def encodeHomozygousData(raw_data=None):
gwas_data = gwas_core.CGWASDataHelper()
gwas_data.encodeHomozygousData(raw_data,raw_data.shape[1],raw_data.shape[0])
encoded = gwas_data.getEncodedData()
maf_data = gwas_data.getMAF()
gwas_data.releaseMemory()
return [encoded,maf_data]
def selectAlgorithm(self, algo_model=None):
if algo_model == "linear":
self.__ass = gwas_core.LinearRegression()
elif algo_model == "logit":
self.__ass = gwas_core.LogisticRegression()
elif algo_model == "MWUrt":
self.__ass = ranksum.RankSumAsso(test="MannWhitneyU")
elif algo_model == "WCrt":
self.__ass = ranksum.RankSumAsso(test="Wilcoxon")
elif algo_model == "ttest":
self.__ass = ranksum.RankSumAsso(test="ttest")
elif algo_model == "FaSTLMM":
self.__ass = gwas_core.FaSTLMM()
elif algo_model == "EMMAX":
self.__ass = gwas_core.EMMAX()
elif algo_model == "EMMAXperm":
self.__ass = gwas_core.EMMAX()
self.__permutation = True
elif algo_model == "linearperm":
self.__ass = gwas_core.LinearRegression()
self.__permutation = True
elif algo_model == "logitperm":
self.__ass = gwas_core.LogisticRegression()
self.__permutation = True
elif algo_model == "fisher":
self.__ass = fe.FisherExact()
self.__algorithm = algo_model
def encodeHeterozygousData(raw_data=None,snp_encoding="additive"):
gwas_data = gwas_core.CGWASDataHelper()
if snp_encoding=="recessive":
encoding = gwas_data.recessive
elif snp_encoding=="dominant":
encoding = gwas_data.dominant
elif snp_encoding=="codominant":
encoding = gwas_data.codominant
else:
encoding = gwas_data.additive
gwas_data.encodeHeterozygousData(raw_data,raw_data.shape[1],raw_data.shape[0],encoding)
encoded = gwas_data.getEncodedData()
maf_data = gwas_data.getMAF()
gwas_data.releaseMemory()
return [encoded,maf_data]
import sys
sys.path.append("bin/" + sys.platform + "/interfaces/python/")
import CEasyGWAS as gwas
import scipy as sp
means1 = sp.array([94, 98, 98, 94, 98, 96])
means2 = sp.array([92, 92, 88, 82, 88, 92])
sd1 = sp.array([22, 21, 28, 19, 21, 21])
sd2 = sp.array([20, 22, 26, 17, 22, 22])
n1 = sp.array([60, 65, 40, 200, 50, 85])
n2 = sp.array([60, 65, 40, 200, 45, 85])
mEffect = gwas.MeanEffectSize(means1, means2, sd1, sd2, n1, n2)
effects = mEffect.getHedgesG()
variance = mEffect.getVariance()
print effects, variance
tmp = sp.array([0.129297, 0.193972])
tmp1 = sp.array([0.265015, 0.341087])
random_model = gwas.RandomEffectModel(tmp, tmp1)
random_model.process()
weights = random_model.getWeights()
print weights
mean = random_model.getWeightedMean()
Z = random_model.getZvalue()
import sys
sys.path.append("bin/" + sys.platform + "/interfaces/python/")
import CEasyGWAS as gwas
import scipy as sp
#create dummy genotype wiht 100 sampkes and 10 SNPs.
#Just to demonstrate how to call some stuff in python
X = sp.random.randn(100, 10)
Y = sp.random.randn(100)
K = gwas.CKernels.realizedRelationshipKernel(X)
lmm = gwas.EMMAX()
lmm.setPhenotype(Y)
lmm.setGenotype(X)
lmm.setK(K)
lmm.test_associations()
print lmm.getPValues()
import sys
sys.path.append("bin/" + sys.platform + "/interfaces/python/")
import CEasyGWAS as gwas
import scipy as sp
#create dummy genotype wiht 100 sampkes and 10 SNPs.
#Just to demonstrate how to call some stuff in python
X = sp.random.randn(100,10)
Y = sp.random.randn(100)
linreg = gwas.LinearRegression()
linreg.setPhenotype(Y)
linreg.setGenotype(X)
linreg.test_associations()
print linreg.getPValues()
import sys
print "bin/" + sys.platform + "/interfaces/python/"
sys.path.append("bin/" + sys.platform + "/interfaces/python/")
import CEasyGWAS as gwas
import scipy as sp
means1 = sp.array([94, 98, 98, 94, 98, 96])
means2 = sp.array([92, 92, 88, 82, 88, 92])
sd1 = sp.array([22, 21, 28, 19, 21, 21])
sd2 = sp.array([20, 22, 26, 17, 22, 22])
n1 = sp.array([ 60, 65, 40, 200, 50, 85])
n2 = sp.array([ 60, 65, 40, 200, 45, 85])
mEffect = gwas.MeanEffectSize(means1,means2,sd1,sd2,n1,n2)
effects = mEffect.getHedgesG()
variance = mEffect.getVariance()
print effects, variance
fixed_model = gwas.FixedEffectModel(effects,variance)
fixed_model.process()
weights = fixed_model.getWeights()
print weights
mean = fixed_model.getWeightedMean()
Z = fixed_model.getZvalue()