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-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/gwas.py99
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm.py44
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm2.py405
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/phenotype.py2
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/runlmm.py26
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:
generated by cgit v1.2.3 (git 2.44.0) at 2024-11-24 21:00:47 +0000