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()