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# pylmm is a python-based linear mixed-model solver with applications to GWAS
# Copyright (C) 2013 Nicholas A. Furlotte (nick.furlotte@gmail.com)
#The program is free for academic use. Please contact Nick Furlotte
#<nick.furlotte@gmail.com> if you are interested in using the software for
#commercial purposes.
#The software must not be modified and distributed without prior
#permission of the author.
#THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
#"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
#LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
#A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
#CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
#EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
#PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
#PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
#LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
#NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
#SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
import sys
import numpy as np
import struct
import pdb
class plink:
def __init__(self,fbase,kFile=None,phenoFile=None,type='b',normGenotype=True,readKFile=False):
self.fbase = fbase
self.type = type
self.indivs = self.getIndivs(self.fbase,type)
self.kFile = kFile
self.phenos = None
self.normGenotype = normGenotype
self.phenoFile = phenoFile
# Originally I was using the fastLMM style that has indiv IDs embedded.
# NOW I want to use this module to just read SNPs so I'm allowing
# the programmer to turn off the kinship reading.
self.readKFile = readKFile
if self.kFile: self.K = self.readKinship(self.kFile)
elif os.path.isfile("%s.kin" % fbase):
self.kFile = "%s.kin" %fbase
if self.readKFile:
self.K = self.readKinship(self.kFile)
else:
self.kFile = None
self.K = None
self.getPhenos(self.phenoFile)
self.fhandle = None
self.snpFileHandle = None
def __del__(self):
if self.fhandle: self.fhandle.close()
if self.snpFileHandle: self.snpFileHandle.close()
def getSNPIterator(self):
if not self.type == 'b':
sys.stderr.write("Have only implemented this for binary plink files (bed)\n")
return
# get the number of snps
file = self.fbase + '.bim'
i = 0
f = open(file,'r')
for line in f: i += 1
f.close()
self.numSNPs = i
self.have_read = 0
self.snpFileHandle = open(file,'r')
self.BytestoRead = self.N / 4 + (self.N % 4 and 1 or 0)
self._formatStr = 'c'*self.BytestoRead
file = self.fbase + '.bed'
self.fhandle = open(file,'rb')
magicNumber = self.fhandle.read(2)
order = self.fhandle.read(1)
if not order == '\x01':
sys.stderr.write("This is not in SNP major order - you did not handle this case\n")
raise StopIteration
return self
def __iter__(self):
return self.getSNPIterator()
def next(self):
if self.have_read == self.numSNPs:
raise StopIteration
X = self.fhandle.read(self.BytestoRead)
XX = [bin(ord(x)) for x in struct.unpack(self._formatStr,X)]
self.have_read += 1
return self.formatBinaryGenotypes(XX,self.normGenotype),self.snpFileHandle.readline().strip().split()[1]
def formatBinaryGenotypes(self,X,norm=True):
D = { \
'00': 0.0, \
'10': 0.5, \
'11': 1.0, \
'01': np.nan \
}
D_tped = { \
'00': '1 1', \
'10': '1 2', \
'11': '2 2', \
'01': '0 0' \
}
#D = D_tped
G = []
for x in X:
if not len(x) == 10:
xx = x[2:]
x = '0b' + '0'*(8 - len(xx)) + xx
a,b,c,d = (x[8:],x[6:8],x[4:6],x[2:4])
L = [D[y] for y in [a,b,c,d]]
G += L
# only take the leading values because whatever is left should be null
G = G[:self.N]
G = np.array(G)
if norm:
G = self.normalizeGenotype(G)
return G
def normalizeGenotype(self,G):
x = True - np.isnan(G)
m = G[x].mean()
s = np.sqrt(G[x].var())
G[np.isnan(G)] = m
if s == 0: G = G - m
else: G = (G - m) / s
return G
def getPhenos(self,phenoFile=None):
if not phenoFile:
self.phenoFile = phenoFile = self.fbase+".phenos"
if not os.path.isfile(phenoFile):
sys.stderr.write("Could not find phenotype file: %s\n" % (phenoFile))
return
f = open(phenoFile,'r')
keys = []
P = []
for line in f:
v = line.strip().split()
keys.append((v[0],v[1]))
P.append([(x == 'NA' or x == '-9') and np.nan or float(x) for x in v[2:]])
f.close()
P = np.array(P)
# reorder to match self.indivs
D = {}
L = []
for i in range(len(keys)): D[keys[i]] = i
for i in range(len(self.indivs)):
if not D.has_key(self.indivs[i]):
continue
L.append(D[self.indivs[i]])
P = P[L,:]
self.phenos = P
return P
def getIndivs(self,base,type='b'):
if type == 't':
famFile = "%s.tfam" % base
else:
famFile = "%s.fam" % base
keys = []
i = 0
f = open(famFile,'r')
for line in f:
v = line.strip().split()
famId = v[0]
indivId = v[1]
k = (famId.strip(),indivId.strip())
keys.append(k)
i += 1
f.close()
self.N = len(keys)
sys.stderr.write("Read %d individuals from %s\n" % (self.N, famFile))
return keys
def readKinship(self,kFile):
# Assume the fastLMM style
# This will read in the kinship matrix and then reorder it
# according to self.indivs - additionally throwing out individuals
# that are not in both sets
if self.indivs == None or len(self.indivs) == 0:
sys.stderr.write("Did not read any individuals so can't load kinship\n")
return
sys.stderr.write("Reading kinship matrix from %s\n" % (kFile) )
f = open(kFile,'r')
# read indivs
v = f.readline().strip().split("\t")[1:]
keys = [tuple(y.split()) for y in v]
D = {}
for i in range(len(keys)): D[keys[i]] = i
# read matrix
K = []
for line in f:
K.append([float(x) for x in line.strip().split("\t")[1:]])
f.close()
K = np.array(K)
# reorder to match self.indivs
L = []
KK = []
X = []
for i in range(len(self.indivs)):
if not D.has_key(self.indivs[i]):
X.append(self.indivs[i])
else:
KK.append(self.indivs[i])
L.append(D[self.indivs[i]])
K = K[L,:][:,L]
self.indivs = KK
self.indivs_removed = X
if len(self.indivs_removed):
sys.stderr.write("Removed %d individuals that did not appear in Kinship\n" % (len(self.indivs_removed)))
return K
def getCovariates(self,covFile=None):
if not os.path.isfile(covFile):
sys.stderr.write("Could not find covariate file: %s\n" % (phenoFile))
return
f = open(covFile,'r')
keys = []
P = []
for line in f:
v = line.strip().split()
keys.append((v[0],v[1]))
P.append([x == 'NA' and np.nan or float(x) for x in v[2:]])
f.close()
P = np.array(P)
# reorder to match self.indivs
D = {}
L = []
for i in range(len(keys)):
D[keys[i]] = i
for i in range(len(self.indivs)):
if not D.has_key(self.indivs[i]): continue
L.append(D[self.indivs[i]])
P = P[L,:]
return P
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