diff options
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/input.py | 246 |
1 files changed, 246 insertions, 0 deletions
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/input.py b/wqflask/wqflask/my_pylmm/pyLMM/input.py new file mode 100644 index 00000000..33666a0d --- /dev/null +++ b/wqflask/wqflask/my_pylmm/pyLMM/input.py @@ -0,0 +1,246 @@ +# pylmm is a python-based linear mixed-model solver with applications to GWAS + +# Copyright (C) 2013 Nicholas A. Furlotte (nick.furlotte@gmail.com) +# +# This program is free software: you can redistribute it and/or modify +# it under the terms of the GNU Affero General Public License as +# published by the Free Software Foundation, either version 3 of the +# License, or (at your option) any later version. + +# This program is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU Affero General Public License for more details. + +# You should have received a copy of the GNU Affero General Public License +# along with this program. If not, see <http://www.gnu.org/licenses/>. + + +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.kFile = kFile + if self.readKFile: 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 + 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 \ No newline at end of file |