(snps_to_use)) #
pos = bed_1.pos[bed_1_index] #
bim_1 = pd.read_table(
bed_1.filename + '.bim',
header=None,
names=['chm', 'id', 'pos_mb', 'pos_bp', 'a1', 'a2'])
af = af1[bed_1_index] #
# if args.afile is not None:
# a1 = pd.read_table(args.afile,header=None,sep='\s*',
# names=['id1','id2','theta'])
# else:
a1 = None
try:
h2weight = h2weight[bed_1_index].values
except NameError:
h2weight = None
self.af = af
self.M = len(bed_1_index) #
self.windows = self.get_windows(pos, args) #
self.chr = pos[:, 0]
self.pos = pos[:, 2]
self.id = bed_1.sid[bed_1_index]
self.A1 = bim_1['a1'].loc[bed_1_index]
self.A2 = bim_1['a2'].loc[bed_1_index]
self.scores = self.compute(bed_1, bed_1_index, af, a1, h2weight,
args) #
def test_match_cpp(self):
'''
match
FaSTLMM.207\Data\DemoData>fastlmmc -snpPairs -bfile snps -extract topsnps.txt -bfileSim snps -extractSim ASout.snps.txt -pheno pheno.txt -covar covariate.txt -out topsnps.pairs.txt -logDelta 0 -verbose 100
'''
logging.info("TestEpistasis test_match_cpp")
from pysnptools.snpreader import Bed
snps = Bed(os.path.join(self.pythonpath, "tests/datasets/selecttest/snps"))
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)
frame = epistasis(snps[:,test_idx], pheno,covar=covar, G0 = snps[:,sim_idx],log_delta=0)
sid0,sid1,pvalue_list =np.array(frame['SNP0']),np.array(frame['SNP1']),np.array(frame['PValue'])
referenceOutfile = TestFeatureSelection.reference_file("epistasis/topsnps.pairs.txt")
import pandas as pd
table = pd.read_table(referenceOutfile,sep="\t") # We've manually remove all comments and blank lines from this file
assert len(pvalue_list) == len(table)
for row in table.iterrows():
snp0cpp,snp1cpp,pvaluecpp,i1,i2 = row[1]
for i in xrange(len(pvalue_list)):
found = False
pvaluepy = pvalue_list[i]
snp0py = sid0[i]
snp1py = sid1[i]
if (snp0py == snp0cpp and snp1py == snp1cpp) or (snp0py == snp1cpp and snp1py == snp0cpp):
found = True
diff = abs(pvaluecpp - pvaluepy)/pvaluecpp
assert diff < .035, "'{0}' '{1}' pvalue_list differ too much {4} -- {2} vs {3}".format(snp0cpp,snp1cpp,pvaluecpp,pvaluepy,diff)
break
assert found
class _Epistasis(object) : #implements IDistributable
def __init__(self,test_snps,pheno,G0, G1=None, mixing=0.0, covar=None,sid_list_0=None,sid_list_1=None,
log_delta=None, min_log_delta=-5, max_log_delta=10, output_file=None, cache_file=None):
self.test_snps = test_snps
self.pheno = pheno
self.output_file_or_none = output_file
self.cache_file = cache_file
self.covar = covar
self.sid_list_0 = sid_list_0
self.sid_list_1 = sid_list_1
self.G0=G0
self.G1_or_none=G1
self.mixing=mixing
self.external_log_delta=log_delta
self.min_log_delta = min_log_delta
self.max_log_delta = max_log_delta
self._ran_once = False
self._str = "{0}({1},{2},G0={6},G1={7},mixing={8},covar={3},output_file={12},sid_list_0={4},sid_list_1{5},log_delta={9},min_log_delta={10},max_log_delta={11},cache_file={13})".format(
self.__class__.__name__, self.test_snps,self.pheno,self.covar,self.sid_list_0,self.sid_list_1,
self.G0, self.G1_or_none, self.mixing, self.external_log_delta, self.min_log_delta, self.max_log_delta, output_file, cache_file)
self.block_size = 1000
def set_sid_sets(self):
sid_set_0 = set(self.sid_list_0)
self.intersect = sid_set_0.intersection(self.sid_list_1)
self.just_sid_0 = sid_set_0.difference(self.intersect)
self.just_sid_1 = self.intersect.symmetric_difference(self.sid_list_1)
self._pair_count = len(self.just_sid_0)*len(self.intersect) + len(self.just_sid_0)*len(self.just_sid_1) + len(self.intersect)*len(self.just_sid_1) + len(self.intersect) * (len(self.intersect)-1)//2
self.test_snps, self.pheno, self.covar, self.G0, self.G1_or_none = pstutil.intersect_apply([self.test_snps, self.pheno, self.covar, self.G0, self.G1_or_none]) #should put G0 and G1 first
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
#start of IDistributable interface--------------------------------------
@property
def work_count(self):
self._run_once()
block_count = self.div_ceil(self._pair_count, self.block_size)
return block_count
def work_sequence(self):
self._run_once()
return self.work_sequence_range(0,self.work_count)
def work_sequence_range(self, start, end):
self._run_once()
lmm = self.lmm_from_cache_file()
lmm.sety(self.pheno['vals'])
for sid0_list, sid1_list in self.pair_block_sequence_range(start,end):
yield lambda lmm=lmm,sid0_list=sid0_list,sid1_list=sid1_list : self.do_work(lmm,sid0_list,sid1_list) # the 'lmm=lmm,...' is need to get around a strangeness in Python
def reduce(self, result_sequence):
#doesn't need "run_once()"
frame = pd.concat(result_sequence)
frame.sort("PValue", inplace=True)
frame.index = np.arange(len(frame))
if self.output_file_or_none is not None:
frame.to_csv(self.output_file_or_none, sep="\t", index=False)
return frame
#!!Find a place to output info like this near the end of the run
#logging.info("PhenotypeName\t{0}".format(pheno['header']))
#logging.info("SampleSize\t{0}".format(test_snps.iid_count))
#logging.info("SNPCount\t{0}".format(test_snps.sid_count))
#logging.info("Runtime\t{0}".format(time.time()-t0))
@property
def tempdirectory(self):
self._run_once()
return self.__tempdirectory
#optional override -- the str name of the instance is used by the cluster as the job name
def __str__(self):
#Doesn't need run_once
return self._str
def copyinputs(self, copier):
self._run_once()
if isinstance(self.test_snps, str):
copier.input(self.test_snps + ".bed")
copier.input(self.test_snps + ".bim")
copier.input(self.test_snps + ".fam")
else:
copier.input(self.test_snps)
copier.input(self.pheno)
copier.input(self.covar)
if isinstance(self.G0, str):
copier.input(self.G0 + ".bed")
copier.input(self.G0 + ".bim")
copier.input(self.G0 + ".fam")
else:
copier.input(self.G0)
copier.input(self.G1_or_none)
copier.input(self.cache_file)
def copyoutputs(self,copier):
#Doesn't need run_once
copier.output(self.output_file_or_none)
#end of IDistributable interface---------------------------------------
@staticmethod
def div_ceil(num, den): #!!move to utils?
return -(-num//den) #The -/- trick makes it do ceiling instead of floor. "//" will do integer division even in the future and on floats.
def pair_block_sequence_range(self,block_start,block_end):
self._run_once()
assert 0 <= block_start and block_start <= block_end and block_end <= self.work_count, "real assert"
block_index = block_start
start = block_index * self.pair_count // self.work_count
next_start = (block_index+1) * self.pair_count // self.work_count
size_goal = next_start - start
end = block_end * self.pair_count // self.work_count
sid0_list = []
sid1_list = []
for sid0, sid1 in self.pair_sequence_range(start,end):
sid0_list.append(sid0)
sid1_list.append(sid1)
if len(sid0_list) == size_goal:
yield sid0_list, sid1_list
block_index += 1
if block_index == block_end:
return
sid0_list = []
sid1_list = []
start = next_start
next_start = (block_index+1) * self.pair_count // self.work_count
size_goal = next_start - start
assert len(sid0_list) == 0, "real assert"
#If start == end, then returns without yielding anything
def pair_sequence_range(self,start,end):
self._run_once()
assert 0 <= start and start <= end and end <= self._pair_count, "real assert"
i = start
for sid0, sid1 in self.pair_sequence_with_start(start):
yield sid0, sid1
i = i + 1
if i == end:
break
assert i == end, "Not enough items found. Didn't get to the end"
def pair_sequence_with_start(self,start):
self._run_once()
skip_ref = [start]
just_sid_0_list = list(self.just_sid_0)
just_sid_1_list = list(self.just_sid_1)
intersect_list = list(self.intersect)
for sid0, sid1 in self.combo_distinct(just_sid_0_list, intersect_list, skip_ref):
yield sid0, sid1
for sid0, sid1 in self.combo_distinct(just_sid_0_list, just_sid_1_list, skip_ref):
yield sid0, sid1
for sid0, sid1 in self.combo_distinct(intersect_list, just_sid_1_list, skip_ref):
yield sid0, sid1
for sid0, sid1 in self.combo_same(intersect_list, skip_ref):
yield sid0, sid1
assert skip_ref[0] == 0, "real assert"
def combo_distinct(self, distinct__list0, distinct__list1, skip_ref):
row_count = len(distinct__list0)
col_count = len(distinct__list1)
if skip_ref[0] >= row_count * col_count:
skip_ref[0] = skip_ref[0] - row_count * col_count
assert skip_ref[0] >=0, "real assert"
return
row_start = skip_ref[0] // col_count
skip_ref[0] = skip_ref[0] - row_start * col_count
assert skip_ref[0] >=0, "real assert"
for row_index in xrange(row_start, row_count):
sid0 = distinct__list0[row_index]
if row_index == row_start:
col_start = skip_ref[0]
skip_ref[0] = 0
else:
col_start = 0
for col_index in xrange(col_start, col_count):
sid1 = distinct__list1[col_index]
yield sid0, sid1
def combo_same(self, list, skip_ref):
count = len(list)
full_size = count * (count + 1) // 2
if skip_ref[0] >= full_size:
skip_ref[0] = skip_ref[0] - full_size
assert skip_ref[0] >=0, "real assert"
return
row_start = int((-1 + 2*count - np.sqrt(1 - 4*count + 4*count**2 - 8*skip_ref[0]))/2)
skip_ref[0] = skip_ref[0] - (count*row_start - (row_start*(1 + row_start))//2)
assert skip_ref[0] >=0, "real assert"
for row_index in xrange(row_start, count):
sid0 = list[row_index]
if row_index == row_start:
col_start = skip_ref[0]
skip_ref[0] = 0
else:
col_start = 0
for col_index in xrange(col_start + 1 + row_index, count):
sid1 = list[col_index]
assert sid0 is not sid1, "real assert"
yield sid0, sid1
@property
def pair_count(self):
self._run_once()
return self._pair_count
def lmm_from_cache_file(self):
logging.info("Loading precomputation from {0}".format(self.cache_file))
lmm = LMM()
with np.load(self.cache_file) as data:
lmm.U = data['arr_0']
lmm.S = data['arr_1']
return lmm
def fill_in_cache_file(self):
self._run_once()
logging.info("filling in the cache_file and log_delta, as needed")
if self.G1_or_none is None:
self.G1val_or_none = None
else:
self.G1val_or_none = self.G1_or_none.read().val
# The S and U are always cached, in case they are needed for the cluster or for multi-threaded runs
if self.cache_file is None:
self.cache_file = os.path.join(self.__tempdirectory, "cache_file.npz")
if os.path.exists(self.cache_file): # If there is already a cache file in the temp directory, it must be removed because it might be out-of-date
os.remove(self.cache_file)
lmm = None
if not os.path.exists(self.cache_file):
logging.info("Precomputing eigen")
lmm = LMM()
G0_standardized = self.G0.read().standardize()
lmm.setG(G0_standardized.val, self.G1val_or_none, a2=self.mixing)
logging.info("Saving precomputation to {0}".format(self.cache_file))
util.create_directory_if_necessary(self.cache_file)
np.savez(self.cache_file, lmm.U,lmm.S) #using np.savez instead of pickle because it seems to be faster to read and write
if self.external_log_delta is None:
if lmm is None:
lmm = self.lmm_from_cache_file()
logging.info("searching for internal delta")
lmm.setX(self.covar)
lmm.sety(self.pheno['vals'])
#log delta is used here. Might be better to use findH2, but if so will need to normalized G so that its K's diagonal would sum to iid_count
result = lmm.find_log_delta(REML=False, sid_count=self.G0.sid_count, min_log_delta=self.min_log_delta, max_log_delta=self.max_log_delta ) #!!what about findA2H2? minH2=0.00001
self.external_log_delta = result['log_delta']
self.internal_delta = np.exp(self.external_log_delta) * self.G0.sid_count
logging.info("internal_delta={0}".format(self.internal_delta))
logging.info("external_log_delta={0}".format(self.external_log_delta))
do_pair_count = 0
do_pair_time = time.time()
def do_work(self, lmm, sid0_list, sid1_list):
dataframe = pd.DataFrame(
index=np.arange(len(sid0_list)),
columns=('SNP0', 'Chr0', 'GenDist0', 'ChrPos0', 'SNP1', 'Chr1', 'GenDist1', 'ChrPos1', 'PValue', 'NullLogLike', 'AltLogLike')
)
#!!Is this the only way to set types in a dataframe?
dataframe['Chr0'] = dataframe['Chr0'].astype(np.float)
dataframe['GenDist0'] = dataframe['GenDist0'].astype(np.float)
dataframe['ChrPos0'] = dataframe['ChrPos0'].astype(np.float)
dataframe['Chr1'] = dataframe['Chr1'].astype(np.float)
dataframe['GenDist1'] = dataframe['GenDist1'].astype(np.float)
dataframe['ChrPos1'] = dataframe['ChrPos1'].astype(np.float)
dataframe['PValue'] = dataframe['PValue'].astype(np.float)
dataframe['NullLogLike'] = dataframe['NullLogLike'].astype(np.float)
dataframe['AltLogLike'] = dataframe['AltLogLike'].astype(np.float)
#This is some of the code for a different way that reads and dot-products 50% more, but does less copying. Seems about the same speed
#sid0_index_list = self.test_snps.sid_to_index(sid0_list)
#sid1_index_list = self.test_snps.sid_to_index(sid1_list)
#sid_index_union_dict = {}
#sid0_index_index_list = self.create_index_index(sid_index_union_dict, sid0_index_list)
#sid1_index_index_list = self.create_index_index(sid_index_union_dict, sid1_index_list)
#snps0_read = self.test_snps[:,sid0_index_list].read().standardize()
#snps1_read = self.test_snps[:,sid1_index_list].read().standardize()
sid_union = set(sid0_list).union(sid1_list)
sid_union_index_list = sorted(self.test_snps.sid_to_index(sid_union))
snps_read = self.test_snps[:,sid_union_index_list].read().standardize()
sid0_index_list = snps_read.sid_to_index(sid0_list)
sid1_index_list = snps_read.sid_to_index(sid1_list)
products = snps_read.val[:,sid0_index_list] * snps_read.val[:,sid1_index_list] # in the products matrix, each column i is the elementwise product of sid i in each list
X = np.hstack((self.covar, snps_read.val, products))
UX = lmm.U.T.dot(X)
k = lmm.S.shape[0]
N = X.shape[0]
if (k<N):
UUX = X - lmm.U.dot(UX)
else:
UUX = None
for pair_index, sid0 in enumerate(sid0_list):
sid1 = sid1_list[pair_index]
sid0_index = sid0_index_list[pair_index]
sid1_index = sid1_index_list[pair_index]
index_list = np.array([pair_index]) #index to product
index_list = index_list + len(sid_union_index_list) #Shift by the number of snps in the union
index_list = np.hstack((np.array([sid0_index,sid1_index]),index_list)) # index to sid0 and sid1
index_list = index_list + self.covar.shape[1] #Shift by the number of values in the covar
index_list = np.hstack((np.arange(self.covar.shape[1]),index_list)) #indexes of the covar
index_list_less_product = index_list[:-1] #index to everything but the product
#Null -- the two additive SNPs
lmm.X = X[:,index_list_less_product]
lmm.UX = UX[:,index_list_less_product]
if (k<N):
lmm.UUX = UUX[:,index_list_less_product]
else:
lmm.UUX = None
res_null = lmm.nLLeval(delta=self.internal_delta, REML=False)
ll_null = -res_null["nLL"]
#Alt -- now with the product feature
lmm.X = X[:,index_list]
lmm.UX = UX[:,index_list]
if (k<N):
lmm.UUX = UUX[:,index_list]
else:
lmm.UUX = None
res_alt = lmm.nLLeval(delta=self.internal_delta, REML=False)
ll_alt = -res_alt["nLL"]
test_statistic = ll_alt - ll_null
degrees_of_freedom = 1
pvalue = stats.chi2.sf(2.0 * test_statistic, degrees_of_freedom)
logging.debug("<{0},{1}>, null={2}, alt={3}, pvalue={4}".format(sid0,sid1,ll_null,ll_alt,pvalue))
dataframe.iloc[pair_index] = [
sid0, snps_read.pos[sid0_index,0], snps_read.pos[sid0_index,1], snps_read.pos[sid0_index,2],
sid1, snps_read.pos[sid1_index,0], snps_read.pos[sid1_index,1], snps_read.pos[sid1_index,2],
pvalue, ll_null, ll_alt]
self.do_pair_count += 1
if self.do_pair_count % 100 == 0:
start = self.do_pair_time
self.do_pair_time = time.time()
logging.info("do_pair_count={0}, time={1}".format(self.do_pair_count,self.do_pair_time-start))
return dataframe
def __init__(self,args):
if args.window_type not in ['BP','SNP']:
raise ValueError('Window type not supported')
bed_1 = Bed(args.bfile1) #
bed_2 = Bed(args.bfile2)
af1 = self.get_allele_frequency(bed_1,args) #
af2 = self.get_allele_frequency(bed_2,args)
print(len(af1), "SNPs in file 1")
print(len(af2), "SNPs in file 2")
snps_1 = (af1>args.maf)&(af1<1-args.maf) #
snps_2 = (af2>args.maf)&(af2<1-args.maf)
print(np.sum(snps_1), "SNPs in file 1 after MAF filter")
print(np.sum(snps_2), "SNPs in file 2 after MAF filter")
if (args.from_bp is not None) and (args.to_bp is not None):
k1 = (bed_1.pos[:,2]>args.from_bp)&(bed_1.pos[:,2]<args.to_bp)
k2 = (bed_2.pos[:,2]>args.from_bp)&(bed_2.pos[:,2]<args.to_bp)
snps_1 = snps_1&k1
snps_2 = snps_2&k2
snps_to_use = np.intersect1d(bed_1.sid[snps_1],bed_2.sid[snps_2])
print(len(snps_to_use),"SNPs common in both populations")
if args.extract is not None:
keep = np.array([l.strip() for l in open(args.extract,'r')])
print(len(keep),"SNPs to extract")
snps_to_use = np.intersect1d(snps_to_use,keep)
print(len(snps_to_use),"SNPs remaining after extraction")
bed_1_index = np.sort(bed_1.sid_to_index(snps_to_use)) #
bed_2_index = np.sort(bed_2.sid_to_index(snps_to_use))
if not args.no_align:
alignment,bed_1_index,bed_2_index =\
self.align_alleles(bed_1,bed_1_index,af1,bed_2,bed_2_index,af2)
else:
alignment = np.ones(len(bed_1_index))
pos = bed_1.pos[bed_1_index] #
bim_1=pd.read_table(bed_1.filename+'.bim',header=None,
names=['chm','id','pos_mb','pos_bp','a1','a2'])
af1 = af1[bed_1_index] #
af2 = af2[bed_2_index]
if args.afile1 is not None:
a1 = pd.read_table(args.afile,header=None,sep='\s*',
names=['id1','id2','theta'])
else:
a1 = None
if args.afile2 is not None:
a2 = pd.read_table(args.afile,header=None,sep='\s*',
names=['id1','id2','theta'])
else:
a2 = None
self.af1 = af1 #
self.af2 = af2
self.M = len(bed_1_index) #
self.N = (bed_1.iid_count, bed_2.iid_count) #
self.chr = pos[:,0]
self.pos = pos[:,2]
self.id = bed_1.sid[bed_1_index]
self.A1 = bim_1['a1'].loc[bed_1_index]
self.A2 = bim_1['a2'].loc[bed_1_index]
self.windows = self.get_windows(pos,args) #
self.scores1 = self.compute(bed_1,bed_1_index,af1,a1,args)
self.scores2 = self.compute(bed_2,bed_2_index,af2,a2,args) #
self.scoresX = self.compute2(bed_1,bed_1_index,bed_2,bed_2_index,
alignment,a1,a2,args) #
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"))
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)
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)
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 unbalance_condition_longwas_trans(
data_file,
id,
tpoint,
trait,
bed_file,
kin_file,
var_com,
condition_snp,
snp_lst=None,
tfix=None,
fix=None,
forder=3,
aorder=3,
porder=3,
na_method='omit',
prefix_outfile='unbalance_condition_longwas_trans'):
"""
the longitudinal GWAS for the unbalanced data treating the SNP as the time varied random effect.
:param data_file: the data file. The first row is the variate names whose first initial position is alphabetical.
For the class variates, the first letter must be capital; for the covariates (continuous variates), the first letter
must be lowercase.
:param id: A class variate name which indicates the individual id column in the data file.
:param tpoint: A covariate names which indicates the time point column in the data file.
:param trait: A variate name which indicates the analyzed trait column in the data file.
:param bed_file: the prefix for the plink binary file.
:param kin_file: the file for genomic relationship matrix. This file can be produced by
gmat.gmatrix.agmat function using agmat(bed_file, inv=True, small_val=0.001, out_fmt='id_id_val')
:param var_com: the estimated variance parameters by the gmat.longwas.unbalance.unbalance_varcom function.
:param condition_snp: conditional snp
:param snp_lst: the snp list to test. Default is None.
:param tfix: A class variate name for the time varied fixed effect. Default value is None. Only one time varied
fixed effect can be included in the current version.
:param fix: Expression for the time independent fixed effect. Default value is None. An example:
fix = "Sex + age + Season".
:param forder: the order of Legendre polynomials for the time varied fixed effect. The default value is 3.
:param aorder: the order of Legendre polynomials for the additive genetic effect. The default value is 3.
:param porder: the order of Legendre polynomials for the permanent environment effect. The default value is 3.
:param na_method: The method to deal with missing values. The default value is 'omit'. 'omit' method will delete the
row with missing values. 'include' method will fill the missing values with the adjacent values.
:param prefix_outfile: the prefix for the output file. Default is 'unbalance_longwas_fixed'.
:return: A pandas data frame for the test result.
"""
logging.info('################################')
logging.info('###Prepare the related matrix###')
logging.info('################################')
if var_com.shape[0] != aorder * (aorder + 1) / 2 + aorder + 1 + porder * (
porder + 1) / 2 + porder + 1 + 1:
logging.info('ERROR: Variances do not match the data, please check')
exit()
logging.info('***Read the data file***')
logging.info('Data file: ' + data_file)
data_df = pd.read_csv(data_file, sep='\s+', header=0)
logging.info('NA method: ' + na_method)
if na_method == 'omit':
data_df = data_df.dropna()
elif na_method == 'include':
data_df = data_df.fillna(method='ffill')
data_df = data_df.fillna(method='bfill')
else:
logging.info('na_method does not exist: ' + na_method)
exit()
col_names = data_df.columns
logging.info('The column names of data file: ' + ' '.join(list(col_names)))
logging.info(
'Note: Variates beginning with a capital letter is converted into factors.'
)
class_vec = []
for val in col_names:
if not val[0].isalpha():
logging.info(
"The first character of columns names must be alphabet!")
exit()
if val[0] == val.capitalize()[0]:
class_vec.append(val)
data_df[val] = data_df[val].astype('str')
else:
try:
data_df[val] = data_df[val].astype('float')
except Exception as e:
logging.info(e)
logging.info(val + " may contain string, please check!")
exit()
logging.info('Individual column: ' + id)
if id not in col_names:
logging.info(id + ' is not in the data file, please check!')
exit()
if id not in class_vec:
logging.info('The initial letter of {} should be capital'.format(id))
exit()
id_order = []
id_arr = list(data_df[id])
id_order.append(id_arr[0])
for i in range(1, len(id_arr)):
if id_arr[i] != id_arr[i - 1]:
id_order.append(id_arr[i])
id_in_data = set(data_df[id])
if len(id_in_data) - len(id_order) != 0:
logging.info('The data is not sored by individual ID!')
exit()
logging.info('Time points column: ' + tpoint)
if tpoint not in col_names:
logging.info(tpoint + ' is not in the data file, please check!')
exit()
if tpoint in class_vec:
logging.info(
'The initial letter of {} should be lowercase'.format(tpoint))
exit()
logging.info('Trait column: ' + trait)
if trait not in col_names:
logging.info(trait + ' is not in the data file, please check!')
exit()
if trait in class_vec:
logging.info(
'The initial letter of {} should be lowercase'.format(trait))
exit()
logging.info('Code factor variables of the data file: ' +
' '.join(list(class_vec)))
code_val = {}
code_dct = dct_2D()
for val in class_vec:
code_val[val] = 0
temp = []
for i in range(data_df.shape[0]):
if data_df[val][i] not in code_dct[val]:
code_val[val] += 1
code_dct[val][data_df[val][i]] = str(code_val[val])
temp.append(code_dct[val][data_df[val][i]])
data_df[val] = np.array(temp)
for val in class_vec:
data_df[val] = data_df[val].astype('int')
logging.info('***Build the design matrix for fixed effect***')
logging.info('Time dependent fixed effect: ' + str(tfix))
leg_fix = leg(data_df[tpoint], forder)
if tfix == None:
xmat_t = np.concatenate(leg_fix, axis=1)
xmat_t = csr_matrix(xmat_t)
else:
if tfix not in class_vec:
logging.info(tfix + ' is not the class variate')
exit()
row = np.array(range(data_df.shape[0]))
col = np.array(data_df[tfix]) - 1
val = np.array([1.0] * data_df.shape[0])
tfix_mat = csr_matrix((val, (row, col)))
xmat_t = []
for i in range(len(leg_fix)):
xmat_t.append(tfix_mat.multiply(leg_fix[i]))
xmat_t = hstack(xmat_t)
del row, col, val
gc.collect()
logging.info('Time independent fix effect: ' + str(fix))
xmat_nt = None
if fix == None:
xmat_nt = None
else:
try:
fix_exp = ''
vec = fix.split('+')
for i in vec:
val = i.strip()
if val in class_vec:
fix_exp += 'C(' + val + ')'
else:
fix_exp += val
xmat_nt = dmatrix(fix_exp, data_df)
logging.info('The expression for fixed effect: ' + fix_exp)
except Exception as e:
logging.info(e + ': Check the fix effect expression.')
exit()
xmat_nt = csr_matrix(xmat_nt[:, 1:])
xmat = hstack([xmat_t, xmat_nt])
xmat = xmat.toarray()
max_id = max(data_df[id]) + 1
tmin = min(data_df[tpoint])
tmax = max(data_df[tpoint])
leg_lst = [
] # legendre polynomials for time dependent fixed SNP effects, save for each individuals
for i in range(1, max_id):
leg_lst.append(
leg_mt(data_df[data_df[id] == i][tpoint], tmax, tmin, forder))
tpoint_vec = sorted(set(data_df[tpoint]))
leg_tpoint_mat = leg_mt(np.array(tpoint_vec), tmax, tmin, aorder)
leg_tpoint_accum = np.sum(leg_tpoint_mat, axis=0)
logging.info('***Read the kinship matrix***')
logging.info('Kinship file: ' + kin_file)
with open(kin_file) as fin:
row = []
col = []
kin = []
id_in_kin = {}
for line in fin:
arr = line.split()
id_in_kin[arr[0]] = 1
id_in_kin[arr[1]] = 1
if arr[0] not in code_dct[id]:
logging.info(arr[0] + ' is not in the kinship inversion file!')
exit()
if arr[1] not in code_dct[id]:
logging.info(arr[1], 'is not in the kinship inversion file!')
exit()
row.append(int(code_dct[id][arr[0]]))
col.append(int(code_dct[id][arr[1]]))
kin.append(float(arr[2]))
id_not_in_kin = list(set(code_dct[id].keys()) - set(id_in_kin.keys()))
if len(id_not_in_kin) != 0:
logging.info(
'The ID: {} in the data file is not in the kinship file!'.format(
' '.join(id_not_in_kin)))
exit()
kin = csr_matrix(
(np.array(kin), (np.array(row) - 1, np.array(col) - 1))).toarray()
kin = np.add(kin, kin.T)
kin[np.diag_indices_from(kin)] = 0.5 * np.diag(kin)
del row, col
gc.collect()
logging.info('***Build the dedign matrix for random effect***')
logging.info('Legendre order for additive effects: ' + str(aorder))
leg_add = leg(data_df[tpoint], aorder)
row = np.array(range(data_df.shape[0]))
col = np.array(data_df[id]) - 1
val = np.array([1.0] * data_df.shape[0])
add_mat = csr_matrix((val, (row, col)),
shape=(data_df.shape[0], kin.shape[0]))
zmat_add = []
for i in range(len(leg_add)):
zmat_add.append(add_mat.multiply(leg_add[i]))
logging.info('Legendre order for permanent environmental effect: ' +
str(porder))
leg_per = leg(data_df[tpoint], porder)
per_mat = csr_matrix((val, (row, col)))
zmat_per = []
for i in range(len(leg_per)):
zmat_per.append((per_mat.multiply(leg_per[i])))
del row, col, val
gc.collect()
zmat = [zmat_add, zmat_per]
y = data_df[trait].values.reshape(data_df.shape[0], 1)
# kin_inv = [kin_inv, sparse.eye(max(data_df[id]), format="csr")]
logging.info('***Prepare the merged Z matrix***')
eff_ind = [[0, xmat.shape[1]]] # the index for all effects [start end]
zmat_con_lst = [] # combined random matrix
for i in range(len(zmat)):
temp = [eff_ind[i][-1]]
zmat_con_lst.append(hstack(zmat[i]))
for j in range(len(zmat[i])):
temp.append(temp[-1] + zmat[i][j].shape[1])
eff_ind.append(temp)
logging.info(
'***Calculate the phenotypic (co)variance and P = V(-1) - V(-1)X[X(T)V(-1)X](-1)X(T)V(-1)***'
)
add_cov = var_com.loc[var_com.loc[:, 'vari'] == 1, :]
row = np.array(add_cov['varij']) - 1
col = np.array(add_cov['varik']) - 1
val = add_cov['var_val']
add_cov = csr_matrix((val, (row, col))).toarray()
add_cov = add_cov + np.tril(add_cov, k=-1).T
per_cov = var_com.loc[var_com.loc[:, 'vari'] == 2, :]
row = np.array(per_cov['varij']) - 1
col = np.array(per_cov['varik']) - 1
val = per_cov['var_val']
per_cov = csr_matrix((val, (row, col))).toarray()
per_cov = per_cov + np.tril(per_cov, k=-1).T
res_var = np.array(var_com['var_val'])[-1]
vmat = zmat_con_lst[0].dot((zmat_con_lst[0].dot(np.kron(add_cov, kin))).T)
one_id = sparse.eye(zmat_con_lst[1].shape[1] / per_cov.shape[0])
vmat = vmat + zmat_con_lst[1].dot(
(zmat_con_lst[1].dot(sparse.kron(per_cov, one_id))).T)
vmat_diag = np.diag(vmat) + res_var
np.fill_diagonal(vmat, vmat_diag)
vmat = linalg.inv(vmat)
fam_df = pd.read_csv(bed_file + '.fam', sep='\s+', header=None)
id_geno = list(np.array(fam_df.iloc[:, 1], dtype=str))
id_order_index = []
for i in id_order:
id_order_index.append(id_geno.index(i))
snp_on_disk = Bed(bed_file, count_A1=False)
condition_snp_index = snp_on_disk.sid_to_index([condition_snp])[0]
condition_snp_val = snp_on_disk[:, condition_snp_index].read().val
condition_snp_val = condition_snp_val[id_order_index, 0]
snp_condition = list(
map(lambda x, y: x * y, leg_lst, list(condition_snp_val)))
snp_condition = np.concatenate(snp_condition, axis=0)
xmat = np.concatenate((xmat, snp_condition), axis=1)
vxmat = np.dot(vmat, xmat)
xvxmat = np.dot(xmat.T, vxmat)
xvxmat = linalg.inv(xvxmat)
pmat = vmat - reduce(np.dot, [vxmat, xvxmat, vxmat.T])
logging.info('***Read the snp data***')
# snp_mat = read_plink(bed_file)
snp_on_disk = Bed(bed_file, count_A1=False)
num_id = snp_on_disk.iid_count
num_snp = snp_on_disk.sid_count
logging.info("There are {:d} individuals and {:d} SNPs.".format(
num_id, num_snp))
"""
fam_df = pd.read_csv(bed_file + '.fam', sep='\s+', header=None)
id_geno = list(np.array(fam_df.iloc[:, 1], dtype=str))
id_order_index = []
for i in id_order:
id_order_index.append(id_geno.index(i))
"""
if snp_lst is None:
snp_lst = range(num_snp)
snp_lst = list(snp_lst)
if min(snp_lst) < 0 or max(snp_lst) >= num_snp:
logging.info('The value in the snp list should be >= {} and < {}', 0,
num_snp)
exit()
snp_mat = snp_on_disk[:, snp_lst].read().val
if np.any(np.isnan(snp_mat)):
logging.info('Missing genotypes are imputed with random genotypes.')
snp_mat = snp_mat[id_order_index, :]
# snp_mat = snp_mat[:, snp_lst]
logging.info(
'#####################################################################'
)
logging.info(
'###Start the random regression longitudinal GWAS for unbalance data###'
)
logging.info(
'#####################################################################'
)
qpmat = zmat_con_lst[0].T.dot(pmat)
qpqmat = zmat_con_lst[0].T.dot(qpmat.T)
qpymat = np.dot(qpmat, y)
chi_df = add_cov.shape[1]
eff_vec = []
chi_vec = []
p_vec = []
p_min_vec = []
p_accum_vec = []
for i in tqdm(range(snp_mat.shape[1])):
snpi = np.kron(add_cov, snp_mat[:, i:(i + 1)].T)
snpi_eff = np.dot(snpi, qpymat)
snpi_var = reduce(np.dot, [snpi, qpqmat, snpi.T])
chi_val = np.sum(
reduce(np.dot,
[snpi_eff.T, linalg.inv(snpi_var), snpi_eff]))
p_val = chi2.sf(chi_val, chi_df)
eff_vec.append(snpi_eff[:, -1])
chi_vec.append(chi_val)
p_vec.append(p_val)
p_tpoint_vec = []
for k in range(leg_tpoint_mat.shape[0]):
eff_tpoint = np.sum(np.dot(leg_tpoint_mat[k, :], snpi_eff))
eff_var_tpoint = np.sum(
np.dot(leg_tpoint_mat[k, :],
np.dot(snpi_var, leg_tpoint_mat[k, :])))
chi_tpoint = eff_tpoint * eff_tpoint / eff_var_tpoint
p_tpoint = chi2.sf(chi_tpoint, 1)
p_tpoint_vec.append(p_tpoint)
p_min_vec.append(min(p_tpoint_vec))
eff_accum = np.sum(np.dot(leg_tpoint_accum, snpi_eff))
eff_var_accum = np.sum(
np.dot(leg_tpoint_accum, np.dot(snpi_var, leg_tpoint_accum)))
chi_accum = eff_accum * eff_accum / eff_var_accum
p_accum = chi2.sf(chi_accum, 1)
p_accum_vec.append(p_accum)
logging.info('Finish association analysis')
logging.info('***Output***')
snp_info_file = bed_file + '.bim'
snp_info = pd.read_csv(snp_info_file, sep='\s+', header=None)
res_df = snp_info.iloc[snp_lst, [0, 1, 3, 4, 5]]
res_df.columns = ['chro', 'snp_ID', 'pos', 'allele1', 'allele2']
res_df.loc[:, 'order'] = snp_lst
res_df = res_df.iloc[:, [5, 0, 1, 2, 3, 4]]
eff_vec = np.array(eff_vec)
for i in range(eff_vec.shape[1]):
col_ind = 'eff' + str(i)
res_df.loc[:, col_ind] = eff_vec[:, i]
res_df.loc[:, 'chi_val'] = chi_vec
res_df.loc[:, 'p_val'] = p_vec
res_df.loc[:, 'p_min'] = p_min_vec
res_df.loc[:, 'p_accum'] = p_accum_vec
out_file = prefix_outfile + '.res'
res_df.to_csv(out_file, sep=' ', index=False)
return res_df
class _Epistasis(object) : #implements IDistributable
def __init__(self,test_snps,pheno,G0, G1=None, mixing=0.0, covar=None,sid_list_0=None,sid_list_1=None,
log_delta=None, min_log_delta=-5, max_log_delta=10, output_file=None, cache_file=None):
self._ran_once = False
self.test_snps = test_snps
self.pheno = pheno
self.output_file_or_none = output_file
self.cache_file = cache_file
self.covar = covar
self.sid_list_0 = sid_list_0
self.sid_list_1 = sid_list_1
self.G0=G0
self.G1_or_none=G1
self.mixing=mixing
self.external_log_delta=log_delta
self.min_log_delta = min_log_delta
self.max_log_delta = max_log_delta
self._str = "{0}({1},{2},G0={6},G1={7},mixing={8},covar={3},output_file={12},sid_list_0={4},sid_list_1{5},log_delta={9},min_log_delta={10},max_log_delta={11},cache_file={13})".format(
self.__class__.__name__, self.test_snps,self.pheno,self.covar,self.sid_list_0,self.sid_list_1,
self.G0, self.G1_or_none, self.mixing, self.external_log_delta, self.min_log_delta, self.max_log_delta, output_file, cache_file)
self.block_size = 1000
def set_sid_sets(self):
sid_set_0 = set(self.sid_list_0)
self.intersect = sid_set_0.intersection(self.sid_list_1)
self.just_sid_0 = sid_set_0.difference(self.intersect)
self.just_sid_1 = self.intersect.symmetric_difference(self.sid_list_1)
self._pair_count = len(self.just_sid_0)*len(self.intersect) + len(self.just_sid_0)*len(self.just_sid_1) + len(self.intersect)*len(self.just_sid_1) + len(self.intersect) * (len(self.intersect)-1)//2
self.test_snps, self.pheno, self.covar, self.G0, self.G1_or_none = pstutil.intersect_apply([self.test_snps, self.pheno, self.covar, self.G0, self.G1_or_none]) #should put G0 and G1 first
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
#start of IDistributable interface--------------------------------------
@property
def work_count(self):
self._run_once()
block_count = self.div_ceil(self._pair_count, self.block_size)
return block_count
def work_sequence(self):
self._run_once()
return self.work_sequence_range(0,self.work_count)
def work_sequence_range(self, start, end):
self._run_once()
lmm = self.lmm_from_cache_file()
lmm.sety(self.pheno['vals'])
for sid0_list, sid1_list in self.pair_block_sequence_range(start,end):
yield lambda lmm=lmm,sid0_list=sid0_list,sid1_list=sid1_list : self.do_work(lmm,sid0_list,sid1_list) # the 'lmm=lmm,...' is need to get around a strangeness in Python
def reduce(self, result_sequence):
#doesn't need "run_once()"
frame = pd.concat(result_sequence)
frame.sort_values(by="PValue", inplace=True)
frame.index = np.arange(len(frame))
if self.output_file_or_none is not None:
frame.to_csv(self.output_file_or_none, sep="\t", index=False)
return frame
#!!Find a place to output info like this near the end of the run
#logging.info("PhenotypeName\t{0}".format(pheno['header']))
#logging.info("SampleSize\t{0}".format(test_snps.iid_count))
#logging.info("SNPCount\t{0}".format(test_snps.sid_count))
#logging.info("Runtime\t{0}".format(time.time()-t0))
@property
def tempdirectory(self):
self._run_once()
return self.__tempdirectory
#optional override -- the str name of the instance is used by the cluster as the job name
def __str__(self):
#Doesn't need run_once
return self._str
def copyinputs(self, copier):
self._run_once()
if isinstance(self.test_snps, str):
copier.input(self.test_snps + ".bed")
copier.input(self.test_snps + ".bim")
copier.input(self.test_snps + ".fam")
else:
copier.input(self.test_snps)
copier.input(self.pheno)
copier.input(self.covar)
if isinstance(self.G0, str):
copier.input(self.G0 + ".bed")
copier.input(self.G0 + ".bim")
copier.input(self.G0 + ".fam")
else:
copier.input(self.G0)
copier.input(self.G1_or_none)
copier.input(self.cache_file)
def copyoutputs(self,copier):
#Doesn't need run_once
copier.output(self.output_file_or_none)
#end of IDistributable interface---------------------------------------
@staticmethod
def div_ceil(num, den): #!!move to utils?
return -(-num//den) #The -/- trick makes it do ceiling instead of floor. "//" will do integer division even in the future and on floats.
def pair_block_sequence_range(self,block_start,block_end):
self._run_once()
assert 0 <= block_start and block_start <= block_end and block_end <= self.work_count, "real assert"
block_index = block_start
start = block_index * self.pair_count // self.work_count
next_start = (block_index+1) * self.pair_count // self.work_count
size_goal = next_start - start
end = block_end * self.pair_count // self.work_count
sid0_list = []
sid1_list = []
for sid0, sid1 in self.pair_sequence_range(start,end):
sid0_list.append(sid0)
sid1_list.append(sid1)
if len(sid0_list) == size_goal:
yield sid0_list, sid1_list
block_index += 1
if block_index == block_end:
return
sid0_list = []
sid1_list = []
start = next_start
next_start = (block_index+1) * self.pair_count // self.work_count
size_goal = next_start - start
assert len(sid0_list) == 0, "real assert"
#If start == end, then returns without yielding anything
def pair_sequence_range(self,start,end):
self._run_once()
assert 0 <= start and start <= end and end <= self._pair_count, "real assert"
i = start
for sid0, sid1 in self.pair_sequence_with_start(start):
yield sid0, sid1
i = i + 1
if i == end:
break
assert i == end, "Not enough items found. Didn't get to the end"
def pair_sequence_with_start(self,start):
self._run_once()
skip_ref = [start]
just_sid_0_list = list(self.just_sid_0)
just_sid_1_list = list(self.just_sid_1)
intersect_list = list(self.intersect)
for sid0, sid1 in self.combo_distinct(just_sid_0_list, intersect_list, skip_ref):
yield sid0, sid1
for sid0, sid1 in self.combo_distinct(just_sid_0_list, just_sid_1_list, skip_ref):
yield sid0, sid1
for sid0, sid1 in self.combo_distinct(intersect_list, just_sid_1_list, skip_ref):
yield sid0, sid1
for sid0, sid1 in self.combo_same(intersect_list, skip_ref):
yield sid0, sid1
assert skip_ref[0] == 0, "real assert"
def combo_distinct(self, distinct__list0, distinct__list1, skip_ref):
row_count = len(distinct__list0)
col_count = len(distinct__list1)
if skip_ref[0] >= row_count * col_count:
skip_ref[0] = skip_ref[0] - row_count * col_count
assert skip_ref[0] >=0, "real assert"
return
row_start = skip_ref[0] // col_count
skip_ref[0] = skip_ref[0] - row_start * col_count
assert skip_ref[0] >=0, "real assert"
for row_index in range(row_start, row_count):
sid0 = distinct__list0[row_index]
if row_index == row_start:
col_start = skip_ref[0]
skip_ref[0] = 0
else:
col_start = 0
for col_index in range(col_start, col_count):
sid1 = distinct__list1[col_index]
yield sid0, sid1
def combo_same(self, list, skip_ref):
count = len(list)
full_size = count * (count + 1) // 2
if skip_ref[0] >= full_size:
skip_ref[0] = skip_ref[0] - full_size
assert skip_ref[0] >=0, "real assert"
return
row_start = int((-1 + 2*count - np.sqrt(1 - 4*count + 4*count**2 - 8*skip_ref[0]))/2)
skip_ref[0] = skip_ref[0] - (count*row_start - (row_start*(1 + row_start))//2)
assert skip_ref[0] >=0, "real assert"
for row_index in range(row_start, count):
sid0 = list[row_index]
if row_index == row_start:
col_start = skip_ref[0]
skip_ref[0] = 0
else:
col_start = 0
for col_index in range(col_start + 1 + row_index, count):
sid1 = list[col_index]
assert sid0 is not sid1, "real assert"
yield sid0, sid1
@property
def pair_count(self):
self._run_once()
return self._pair_count
def lmm_from_cache_file(self):
logging.info("Loading precomputation from {0}".format(self.cache_file))
lmm = LMM()
with np.load(self.cache_file) as data:
lmm.U = data['arr_0']
lmm.S = data['arr_1']
return lmm
def fill_in_cache_file(self):
self._run_once()
logging.info("filling in the cache_file and log_delta, as needed")
if self.G1_or_none is None:
self.G1val_or_none = None
else:
self.G1val_or_none = self.G1_or_none.read().val
# The S and U are always cached, in case they are needed for the cluster or for multi-threaded runs
if self.cache_file is None:
self.cache_file = os.path.join(self.__tempdirectory, "cache_file.npz")
if os.path.exists(self.cache_file): # If there is already a cache file in the temp directory, it must be removed because it might be out-of-date
os.remove(self.cache_file)
lmm = None
if not os.path.exists(self.cache_file):
logging.info("Precomputing eigen")
lmm = LMM()
G0_standardized = self.G0.read().standardize()
lmm.setG(G0_standardized.val, self.G1val_or_none, a2=self.mixing)
logging.info("Saving precomputation to {0}".format(self.cache_file))
util.create_directory_if_necessary(self.cache_file)
np.savez(self.cache_file, lmm.U,lmm.S) #using np.savez instead of pickle because it seems to be faster to read and write
if self.external_log_delta is None:
if lmm is None:
lmm = self.lmm_from_cache_file()
logging.info("searching for internal delta")
lmm.setX(self.covar)
lmm.sety(self.pheno['vals'])
#log delta is used here. Might be better to use findH2, but if so will need to normalized G so that its K's diagonal would sum to iid_count
result = lmm.find_log_delta(REML=False, sid_count=self.G0.sid_count, min_log_delta=self.min_log_delta, max_log_delta=self.max_log_delta ) #!!what about findA2H2? minH2=0.00001
self.external_log_delta = result['log_delta']
self.internal_delta = np.exp(self.external_log_delta) * self.G0.sid_count
logging.info("internal_delta={0}".format(self.internal_delta))
logging.info("external_log_delta={0}".format(self.external_log_delta))
do_pair_count = 0
do_pair_time = time.time()
def do_work(self, lmm, sid0_list, sid1_list):
dataframe = pd.DataFrame(
index=np.arange(len(sid0_list)),
columns=('SNP0', 'Chr0', 'GenDist0', 'ChrPos0', 'SNP1', 'Chr1', 'GenDist1', 'ChrPos1', 'PValue', 'NullLogLike', 'AltLogLike')
)
#!!Is this the only way to set types in a dataframe?
dataframe['Chr0'] = dataframe['Chr0'].astype(np.float)
dataframe['GenDist0'] = dataframe['GenDist0'].astype(np.float)
dataframe['ChrPos0'] = dataframe['ChrPos0'].astype(np.float)
dataframe['Chr1'] = dataframe['Chr1'].astype(np.float)
dataframe['GenDist1'] = dataframe['GenDist1'].astype(np.float)
dataframe['ChrPos1'] = dataframe['ChrPos1'].astype(np.float)
dataframe['PValue'] = dataframe['PValue'].astype(np.float)
dataframe['NullLogLike'] = dataframe['NullLogLike'].astype(np.float)
dataframe['AltLogLike'] = dataframe['AltLogLike'].astype(np.float)
#This is some of the code for a different way that reads and dot-products 50% more, but does less copying. Seems about the same speed
#sid0_index_list = self.test_snps.sid_to_index(sid0_list)
#sid1_index_list = self.test_snps.sid_to_index(sid1_list)
#sid_index_union_dict = {}
#sid0_index_index_list = self.create_index_index(sid_index_union_dict, sid0_index_list)
#sid1_index_index_list = self.create_index_index(sid_index_union_dict, sid1_index_list)
#snps0_read = self.test_snps[:,sid0_index_list].read().standardize()
#snps1_read = self.test_snps[:,sid1_index_list].read().standardize()
sid_union = set(sid0_list).union(sid1_list)
sid_union_index_list = sorted(self.test_snps.sid_to_index(sid_union))
snps_read = self.test_snps[:,sid_union_index_list].read().standardize()
sid0_index_list = snps_read.sid_to_index(sid0_list)
sid1_index_list = snps_read.sid_to_index(sid1_list)
products = snps_read.val[:,sid0_index_list] * snps_read.val[:,sid1_index_list] # in the products matrix, each column i is the elementwise product of sid i in each list
X = np.hstack((self.covar, snps_read.val, products))
UX = lmm.U.T.dot(X)
k = lmm.S.shape[0]
N = X.shape[0]
if (k<N):
UUX = X - lmm.U.dot(UX)
else:
UUX = None
for pair_index, sid0 in enumerate(sid0_list):
sid1 = sid1_list[pair_index]
sid0_index = sid0_index_list[pair_index]
sid1_index = sid1_index_list[pair_index]
index_list = np.array([pair_index]) #index to product
index_list = index_list + len(sid_union_index_list) #Shift by the number of snps in the union
index_list = np.hstack((np.array([sid0_index,sid1_index]),index_list)) # index to sid0 and sid1
index_list = index_list + self.covar.shape[1] #Shift by the number of values in the covar
index_list = np.hstack((np.arange(self.covar.shape[1]),index_list)) #indexes of the covar
index_list_less_product = index_list[:-1] #index to everything but the product
#Null -- the two additive SNPs
lmm.X = X[:,index_list_less_product]
lmm.UX = UX[:,index_list_less_product]
if (k<N):
lmm.UUX = UUX[:,index_list_less_product]
else:
lmm.UUX = None
res_null = lmm.nLLeval(delta=self.internal_delta, REML=False)
ll_null = -res_null["nLL"]
#Alt -- now with the product feature
lmm.X = X[:,index_list]
lmm.UX = UX[:,index_list]
if (k<N):
lmm.UUX = UUX[:,index_list]
else:
lmm.UUX = None
res_alt = lmm.nLLeval(delta=self.internal_delta, REML=False)
ll_alt = -res_alt["nLL"]
test_statistic = ll_alt - ll_null
degrees_of_freedom = 1
pvalue = stats.chi2.sf(2.0 * test_statistic, degrees_of_freedom)
logging.debug("<{0},{1}>, null={2}, alt={3}, pvalue={4}".format(sid0,sid1,ll_null,ll_alt,pvalue))
dataframe.iloc[pair_index] = [
sid0, snps_read.pos[sid0_index,0], snps_read.pos[sid0_index,1], snps_read.pos[sid0_index,2],
sid1, snps_read.pos[sid1_index,0], snps_read.pos[sid1_index,1], snps_read.pos[sid1_index,2],
pvalue, ll_null, ll_alt]
self.do_pair_count += 1
if self.do_pair_count % 100 == 0:
start = self.do_pair_time
self.do_pair_time = time.time()
logging.info("do_pair_count={0}, time={1}".format(self.do_pair_count,self.do_pair_time-start))
return dataframe
def __init__(self,args):
if args.window_type not in ['KBP','SNP']:
raise ValueError('Window type not supported')
# Open files
bed_1 = Bed(args.bfile1,count_A1=False) #
bed_2 = Bed(args.bfile2,count_A1=False)
# Get indel locations, if any
bim_1=pd.read_table(bed_1.filename+'.bim',header=None,
names=['chm','id','pos_mb','pos_bp','a1','a2'])
bim_2=pd.read_table(bed_2.filename+'.bim',header=None,
names=['chm','id','pos_mb','pos_bp','a1','a2'])
is_indel_1 = np.array([(len(str(a1))>1)|(len(str(a2))>1) for a1,a2 in bim_1[['a1','a2']].values])
is_indel_2 = np.array([(len(str(a1))>1)|(len(str(a2))>1) for a1,a2 in bim_2[['a1','a2']].values])
# Make sure two SNPs don't have the same position
is_duplicated_bp_1=bim_1.pos_bp.duplicated()
is_duplicated_bp_2=bim_2.pos_bp.duplicated()
# Make sure two SNPs don't have the same ID
is_duplicated_id_1=bim_1.id.duplicated()
is_duplicated_id_2=bim_2.id.duplicated()
# Get allele frequencies
af1 = self.get_allele_frequency(bed_1,args) #
af2 = self.get_allele_frequency(bed_2,args)
print(len(af1), "Variants in file 1")
print(len(af2), "Variants in file 2")
# Get good SNPs
snps_1 = (af1>args.maf)&(af1<1-args.maf)&(~is_indel_1)&(~is_duplicated_bp_1)&(~is_duplicated_id_1) #
snps_2 = (af2>args.maf)&(af2<1-args.maf)&(~is_indel_2)&(~is_duplicated_bp_2)&(~is_duplicated_id_2)
print(np.sum(snps_1), "SNPs in file 1 after MAF and indel filter")
print(np.sum(snps_2), "SNPs in file 2 after MAF and indel filter")
if (args.from_bp is not None) and (args.to_bp is not None):
k1 = (bed_1.pos[:,2]>args.from_bp)&(bed_1.pos[:,2]<args.to_bp)
k2 = (bed_2.pos[:,2]>args.from_bp)&(bed_2.pos[:,2]<args.to_bp)
snps_1 = snps_1&k1
snps_2 = snps_2&k2
snps_to_use = np.intersect1d(bed_1.sid[snps_1.values],bed_2.sid[snps_2.values])
print(len(snps_to_use),"SNPs common in both populations")
if args.extract is not None:
keep = np.array([l.strip() for l in open(args.extract,'r')])
print(len(keep),"SNPs to extract")
snps_to_use = np.intersect1d(snps_to_use,keep)
print(len(snps_to_use),"SNPs remaining after extraction")
bed_1_index = np.sort(bed_1.sid_to_index(snps_to_use)) #
bed_2_index = np.sort(bed_2.sid_to_index(snps_to_use))
if not args.no_align:
alignment,bed_1_index,bed_2_index =\
self.align_alleles(bed_1,bed_1_index,af1,bed_2,bed_2_index,af2)
else:
alignment = np.ones(len(bed_1_index))
pos = bed_1.pos[bed_1_index] #
af1 = af1[bed_1_index] #
af2 = af2[bed_2_index]
# if args.afile1 is not None:
# a1 = pd.read_table(args.afile,header=None,sep='\s*',
# names=['id1','id2','theta'])
# else:
a1 = None
# if args.afile2 is not None:
# a2 = pd.read_table(args.afile,header=None,sep='\s*',
# names=['id1','id2','theta'])
# else:
a2 = None
self.af1 = af1 #
self.af2 = af2
self.M = len(bed_1_index) #
self.N = (bed_1.iid_count, bed_2.iid_count) #
self.chr = pos[:,0]
self.pos = pos[:,2]
self.id = bed_1.sid[bed_1_index]
self.A1 = bim_1['a1'].iloc[bed_1_index]
self.A2 = bim_1['a2'].iloc[bed_1_index]
self.windows = self.get_windows(pos,args) #
self.scores1 = self.compute(bed_1,bed_1_index,af1,a1,args)
self.scores2 = self.compute(bed_2,bed_2_index,af2,a2,args) #
self.scoresX = self.compute2(bed_1,bed_1_index,bed_2,bed_2_index,
alignment,a1,a2,args) #
class fit(object):
def __init__(self,args):
self.bed = Bed(args.bfile) #
self.N = self.bed.iid_count
if args.covfile is not None:
cov = pd.read_table(args.covfile,header=None)
self.cov = sm.add_constant(ju._reorder(cov,self.bed.iid))
self.ncov = self.cov.shape[1] # + constant
else:
self.cov = np.ones((self.N,1))
self.ncov = 1 # Constant
if args.phenofile is not None:
Y = pd.read_table(args.phenofile,header=None,na_values='-9')
else:
try:
Y = pd.read_table(args.bfile+'.pheno',header=None,na_values='-9')
except IOError:
print("Phenotype file not found.")
exit(1)
self.Y = ju._reorder(Y,self.bed.iid)
af = ju.get_allele_frequency(self.bed,args) #
snps = (af>args.maf)&(af<1-args.maf) #
if (args.from_bp is not None) and (args.to_bp is not None):
k = (bed.pos[:,2]>args.from_bp)&(bed.pos[:,2]<args.to_bp)
snp1 = snps&k
snps_to_use = self.bed.sid[snps]
if args.extract is not None:
keep = np.array([l.strip() for l in open(args.extract,'r')])
snps_to_use = np.intersect1d(snps_to_use,keep)
self.bed_index = np.sort(self.bed.sid_to_index(snps_to_use)) #
pos = self.bed.pos[self.bed_index] #
bim=pd.read_table(self.bed.filename+'.bim',header=None,
names=['chm','id','pos_mb','pos_bp','a1','a2'])
self.af = af[self.bed_index] #
self.M = len(self.bed_index) #
self.windows = ju.get_windows(pos,self.M,args.window_size,args.window_type)
self.pos = pos[:,2]
self.chr = pos[:,0]
self.id = self.bed.sid[self.bed_index]
self.A1 = bim['a1'].loc[self.bed_index]
self.A2 = bim['a2'].loc[self.bed_index]
self.logistic = False
self.chimin = stats.chi2.ppf(1-args.minp,2)
# Fit null
if (not args.linear) and (self.Y.min() >= 0 and self.Y.max() <= 1):
self.null = sm.Logit(self.Y, self.cov, missing='drop').fit(disp=0)
self.logistic = True
else:
self.null = sm.OLS(self.Y, self.cov, missing='drop').fit(disp=0)
if self.ncov > 1:
self.cov = sm.add_constant(self.null.fittedvalues)
self.marg_res, self.joint_res = self.compute(args)
def compute(self,args):
t=time()
marg_res = []
joint_res = []
Z = []
windex = 0
li,ri = self.windows[windex]
nstr = np.max((args.SNPs_to_read,ri-li))
offset = li
G = self.bed[:,self.bed_index[li:(li+nstr)]].read().val
G = ju._impute_missing(G) # replace missing with mean
self.compute_marg(marg_res,Z,G,li,args)
A = ju._norm_data(G)
while ri < offset+nstr:
st = li-offset
fi = ri-offset
# All correlations of SNP j with SNPs in its window
R = np.dot(np.atleast_2d(A[:,st]/self.N),A[:,(st+1):fi]).flatten()
Zl = Z[li]
Zr = np.array(Z[(li+1):ri])
# Use marginal Z-scores and R to compute expected joint chi2s
ChiP = (1/(1-R**2))*(Zl**2+Zr**2-2*R*Zl*Zr)
ChiP[R**2 < args.r2min] = -1
self.compute_joint(joint_res,G,ChiP,offset,li,ri,args)
windex += 1
li,ri = self.windows[windex]
for i in xrange(offset+nstr,self.M,nstr):
sys.stdout.flush()
sys.stdout.write("SNP: %d, %f\r" % (i, time()-t))
Gn = self.bed[:,self.bed_index[i:(i+nstr)]].read().val
Gn = ju._impute_missing(Gn)
An = ju._norm_data(Gn)
self.compute_marg(marg_res,Z,Gn,i,args)
G = np.hstack((G,Gn))
A = np.hstack((A,An))
if G.shape[1] > args.SNPs_to_store:
G = G[:,nstr:]
A = A[:,nstr:]
offset += nstr
while ri < i+nstr:
st = li-offset
fi = ri-offset
# All correlations of SNP j with SNPs in its window
R = np.dot(np.atleast_2d(A[:,st]/self.N),A[:,(st+1):fi]).flatten()
Zl = Z[li]
Zr = np.array(Z[(li+1):ri])
ChiP = (1/(1-R**2))*(Zl**2+Zr**2-2*R*Zl*Zr)
ChiP[R**2 < args.r2min] = -1
self.compute_joint(joint_res,G,ChiP,offset,li,ri,args)
try:
windex += 1
li,ri = self.windows[windex]
except IndexError:
break
marg_res = pd.DataFrame(marg_res)
joint_res = pd.DataFrame(joint_res)
return marg_res, joint_res
def compute_joint(self,joint_res,G,ChiP,offset,li,ri,args):
st = li-offset
fi = ri-offset
snp1 = G[:,st]
for i,snp2 in enumerate(G[:,(st+1):fi].T):
if ChiP[i] > self.chimin:
X = np.hstack((self.cov,snp1.reshape((len(snp1),1)),
snp2.reshape((len(snp2),1))))
if self.logistic:
joint = sm.Logit(self.Y,X).fit(disp=0)
else:
joint = sm.OLS(self.Y,X).fit(disp=0)
joint_b1 = joint.params[-2]
joint_b2 = joint.params[-1]
joint_or1 = np.exp(joint_b1)
joint_or2 = np.exp(joint_b2)
joint_se1 = joint.bse[-2]
joint_se2 = joint.bse[-1]
joint_p1 = joint.pvalues[-2]
joint_p2 = joint.pvalues[-1]
joint_t1 = joint.tvalues[-2]
joint_t2 = joint.tvalues[-1]
joint_Chi2 = 2*(joint.llf - self.null.llf)
pv = stats.chi2.sf(joint_Chi2,2)
joint_res.append([self.chr[li],self.id[li],self.id[offset+i],
self.pos[li],self.pos[offset+i],joint_b1,joint_se1,
joint_or1,joint_t1,joint_p1,joint_b2,joint_se2,
joint_or2,joint_t2,joint_p2,joint_Chi2,pv])
else:
continue
def compute_marg(self,marg_res,Z,G,offset,args):
for i,snp in enumerate(G.T):
X = np.hstack((self.cov,snp.reshape((len(snp),1))))
if self.logistic:
marg = sm.Logit(self.Y,X).fit(disp=0)
else:
marg = sm.OLS(self.Y,X).fit(disp=0)
marg_b = marg.params[-1]
marg_or = np.exp(marg_b)
marg_se = marg.bse[-1]
marg_p = marg.pvalues[-1]
marg_t = marg.tvalues[-1]
marg_Chi2 = 2*(marg.llf - self.null.llf)
pv = stats.chi2.sf(marg_Chi2,1)
marg_res.append([self.chr[offset+i],self.id[offset+i],
self.pos[offset+i],marg_b, marg_se, marg_or, marg_t,
marg_p,marg_Chi2,pv])
Z.append(marg_b/marg_se)
def __init__(self, args):
if args.window_type not in ['BP', 'SNP']:
raise ValueError('Window type not supported')
bed_1 = Bed(args.bfile1) #
bed_2 = Bed(args.bfile2)
af1 = self.get_allele_frequency(bed_1, args) #
af2 = self.get_allele_frequency(bed_2, args)
print(len(af1), "SNPs in file 1")
print(len(af2), "SNPs in file 2")
snps_1 = (af1 > args.maf) & (af1 < 1 - args.maf) #
snps_2 = (af2 > args.maf) & (af2 < 1 - args.maf)
print(np.sum(snps_1), "SNPs in file 1 after MAF filter")
print(np.sum(snps_2), "SNPs in file 2 after MAF filter")
if (args.from_bp is not None) and (args.to_bp is not None):
k1 = (bed_1.pos[:, 2] > args.from_bp) & (bed_1.pos[:, 2] <
args.to_bp)
k2 = (bed_2.pos[:, 2] > args.from_bp) & (bed_2.pos[:, 2] <
args.to_bp)
snps_1 = snps_1 & k1
snps_2 = snps_2 & k2
snps_to_use = np.intersect1d(bed_1.sid[snps_1], bed_2.sid[snps_2])
print(len(snps_to_use), "SNPs common in both populations")
if args.extract is not None:
keep = np.array([l.strip() for l in open(args.extract, 'r')])
print(len(keep), "SNPs to extract")
snps_to_use = np.intersect1d(snps_to_use, keep)
print(len(snps_to_use), "SNPs remaining after extraction")
bed_1_index = np.sort(bed_1.sid_to_index(snps_to_use)) #
bed_2_index = np.sort(bed_2.sid_to_index(snps_to_use))
if not args.no_align:
alignment,bed_1_index,bed_2_index =\
self.align_alleles(bed_1,bed_1_index,af1,bed_2,bed_2_index,af2)
else:
alignment = np.ones(len(bed_1_index))
pos = bed_1.pos[bed_1_index] #
bim_1 = pd.read_table(
bed_1.filename + '.bim',
header=None,
names=['chm', 'id', 'pos_mb', 'pos_bp', 'a1', 'a2'])
af1 = af1[bed_1_index] #
af2 = af2[bed_2_index]
if args.afile1 is not None:
a1 = pd.read_table(args.afile,
header=None,
sep='\s*',
names=['id1', 'id2', 'theta'])
else:
a1 = None
if args.afile2 is not None:
a2 = pd.read_table(args.afile,
header=None,
sep='\s*',
names=['id1', 'id2', 'theta'])
else:
a2 = None
self.af1 = af1 #
self.af2 = af2
self.M = len(bed_1_index) #
self.N = (bed_1.iid_count, bed_2.iid_count) #
self.chr = pos[:, 0]
self.pos = pos[:, 2]
self.id = bed_1.sid[bed_1_index]
self.A1 = bim_1['a1'].loc[bed_1_index]
self.A2 = bim_1['a2'].loc[bed_1_index]
self.windows = self.get_windows(pos, args) #
self.scores1 = self.compute(bed_1, bed_1_index, af1, a1, args)
self.scores2 = self.compute(bed_2, bed_2_index, af2, a2, args) #
self.scoresX = self.compute2(bed_1, bed_1_index, bed_2, bed_2_index,
alignment, a1, a2, args) #
class sample(object):
def __init__(self,args):
self.bed = Bed(args.bfile) #
self.N = self.bed.iid_count
if args.covfile is not None:
cov = pd.read_table(args.covfile,header=None)
self.cov = sm.add_constant(ju._reorder(cov,self.bed.iid))
self.ncov = self.cov.shape[1] # + constant
else:
self.cov = np.ones((self.N,1))
self.ncov = 1 # Constant
af = ju.get_allele_frequency(self.bed,args) #
snps = (af>args.maf)&(af<1-args.maf) #
if (args.from_bp is not None) and (args.to_bp is not None):
k = (bed.pos[:,2]>args.from_bp)&(bed.pos[:,2]<args.to_bp)
snp1 = snps&k
snps_to_use = self.bed.sid[snps]
if args.extract is not None:
keep = np.array([l.strip() for l in open(args.extract,'r')])
snps_to_use = np.intersect1d(snps_to_use,keep)
self.bed_index = np.sort(self.bed.sid_to_index(snps_to_use)) #
pos = self.bed.pos[self.bed_index] #
bim=pd.read_table(self.bed.filename+'.bim',header=None,
names=['chm','id','pos_mb','pos_bp','a1','a2'])
self.af = af[self.bed_index] #
self.M = len(self.bed_index) #
self.windows = ju.get_windows(pos,self.M,args.window_size,args.window_type)
self.sample_windows = ju.get_windows(pos,self.M,args.sample_window_size,
args.sample_window_type)
self.pos = pos[:,2]
self.chr = pos[:,0]
self.id = self.bed.sid[self.bed_index]
self.A1 = bim['a1'].loc[self.bed_index]
self.A2 = bim['a2'].loc[self.bed_index]
self.numSamples = args.numSamples
self.JMaxStats, self.ZMaxStats = self.sample(args)
self.JMinP = stats.chi2.sf(self.JMaxStats,2)
self.ZMinP = stats.chi2.sf(self.ZMaxStats**2,1)
self.minP = np.minimum(self.JMinP,self.ZMinP)
def sample(self,args):
t=time()
nz = 0
ZMaxStats = np.zeros((self.numSamples,1))
JMaxStats = np.zeros((self.numSamples,1))
windex = 0
sli,sri = self.sample_windows[windex]
tli,tri = self.windows[windex]
nstr = np.max((args.SNPs_to_read,sri-sli))
offset = sli
G = self.bed[:,self.bed_index[sli:(sli+nstr)]].read().val
G = ju._impute_missing(G)
A = ju._norm_data(G)
# Sample Z-scores and do joint tests of first window
R = np.dot(A[:,sli:sri].T/self.N,A[:,sli:sri])
Z=np.random.multivariate_normal(np.zeros((R.shape[0])),R,args.numSamples)
nz += R.shape[0]
zli,zri = sli,sri # position of Z relative to full genotype
gli,gri = zli,zri # position of Z relative to genotype in memory
Rp = R[(tli+1):tri,0]
to_test = Rp**2 > args.r2min
Rp = Rp[to_test]
Zl = np.atleast_2d(Z[:,0]).T
Zr = np.array(Z[:,1:(tri-tli)])[:,to_test]
ChiP = (1/(1-Rp**2))*(Zl**2+Zr**2-2*Rp*Zl*Zr)
ZMaxStats = np.atleast_2d(np.hstack((ZMaxStats,abs(Z))).max(1)).T
# ZMaxStats = np.maximum(ZMaxStats,abs(Z.max(1)))
# JMaxStats = np.maximum(JMaxStats,ChiP.max(1))
JMaxStats = np.atleast_2d(np.hstack((JMaxStats,ChiP)).max(1)).T
# Slide through genotype in memory
while True:
windex += 1
sli,sri = self.sample_windows[windex]
tli,tri = self.windows[windex]
if sri >= offset+nstr: break
tst,tfi,sst,sfi = np.array([tli,tri,sli,sri])-offset
#print sli, sri, zli, zri, gli, gri, Z.shape[1]
if zli < sli: # drop zli..sli and update indices
Z = Z[:,(sli-zli):]
zli,gli = sli,sst
if zri < sri: # marginal sample everything from zri..sri
S = A[:,gli:gri] # G that overlaps Z
Sn = A[:,gri:sri] # G about to have Z scores sampled
r12 = S.T.dot(Sn)/self.N
r11 = Sn.T.dot(Sn)/self.N
Zn = self.sample_func(Z,S,Sn,r11,r12,args)
Z = np.hstack((Z,Zn))
nz += (sri-gri)
zri,gri = sri,sfi
ZMaxStats = np.atleast_2d(np.hstack((ZMaxStats,abs(Zn))).max(1)).T
# ZMaxStats = np.maximum(ZMaxStats,abs(Zn.flatten()))
# All correlations of SNP tli with SNPs in its window
# Surely these are already computed and some cleverness
# can be used to re-use them but its a fast calculation anyways
if sri-sli > 1:
R = np.dot(np.atleast_2d(A[:,tst]/self.N),
A[:,(tst+1):tfi]).flatten()
to_test = R**2 > args.r2min
R = R[to_test]
Zl = np.atleast_2d(Z[:,0]).T
Zr = np.array(Z[:,1:(sri-sli)])[:,to_test]
ChiP = (1/(1-R**2))*(Zl**2+Zr**2-2*R*Zl*Zr)
JMaxStats = np.atleast_2d(np.hstack((JMaxStats,ChiP)).max(1)).T
#JMaxStats = np.maximum(JMaxStats,ChiP.max(1))
for i in xrange(offset+nstr,self.M,nstr):
sys.stdout.flush()
sys.stdout.write("SNP: %d, %f\r" % (i, time()-t))
Gn = self.bed[:,self.bed_index[i:(i+nstr)]].read().val
Gn = ju._impute_missing(Gn)
An = ju._norm_data(Gn)
G = np.hstack((G,Gn))
A = np.hstack((A,An))
if G.shape[1] > args.SNPs_to_store:
G = G[:,nstr:]
A = A[:,nstr:]
offset += nstr
gli -= nstr
gri -= nstr
while sri < i+nstr:
tst,tfi,sst,sfi = np.array([tli,tri,sli,sri])-offset
if zli < sli: # drop zli..sli and update indices
Z = Z[:,(sli-zli):]
zli,gli = sli,sst
if zri < sri: # marginal sample everything from zri..sri
S = A[:,gli:gri] # G that overlaps Z
Sn = A[:,gri:sfi] # G about to have Z scores sampled
r12 = S.T.dot(Sn)/self.N
r11 = Sn.T.dot(Sn)/self.N
Zn = self.sample_func(Z,S,Sn,r11,r12,args)
Z = np.hstack((Z,Zn))
nz += (sfi-gri)
zri,gri = sri,sfi
ZMaxStats = np.atleast_2d(np.hstack((ZMaxStats,abs(Zn))).max(1)).T
# ZMaxStats = np.maximum(ZMaxStats,abs(Zn.flatten()))
if sri-sli > 1:
R = np.dot(np.atleast_2d(A[:,tst]/self.N),
A[:,(tst+1):tfi]).flatten()
to_test = R**2 > args.r2min
R = R[to_test]
Zl = np.atleast_2d(Z[:,0]).T
Zr = np.array(Z[:,1:(sri-sli)])[:,to_test]
ChiP = (1/(1-R**2))*(Zl**2+Zr**2-2*R*Zl*Zr)
#JMaxStats = np.maximum(JMaxStats,ChiP.max(1))
JMaxStats = np.atleast_2d(np.hstack((JMaxStats,ChiP)).max(1)).T
try:
windex += 1
sli,sri = self.sample_windows[windex]
tli,tri = self.windows[windex]
except IndexError:
break
# print "HERE:", nz
return JMaxStats.flatten(), ZMaxStats.flatten()
def sample_func(self,Z,S,Sn,r11,r12,args):
S22IS12 = sp.linalg.lstsq(S,Sn,cond=1e-8)[0]
muC = Z.dot(S22IS12)
SigC = r11-r12.T.dot(S22IS12)
Zn = np.random.multivariate_normal(np.zeros((SigC.shape[0])),
SigC,size=args.numSamples)
Zn += muC
return Zn
