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-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/kinship.py136
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm.py4
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/optmatrix.py2
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/runlmm.py59
4 files changed, 177 insertions, 24 deletions
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/kinship.py b/wqflask/wqflask/my_pylmm/pyLMM/kinship.py
new file mode 100644
index 00000000..dc2d717d
--- /dev/null
+++ b/wqflask/wqflask/my_pylmm/pyLMM/kinship.py
@@ -0,0 +1,136 @@
+# pylmm kinship calculation
+#
+# Copyright (C) 2013 Nicholas A. Furlotte (nick.furlotte@gmail.com)
+# Copyright (C) 2015 Pjotr Prins (pjotr.prins@thebird.nl)
+#
+# 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/>.
+
+# env PYTHONPATH=$pylmm_lib_path:./lib python $pylmm_lib_path/runlmm.py --pheno test.pheno --geno test9000.geno kinship --test
+
+import sys
+import os
+import numpy as np
+import multiprocessing as mp # Multiprocessing is part of the Python stdlib
+import Queue
+
+from optmatrix import matrix_initialize, matrixMultT
+
+
+def compute_W(job,G,n,compute_size):
+ """
+ Read 1000 SNPs at a time into matrix and return the result
+ """
+ W = np.ones((n,compute_size)) * np.nan # W matrix has dimensions individuals x SNPs (initially all NaNs)
+ for j in range(0,compute_size):
+ row = job*compute_size + j
+ if row >= compute_size:
+ W = W[:,range(0,j)]
+ break
+ snp = G[job*compute_size+j]
+ # print snp.shape,snp
+ if snp.var() == 0:
+ continue
+ W[:,j] = snp # set row to list of SNPs
+ return W
+
+def compute_matrixMult(job,W,q = None):
+ """
+ Compute Kinship(W)*j
+
+ For every set of SNPs matrixMult is used to multiply matrices T(W)*W
+ """
+ res = matrixMultT(W)
+ if not q: q=compute_matrixMult.q
+ q.put([job,res])
+ return job
+
+def f_init(q):
+ compute_matrixMult.q = q
+
+# Calculate the kinship matrix from G (SNPs as rows!), returns K
+#
+def kinship(G,options):
+ numThreads = None
+ if options.numThreads:
+ numThreads = int(options.numThreads)
+ options.computeSize = 1000
+ matrix_initialize(options.useBLAS)
+
+ sys.stderr.write(str(G.shape)+"\n")
+ n = G.shape[1] # inds
+ inds = n
+ m = G.shape[0] # snps
+ snps = m
+ sys.stderr.write(str(m)+" SNPs\n")
+
+ q = mp.Queue()
+ p = mp.Pool(numThreads, f_init, [q])
+ iterations = snps/options.computeSize+1
+ if options.testing:
+ iterations = 8
+ # jobs = range(0,8) # range(0,iterations)
+
+ results = []
+
+ K = np.zeros((n,n)) # The Kinship matrix has dimension individuals x individuals
+
+ completed = 0
+ for job in range(iterations):
+ if options.verbose:
+ sys.stderr.write("Processing job %d first %d SNPs\n" % (job, ((job+1)*options.computeSize)))
+ W = compute_W(job,G,n,options.computeSize)
+ if numThreads == 1:
+ compute_matrixMult(job,W,q)
+ j,x = q.get()
+ if options.verbose: sys.stderr.write("Job "+str(j)+" finished\n")
+ K_j = x
+ # print j,K_j[:,0]
+ K = K + K_j
+ else:
+ results.append(p.apply_async(compute_matrixMult, (job,W)))
+ # Do we have a result?
+ try:
+ j,x = q.get_nowait()
+ if options.verbose: sys.stderr.write("Job "+str(j)+" finished\n")
+ K_j = x
+ # print j,K_j[:,0]
+ K = K + K_j
+ completed += 1
+ except Queue.Empty:
+ pass
+
+ if numThreads == None or numThreads > 1:
+ for job in range(len(results)-completed):
+ j,x = q.get(True,15)
+ if options.verbose: sys.stderr.write("Job "+str(j)+" finished\n")
+ K_j = x
+ # print j,K_j[:,0]
+ K = K + K_j
+
+ print K.shape,K
+ K = K / float(snps)
+ outFile = 'runtest.kin'
+ if options.verbose: sys.stderr.write("Saving Kinship file to %s\n" % outFile)
+ np.savetxt(outFile,K)
+
+ if options.saveKvaKve:
+ if options.verbose: sys.stderr.write("Obtaining Eigendecomposition\n")
+ Kva,Kve = linalg.eigh(K)
+ if options.verbose: sys.stderr.write("Saving eigendecomposition to %s.[kva | kve]\n" % outFile)
+ np.savetxt(outFile+".kva",Kva)
+ np.savetxt(outFile+".kve",Kve)
+
+
+
+
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index 58d7593d..65b989a8 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -406,6 +406,8 @@ def GWAS(pheno_vector,
v = np.isnan(pheno_vector)
if v.sum():
keep = True - v
+ print(pheno_vector.shape,pheno_vector)
+ print(keep.shape,keep)
pheno_vector = pheno_vector[keep]
#genotype_matrix = genotype_matrix[keep,:]
#covariate_matrix = covariate_matrix[keep,:]
@@ -437,6 +439,8 @@ def GWAS(pheno_vector,
p_values.append(0)
t_statistics.append(np.nan)
continue
+
+ print(genotype_matrix.shape,pheno_vector.shape,keep.shape)
pheno_vector = pheno_vector[keep]
covariate_matrix = covariate_matrix[keep,:]
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/optmatrix.py b/wqflask/wqflask/my_pylmm/pyLMM/optmatrix.py
index abb72081..5c71db6a 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/optmatrix.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/optmatrix.py
@@ -9,7 +9,7 @@ from scipy import stats
useNumpy = None
hasBLAS = None
-def matrix_initialize(useBLAS):
+def matrix_initialize(useBLAS=True):
global useNumpy # module based variable
if useBLAS and useNumpy == None:
print get_info('blas_opt')
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
index 738268be..bd6c55fc 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
@@ -22,17 +22,19 @@ import sys
import tsvreader
import numpy as np
from lmm import gn2_load_redis
+from kinship import kinship
usage = """
python runlmm.py [options] command
runlmm.py processing multiplexer reads standardised input formats
- and calls the different routines
+ and calls the different routines (writes to stdout)
Current commands are:
parse : only parse input files
redis : use Redis to call into GN2
+ kinship : calculate (new) kinship matrix
try --help for more information
"""
@@ -50,6 +52,13 @@ parser.add_option("--geno",dest="geno",
parser.add_option("-q", "--quiet",
action="store_false", dest="verbose", default=True,
help="don't print status messages to stdout")
+parser.add_option("--blas", action="store_true", default=False, dest="useBLAS", help="Use BLAS instead of numpy matrix multiplication")
+parser.add_option("-t", "--threads",
+ type="int", dest="numThreads",
+ help="Threads to use")
+parser.add_option("--saveKvaKve",
+ action="store_true", dest="saveKvaKve", default=False,
+ help="Testing mode")
parser.add_option("--test",
action="store_true", dest="testing", default=False,
help="Testing mode")
@@ -63,6 +72,10 @@ if len(args) != 1:
cmd = args[0]
print "Command: ",cmd
+k = None
+y = None
+g = None
+
if options.kinship:
k = tsvreader.kinship(options.kinship)
print k.shape
@@ -74,7 +87,7 @@ if options.pheno:
if options.geno:
g = tsvreader.geno(options.geno)
print g.shape
-
+
def normalizeGenotype(G):
# Run for every SNP list (num individuals)
x = True - np.isnan(G) # Matrix of True/False
@@ -89,32 +102,32 @@ def normalizeGenotype(G):
G = (G - m) / s # Normalize the deviation
return G
-# g = g.reshape((1, -1))[0]
print("Before",g)
-gn = []
-for snp in g:
- gn.append( normalizeGenotype(snp) )
-
-gn = g
-gn = np.array(gn)
-print("After1",gn)
-gnT = gn.T
-print("After",gnT)
-# G = gnT
-G = gnT
+
+gT = g.T
+print("After",gT)
+G = np.array(gT)
print "G shape",G.shape
-# sys.exit(1)
-# assert(G[0,0]==-2.25726341)
# Remove individuals with missing phenotypes
-v = np.isnan(y)
-keep = True - v
-if v.sum():
- if options.verbose: sys.stderr.write("Cleaning the phenotype vector by removing %d individuals...\n" % (v.sum()))
- y = y[keep]
- G = G[keep,:]
- k = k[keep,:][:,keep]
+if y != None:
+ v = np.isnan(y)
+ keep = True - v
+ if v.sum():
+ if options.verbose: sys.stderr.write("Cleaning the phenotype vector by removing %d individuals...\n" % (v.sum()))
+ y = y[keep]
+ G = G[keep,:]
+ if k != None: k = k[keep,:][:,keep]
if cmd == 'redis':
ps, ts = gn2_load_redis('testrun','other',np.array(k),y,G,options.testing)
print np.array(ps)
+elif cmd == 'kinship':
+ gn = []
+ for snp in G.T:
+ gn.append( normalizeGenotype(snp) )
+ G2 = np.array(gn)
+ print G2.shape,G2
+ K = kinship(G2,options)
+else:
+ print "Doing nothing"
generated by cgit v1.2.3 (git 2.44.0) at 2024-11-23 01:49:16 +0000