diff options
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/gwas.py | 99 | ||||
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/lmm.py | 44 | ||||
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/lmm2.py | 405 | ||||
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/phenotype.py | 2 | ||||
| -rw-r--r-- | wqflask/wqflask/my_pylmm/pyLMM/runlmm.py | 26 |
5 files changed, 490 insertions, 86 deletions
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/gwas.py b/wqflask/wqflask/my_pylmm/pyLMM/gwas.py index 52455014..b9d5db61 100644 --- a/wqflask/wqflask/my_pylmm/pyLMM/gwas.py +++ b/wqflask/wqflask/my_pylmm/pyLMM/gwas.py @@ -21,79 +21,30 @@ import pdb import time import sys # from utility import temp_data -import lmm - +import lmm2 import os import numpy as np import input from optmatrix import matrix_initialize -# from lmm import LMM +from lmm2 import LMM2 import multiprocessing as mp # Multiprocessing is part of the Python stdlib import Queue -def compute_snp(j,snp_ids,q = None): - # print(j,len(snp_ids),"\n") +def formatResult(id,beta,betaSD,ts,ps): + return "\t".join([str(x) for x in [id,beta,betaSD,ts,ps]]) + "\n" + +def compute_snp(j,n,snp_ids,lmm2,REML,q = None): + # print(j,snp_ids,"\n") result = [] for snp_id in snp_ids: - # j,snp_id = collect snp,id = snp_id - # id = collect[1] - # result = [] - # Check SNPs for missing values - x = snp[keep].reshape((n,1)) # all the SNPs - v = np.isnan(x).reshape((-1,)) - if v.sum(): - # NOTE: this code appears to be unreachable! - if verbose: - sys.stderr.write("Found missing values in "+str(x)) - keeps = True - v - xs = x[keeps,:] - if keeps.sum() <= 1 or xs.var() <= 1e-6: - # PS.append(np.nan) - # TS.append(np.nan) - # result.append(formatResult(id,np.nan,np.nan,np.nan,np.nan)) - # continue - result.append(formatResult(id,np.nan,np.nan,np.nan,np.nan)) - continue - - # Its ok to center the genotype - I used normalizeGenotype to - # force the removal of missing genotypes as opposed to replacing them with MAF. - if not normalizeGenotype: - xs = (xs - xs.mean()) / np.sqrt(xs.var()) - Ys = Y[keeps] - X0s = X0[keeps,:] - Ks = K[keeps,:][:,keeps] - if kfile2: - K2s = K2[keeps,:][:,keeps] - Ls = LMM_withK2(Ys,Ks,X0=X0s,verbose=verbose,K2=K2s) - else: - Ls = LMM(Ys,Ks,X0=X0s,verbose=verbose) - if refit: - Ls.fit(X=xs,REML=REML) - else: - #try: - Ls.fit(REML=REML) - #except: pdb.set_trace() - ts,ps,beta,betaVar = Ls.association(xs,REML=REML,returnBeta=True) - else: - if x.var() == 0: - # Note: this code appears to be unreachable! - - # PS.append(np.nan) - # TS.append(np.nan) - # result.append(formatResult(id,np.nan,np.nan,np.nan,np.nan)) # writes nan values - result.append(formatResult(id,np.nan,np.nan,np.nan,np.nan)) - continue - - if refit: - L.fit(X=x,REML=REML) - # This is where it happens - ts,ps,beta,betaVar = L.association(x,REML=REML,returnBeta=True) + x = snp.reshape((n,1)) # all the SNPs + # if refit: + # L.fit(X=snp,REML=REML) + ts,ps,beta,betaVar = lmm2.association(x,REML=REML,returnBeta=True) result.append(formatResult(id,beta,np.sqrt(betaVar).sum(),ts,ps)) - # compute_snp.q.put([j,formatResult(id,beta,np.sqrt(betaVar).sum(),ts,ps)]) - # print [j,result[0]]," in result queue\n" if not q: q = compute_snp.q q.put([j,result]) @@ -113,8 +64,9 @@ def gwas(Y,G,K,restricted_max_likelihood=True,refit=False,verbose=True): """ matrix_initialize() cpu_num = mp.cpu_count() - numThreads = 1 + numThreads = None kfile2 = False + reml = restricted_max_likelihood sys.stderr.write(str(G.shape)+"\n") n = G.shape[1] # inds @@ -123,17 +75,17 @@ def gwas(Y,G,K,restricted_max_likelihood=True,refit=False,verbose=True): snps = m sys.stderr.write(str(m)+" SNPs\n") # print "***** GWAS: G",G.shape,G - assert m>n, "n should be larger than m (snps>inds)" + assert snps>inds, "snps should be larger than inds (snps=%d,inds=%d)" % (snps,inds) # CREATE LMM object for association # if not kfile2: L = LMM(Y,K,Kva,Kve,X0,verbose=verbose) # else: L = LMM_withK2(Y,K,Kva,Kve,X0,verbose=verbose,K2=K2) - runlmm = lmm.LMM(Y,K) # ,Kva,Kve,X0,verbose=verbose) + lmm2 = LMM2(Y,K) # ,Kva,Kve,X0,verbose=verbose) if not refit: if verbose: sys.stderr.write("Computing fit for null model\n") - runlmm.fit() # follow GN model in run_other - if verbose: sys.stderr.write("\t heritability=%0.3f, sigma=%0.3f\n" % (runlmm.optH,runlmm.optSigma)) + lmm2.fit() # follow GN model in run_other + if verbose: sys.stderr.write("\t heritability=%0.3f, sigma=%0.3f\n" % (lmm2.optH,lmm2.optSigma)) # outFile = "test.out" # out = open(outFile,'w') @@ -142,8 +94,6 @@ def gwas(Y,G,K,restricted_max_likelihood=True,refit=False,verbose=True): def outputResult(id,beta,betaSD,ts,ps): out.write(formatResult(id,beta,betaSD,ts,ps)) def printOutHead(): out.write("\t".join(["SNP_ID","BETA","BETA_SD","F_STAT","P_VALUE"]) + "\n") - def formatResult(id,beta,betaSD,ts,ps): - return "\t".join([str(x) for x in [id,beta,betaSD,ts,ps]]) + "\n" printOutHead() @@ -162,15 +112,15 @@ def gwas(Y,G,K,restricted_max_likelihood=True,refit=False,verbose=True): last_j = 0 # for snp_id in G: for snp in G: - print snp.shape,snp - snp_id = ('SNPID',snp) + snp_id = (snp,'SNPID') count += 1 if count % 1000 == 0: job = count/1000 if verbose: sys.stderr.write("Job %d At SNP %d\n" % (job,count)) if numThreads == 1: - compute_snp(job,collect,q) + print "Running on 1 THREAD" + compute_snp(job,n,collect,lmm2,reml,q) collect = [] j,lines = q.get() if verbose: @@ -178,7 +128,7 @@ def gwas(Y,G,K,restricted_max_likelihood=True,refit=False,verbose=True): for line in lines: out.write(line) else: - p.apply_async(compute_snp,(job,collect)) + p.apply_async(compute_snp,(job,n,collect,lmm2,reml)) collect = [] while job > completed: try: @@ -205,6 +155,13 @@ def gwas(Y,G,K,restricted_max_likelihood=True,refit=False,verbose=True): sys.stderr.write("Job "+str(j)+" finished\n") for line in lines: out.write(line) + else: + print "Running on 1 THREAD" + # print collect + compute_snp(count/1000,n,collect,lmm2,reml,q) + j,lines = q.get() + for line in lines: + out.write(line) # print collect ps = [item[1][3] for item in collect] diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py index 8c6d3c3c..c42f9fb7 100644 --- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py +++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py @@ -51,6 +51,7 @@ from utility.benchmark import Bench from utility import temp_data from kinship import kinship, kinship_full, kvakve import genotype +import phenotype import gwas try: @@ -315,32 +316,45 @@ def run_other_new(pheno_vector, print("In run_other (new)") print("REML=",restricted_max_likelihood," REFIT=",refit) + + # Adjust phenotypes + Y,G,keep = phenotype.remove_missing(pheno_vector,genotype_matrix,verbose=True) + print("Removed missing phenotypes",Y.shape) + # if options.maf_normalization: + # G = np.apply_along_axis( genotype.replace_missing_with_MAF, axis=0, arr=g ) + # print "MAF replacements: \n",G + # if not options.skip_genotype_normalization: + # G = np.apply_along_axis( genotype.normalize, axis=1, arr=G) + with Bench("Calculate Kinship"): - kinship_matrix,genotype_matrix = calculate_kinship(genotype_matrix, tempdata) + K,G = calculate_kinship(G, tempdata) - print("kinship_matrix: ", pf(kinship_matrix)) - print("kinship_matrix.shape: ", pf(kinship_matrix.shape)) + print("kinship_matrix: ", pf(K)) + print("kinship_matrix.shape: ", pf(K.shape)) - with Bench("Create LMM object"): - lmm_ob = LMM(pheno_vector, kinship_matrix) - with Bench("LMM_ob fitting"): - lmm_ob.fit() + # with Bench("Create LMM object"): + # lmm_ob = lmm2.LMM2(Y,K) + # with Bench("LMM_ob fitting"): + # lmm_ob.fit() - print("genotype_matrix: ", genotype_matrix.shape) - print(genotype_matrix) + print("genotype_matrix: ", G.shape) + print(G) with Bench("Doing GWAS"): - t_stats, p_values = gwas.gwas(pheno_vector, - genotype_matrix.T, - kinship_matrix, + t_stats, p_values = gwas.gwas(Y, + G.T, + K, restricted_max_likelihood=True, refit=False,verbose=True) Bench().report() return p_values, t_stats -run_other = run_other_old +run_other = run_other_new def matrixMult(A,B): + return np.dot(A,B) + +def matrixMult_old(A,B): # If there is no fblas then we will revert to np.dot() @@ -674,11 +688,15 @@ class LMM: optimum. """ + print("H=",H) + print("X=",X) + print("REML=",REML) n = len(self.LLs) HOpt = [] for i in range(1,n-2): if self.LLs[i-1] < self.LLs[i] and self.LLs[i] > self.LLs[i+1]: HOpt.append(optimize.brent(self.LL_brent,args=(X,REML),brack=(H[i-1],H[i+1]))) + print("HOpt=",HOpt) if np.isnan(HOpt[-1][0]): HOpt[-1][0] = [self.LLs[i-1]] diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm2.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm2.py new file mode 100644 index 00000000..cba47a9b --- /dev/null +++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm2.py @@ -0,0 +1,405 @@ +# pylmm is a python-based linear mixed-model solver with applications to GWAS + +# Copyright (C) 2013,2014 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 sys +import time +import numpy as np +from scipy.linalg import eigh, inv, det +import scipy.stats as stats # t-tests +from scipy import optimize +from optmatrix import matrixMult + +def calculateKinship(W,center=False): + """ + W is an n x m matrix encoding SNP minor alleles. + + This function takes a matrix oF SNPs, imputes missing values with the maf, + normalizes the resulting vectors and returns the RRM matrix. + """ + n = W.shape[0] + m = W.shape[1] + keep = [] + for i in range(m): + mn = W[True - np.isnan(W[:,i]),i].mean() + W[np.isnan(W[:,i]),i] = mn + vr = W[:,i].var() + if vr == 0: continue + + keep.append(i) + W[:,i] = (W[:,i] - mn) / np.sqrt(vr) + + W = W[:,keep] + K = matrixMult(W,W.T) * 1.0/float(m) + if center: + P = np.diag(np.repeat(1,n)) - 1/float(n) * np.ones((n,n)) + S = np.trace(matrixMult(matrixMult(P,K),P)) + K_n = (n - 1)*K / S + return K_n + return K + +def GWAS(Y, X, K, Kva=[], Kve=[], X0=None, REML=True, refit=False): + """ + + Performs a basic GWAS scan using the LMM. This function + uses the LMM module to assess association at each SNP and + does some simple cleanup, such as removing missing individuals + per SNP and re-computing the eigen-decomp + + Y - n x 1 phenotype vector + X - n x m SNP matrix (genotype matrix) + K - n x n kinship matrix + Kva,Kve = linalg.eigh(K) - or the eigen vectors and values for K + X0 - n x q covariate matrix + REML - use restricted maximum likelihood + refit - refit the variance component for each SNP + + """ + n = X.shape[0] + m = X.shape[1] + prins("Initialize GWAS") + print("genotype matrix n is:", n) + print("genotype matrix m is:", m) + + if X0 == None: + X0 = np.ones((n,1)) + + # Remove missing values in Y and adjust associated parameters + v = np.isnan(Y) + if v.sum(): + keep = True - v + keep = keep.reshape((-1,)) + Y = Y[keep] + X = X[keep,:] + X0 = X0[keep,:] + K = K[keep,:][:,keep] + Kva = [] + Kve = [] + + if len(Y) == 0: + return np.ones(m)*np.nan,np.ones(m)*np.nan + + L = LMM(Y,K,Kva,Kve,X0) + if not refit: L.fit() + + PS = [] + TS = [] + + n = X.shape[0] + m = X.shape[1] + + for i in range(m): + x = X[:,i].reshape((n,1)) + v = np.isnan(x).reshape((-1,)) + if v.sum(): + keep = True - v + xs = x[keep,:] + if xs.var() == 0: + PS.append(np.nan) + TS.append(np.nan) + continue + + Ys = Y[keep] + X0s = X0[keep,:] + Ks = K[keep,:][:,keep] + Ls = LMM(Ys,Ks,X0=X0s) + if refit: + Ls.fit(X=xs) + else: + Ls.fit() + ts,ps = Ls.association(xs,REML=REML) + else: + if x.var() == 0: + PS.append(np.nan) + TS.append(np.nan) + continue + + if refit: + L.fit(X=x) + ts,ps = L.association(x,REML=REML) + + PS.append(ps) + TS.append(ts) + + return TS,PS + +class LMM2: + + """ + This is a simple version of EMMA/fastLMM. + The main purpose of this module is to take a phenotype vector (Y), a set of covariates (X) and a kinship matrix (K) + and to optimize this model by finding the maximum-likelihood estimates for the model parameters. + There are three model parameters: heritability (h), covariate coefficients (beta) and the total + phenotypic variance (sigma). + Heritability as defined here is the proportion of the total variance (sigma) that is attributed to + the kinship matrix. + + For simplicity, we assume that everything being input is a numpy array. + If this is not the case, the module may throw an error as conversion from list to numpy array + is not done consistently. + + """ + def __init__(self,Y,K,Kva=[],Kve=[],X0=None,verbose=False): + + """ + The constructor takes a phenotype vector or array Y of size n. + It takes a kinship matrix K of size n x n. Kva and Kve can be computed as Kva,Kve = linalg.eigh(K) and cached. + If they are not provided, the constructor will calculate them. + X0 is an optional covariate matrix of size n x q, where there are q covariates. + When this parameter is not provided, the constructor will set X0 to an n x 1 matrix of all ones to represent a mean effect. + """ + + if X0 == None: + X0 = np.ones(len(Y)).reshape(len(Y),1) + self.verbose = verbose + + x = True - np.isnan(Y) + x = x.reshape(-1,) + if not x.sum() == len(Y): + if self.verbose: sys.stderr.write("Removing %d missing values from Y\n" % ((True - x).sum())) + Y = Y[x] + K = K[x,:][:,x] + X0 = X0[x,:] + Kva = [] + Kve = [] + self.nonmissing = x + + if len(Kva) == 0 or len(Kve) == 0: + if self.verbose: sys.stderr.write("Obtaining eigendecomposition for %dx%d matrix\n" % (K.shape[0],K.shape[1]) ) + begin = time.time() + Kva,Kve = eigh(K) + end = time.time() + if self.verbose: sys.stderr.write("Total time: %0.3f\n" % (end - begin)) + + self.K = K + self.Kva = Kva + self.Kve = Kve + self.N = self.K.shape[0] + self.Y = Y.reshape((self.N,1)) + self.X0 = X0 + + if sum(self.Kva < 1e-6): + if self.verbose: sys.stderr.write("Cleaning %d eigen values\n" % (sum(self.Kva < 0))) + self.Kva[self.Kva < 1e-6] = 1e-6 + + self.transform() + + def transform(self): + + """ + Computes a transformation on the phenotype vector and the covariate matrix. + The transformation is obtained by left multiplying each parameter by the transpose of the + eigenvector matrix of K (the kinship). + """ + + self.Yt = matrixMult(self.Kve.T, self.Y) + self.X0t = matrixMult(self.Kve.T, self.X0) + self.X0t_stack = np.hstack([self.X0t, np.ones((self.N,1))]) + self.q = self.X0t.shape[1] + + def getMLSoln(self,h,X): + + """ + Obtains the maximum-likelihood estimates for the covariate coefficients (beta), + the total variance of the trait (sigma) and also passes intermediates that can + be utilized in other functions. The input parameter h is a value between 0 and 1 and represents + the heritability or the proportion of the total variance attributed to genetics. The X is the + covariate matrix. + """ + + S = 1.0/(h*self.Kva + (1.0 - h)) + Xt = X.T*S + XX = matrixMult(Xt,X) + XX_i = inv(XX) + beta = matrixMult(matrixMult(XX_i,Xt),self.Yt) + Yt = self.Yt - matrixMult(X,beta) + Q = np.dot(Yt.T*S,Yt) + sigma = Q * 1.0 / (float(self.N) - float(X.shape[1])) + return beta,sigma,Q,XX_i,XX + + def LL_brent(self,h,X=None,REML=False): + #brent will not be bounded by the specified bracket. + # I return a large number if we encounter h < 0 to avoid errors in LL computation during the search. + if h < 0: return 1e6 + return -self.LL(h,X,stack=False,REML=REML)[0] + + def LL(self,h,X=None,stack=True,REML=False): + + """ + Computes the log-likelihood for a given heritability (h). If X==None, then the + default X0t will be used. If X is set and stack=True, then X0t will be matrix concatenated with + the input X. If stack is false, then X is used in place of X0t in the LL calculation. + REML is computed by adding additional terms to the standard LL and can be computed by setting REML=True. + """ + + if X == None: X = self.X0t + elif stack: + self.X0t_stack[:,(self.q)] = matrixMult(self.Kve.T,X)[:,0] + X = self.X0t_stack + + n = float(self.N) + q = float(X.shape[1]) + beta,sigma,Q,XX_i,XX = self.getMLSoln(h,X) + LL = n*np.log(2*np.pi) + np.log(h*self.Kva + (1.0-h)).sum() + n + n*np.log(1.0/n * Q) + LL = -0.5 * LL + + if REML: + LL_REML_part = q*np.log(2.0*np.pi*sigma) + np.log(det(matrixMult(X.T,X))) - np.log(det(XX)) + LL = LL + 0.5*LL_REML_part + + + LL = LL.sum() + return LL,beta,sigma,XX_i + + def getMax(self,H, X=None,REML=False): + + """ + Helper functions for .fit(...). + This function takes a set of LLs computed over a grid and finds possible regions + containing a maximum. Within these regions, a Brent search is performed to find the + optimum. + + """ + n = len(self.LLs) + HOpt = [] + for i in range(1,n-2): + if self.LLs[i-1] < self.LLs[i] and self.LLs[i] > self.LLs[i+1]: + HOpt.append(optimize.brent(self.LL_brent,args=(X,REML),brack=(H[i-1],H[i+1]))) + if np.isnan(HOpt[-1]): HOpt[-1] = H[i-1] + #if np.isnan(HOpt[-1]): HOpt[-1] = self.LLs[i-1] + #if np.isnan(HOpt[-1][0]): HOpt[-1][0] = [self.LLs[i-1]] + + if len(HOpt) > 1: + if self.verbose: sys.stderr.write("NOTE: Found multiple optima. Returning first...\n") + return HOpt[0] + elif len(HOpt) == 1: return HOpt[0] + elif self.LLs[0] > self.LLs[n-1]: return H[0] + else: return H[n-1] + + + def fit(self,X=None,ngrids=100,REML=True): + + """ + Finds the maximum-likelihood solution for the heritability (h) given the current parameters. + X can be passed and will transformed and concatenated to X0t. Otherwise, X0t is used as + the covariate matrix. + + This function calculates the LLs over a grid and then uses .getMax(...) to find the optimum. + Given this optimum, the function computes the LL and associated ML solutions. + """ + + if X == None: X = self.X0t + else: + #X = np.hstack([self.X0t,matrixMult(self.Kve.T, X)]) + self.X0t_stack[:,(self.q)] = matrixMult(self.Kve.T,X)[:,0] + X = self.X0t_stack + + H = np.array(range(ngrids)) / float(ngrids) + L = np.array([self.LL(h,X,stack=False,REML=REML)[0] for h in H]) + self.LLs = L + + hmax = self.getMax(H,X,REML) + L,beta,sigma,betaSTDERR = self.LL(hmax,X,stack=False,REML=REML) + + self.H = H + self.optH = hmax.sum() + self.optLL = L + self.optBeta = beta + self.optSigma = sigma.sum() + + return hmax,beta,sigma,L + + def association(self,X,h=None,stack=True,REML=True,returnBeta=False): + """ + Calculates association statitics for the SNPs encoded in the vector X of size n. + If h == None, the optimal h stored in optH is used. + + """ + if False: + print "X=",X + print "h=",h + print "q=",self.q + print "self.Kve=",self.Kve + print "X0t_stack=",self.X0t_stack.shape,self.X0t_stack + + if stack: + # X = np.hstack([self.X0t,matrixMult(self.Kve.T, X)]) + m = matrixMult(self.Kve.T,X) + # print "m=",m + m = m[:,0] + self.X0t_stack[:,(self.q)] = m + X = self.X0t_stack + + if h == None: h = self.optH + + L,beta,sigma,betaVAR = self.LL(h,X,stack=False,REML=REML) + q = len(beta) + ts,ps = self.tstat(beta[q-1],betaVAR[q-1,q-1],sigma,q) + + if returnBeta: return ts,ps,beta[q-1].sum(),betaVAR[q-1,q-1].sum()*sigma + return ts,ps + + def tstat(self,beta,var,sigma,q,log=False): + + """ + Calculates a t-statistic and associated p-value given the estimate of beta and its standard error. + This is actually an F-test, but when only one hypothesis is being performed, it reduces to a t-test. + """ + + ts = beta / np.sqrt(var * sigma) + #ps = 2.0*(1.0 - stats.t.cdf(np.abs(ts), self.N-q)) + # sf == survival function - this is more accurate -- could also use logsf if the precision is not good enough + if log: + ps = 2.0 + (stats.t.logsf(np.abs(ts), self.N-q)) + else: + ps = 2.0*(stats.t.sf(np.abs(ts), self.N-q)) + if not len(ts) == 1 or not len(ps) == 1: + raise Exception("Something bad happened :(") + return ts.sum(),ps.sum() + + def plotFit(self,color='b-',title=''): + + """ + Simple function to visualize the likelihood space. It takes the LLs + calcualted over a grid and normalizes them by subtracting off the mean and exponentiating. + The resulting "probabilities" are normalized to one and plotted against heritability. + This can be seen as an approximation to the posterior distribuiton of heritability. + + For diagnostic purposes this lets you see if there is one distinct maximum or multiple + and what the variance of the parameter looks like. + """ + import matplotlib.pyplot as pl + + mx = self.LLs.max() + p = np.exp(self.LLs - mx) + p = p/p.sum() + + pl.plot(self.H,p,color) + pl.xlabel("Heritability") + pl.ylabel("Probability of data") + pl.title(title) + + def meanAndVar(self): + + mx = self.LLs.max() + p = np.exp(self.LLs - mx) + p = p/p.sum() + + mn = (self.H * p).sum() + vx = ((self.H - mn)**2 * p).sum() + + return mn,vx + diff --git a/wqflask/wqflask/my_pylmm/pyLMM/phenotype.py b/wqflask/wqflask/my_pylmm/pyLMM/phenotype.py index bb620052..682ba371 100644 --- a/wqflask/wqflask/my_pylmm/pyLMM/phenotype.py +++ b/wqflask/wqflask/my_pylmm/pyLMM/phenotype.py @@ -36,5 +36,5 @@ def remove_missing(y,g,verbose=False): sys.stderr.write("runlmm.py: Cleaning the phenotype vector and genotype matrix by removing %d individuals...\n" % (v.sum())) y1 = y[keep] g1 = g[keep,:] - return y1,g1 + return y1,g1,keep diff --git a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py index f17f1bd1..4268f3be 100644 --- a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py +++ b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py @@ -99,13 +99,37 @@ if options.geno: g = tsvreader.geno(options.geno) print g.shape +if cmd == 'redis_new': + # Emulating the redis setup of GN2 + Y = y + G = g + print "Original G",G.shape, "\n", G + + gt = G.T + G = None + ps, ts = gn2_load_redis('testrun','other',k,Y,gt) + print np.array(ps) + print sum(ps) + # Test results + p1 = round(ps[0],4) + p2 = round(ps[-1],4) + sys.stderr.write(options.geno+"\n") + if options.geno == 'data/small.geno': + assert p1==0.0708, "p1=%f" % p1 + assert p2==0.1417, "p2=%f" % p2 + if options.geno == 'data/small_na.geno': + assert p1==0.0958, "p1=%f" % p1 + assert p2==0.0435, "p2=%f" % p2 + if options.geno == 'data/test8000.geno': + assert p1==0.8984, "p1=%f" % p1 + assert p2==0.9623, "p2=%f" % p2 if cmd == 'redis': # Emulating the redis setup of GN2 G = g print "Original G",G.shape, "\n", G if y != None: gnt = np.array(g).T - Y,g = phenotype.remove_missing(y,g.T,options.verbose) + Y,g,keep = phenotype.remove_missing(y,g.T,options.verbose) G = g.T print "Removed missing phenotypes",G.shape, "\n", G if options.maf_normalization: |