def marginal_genecorr(pfile, gfile, startTraitIdx=0, nTraits=np.inf):
"""
running marginal gene-gene correlations
Input:
pfile : phenotype file
cfile : covariance matrix file
ffile : covariates file
gfile : basename of output file
startTraidIdx : first trait to be analyses
nTraits : number of traits to be analysed
"""
if np.isfinite(nTraits): gfile += ".startTrait_%d" % startTraitIdx
preader = phenoReaderFile.PhenoReaderFile(pfile)
model = gnetlmm.GNetLMM(preader, None)
corr, pv = model.marginal_gene_correlations(startTraitIdx=startTraitIdx,
nTraits=nTraits)
write = writer.Writer(gfile + '.pv')
write.writeMatrix(pv, fmt='%.4e')
write = writer.Writer(gfile + '.corr')
write.writeMatrix(corr, fmt='%.4f')
def simPheno(options):
assert options.bfile!=None, 'Please specify a bfile.'
assert options.pfile!=None, 'Please specify a pfile.'
sp.random.seed(options.seed)
print 'simulating genes'
if options.networkDesign=='star':
sim = simulator.HotSpotSimulator(options.bfile)
elif options.networkDesign=='sparse':
sim = simulator.SparseSimulator(options.bfile)
else:
raise Exception("networkDesign '%s' is not known."%options.networkDesign)
RV = sim.simulateGenes(T=options.T,varSnp=options.varSnp,varNetwork=options.varNetwork,expN=options.expN,
alpha=options.alpha,nConfounder=options.nConfounder,confPerGene=options.confPerGene)
print 'exporting simulated data'
outdir = os.path.split(options.pfile)[0]
if not(os.path.exists(outdir)): os.mkdir(outdir)
np.savetxt('%s.Aconf'%options.pfile, RV['Aconf'], fmt='%d')
np.savetxt('%s.Agene'%options.pfile, RV['Agene'], fmt='%d')
np.savetxt('%s.conf'%options.pfile, RV['H'], fmt='%.6f')
write = writer.Writer(options.pfile)
write.writeColumnInfo(data={'fid':sim.genoreader.fam[:,0]})
write.writeRowInfo(data={'gene_ids':RV['gene_ids'], 'gene_chrom':RV['gene_chrom'], 'gene_start':RV['gene_start'],
'causal_rs':RV['rs']})
write.writeMatrix(RV['Y'], fmt='%.6f')
def marginal_genecorr(bfile, pfile, gfile):
"""
running marginal gene-gene correlations
Input:
bfile : binary bed file (bfile.bed, bfile.bim and bfile.fam are required)
pfile : phenotype file
cfile : covariance matrix file
ffile : covariates file
gfile : basename of output file
"""
preader = phenoReader.PhenoReaderFile(pfile)
greader = bedReader.BedReader(bfile)
model = gnetlmm.GNetLMM(preader, greader)
corr, pv = model.marginal_gene_correlations()
write = writer.Writer(gfile + '.pv')
write.writeMatrix(pv, fmt='%.4e')
write = writer.Writer(gfile + '.corr')
write.writeMatrix(corr, fmt='%.4f')
def initial_scan(bfile,
pfile,
cfile,
ffile,
assoc0file,
startSnpIdx=0,
nSnps=np.inf,
memory_efficient=False):
"""
running initial scan using a standard linear mixed model
Input:
bfile : binary bed file (bfile.bed, bfile.bim and bfile.fam are required)
pfile : phenotype file
cfile : covariance matrix file
ffile : covariates file
assoc0file : basename of output file
"""
K = None
if cfile is not None:
K = np.loadtxt(cfile)
Covs = None
if ffile is not None:
Covs = np.loadtxt(ffile)
if np.isfinite(nSnps): assoc0file += ".startSnp_%d" % startSnpIdx
preader = phenoReaderFile.PhenoReaderFile(pfile)
greader = bedReader.BedReader(bfile)
model = gnetlmm.GNetLMM(preader, greader, K=K, Covs=Covs)
beta0, pv0 = model.initial_scan(startSnpIdx, nSnps, memory_efficient)
write = writer.Writer(assoc0file + '.pv')
write.writeMatrix(pv0, fmt='%.4e')
write = writer.Writer(assoc0file + '.beta')
write.writeMatrix(beta0, fmt='%.4f')