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-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/lmm.py17
-rw-r--r--wqflask/wqflask/my_pylmm/pyLMM/runlmm.py17
2 files changed, 25 insertions, 9 deletions
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
index ad6375e9..e51742c4 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/lmm.py
@@ -795,9 +795,9 @@ class LMM:
pl.title(title)
-def run_gwas(species,k,y,geno,cov,reml,refit,inputfn,new_code):
+def run_gwas(species,n,m,k,y,geno,cov=None,reml=True,refit=False,inputfn=None,new_code=True):
"""
- Invoke pylmm using a genotype (SNP) iterator
+ Invoke pylmm using genotype as a matrix or as a (SNP) iterator.
"""
info("gwas_without_redis")
print('pheno', y)
@@ -848,8 +848,11 @@ def gwas_with_redis(key,species,new_code=True):
if v is not None:
v = np.array(v)
return v
-
- ps,ts = run_gwas(species,narray('kinship_matrix'),narray('pheno_vector'),narray('genotype_matrix'),narray('covariate_matrix'),params['restricted_max_likelihood'],params['refit'],params['input_file_name'],new_code)
+
+ y = narray('pheno_vector')
+ n = len(y)
+ m = params['num_genotypes']
+ ps,ts = run_gwas(species,n,m,narray('kinship_matrix'),y,narray('genotype_matrix'),narray('covariate_matrix'),params['restricted_max_likelihood'],params['refit'],params['input_file_name'],new_code)
results_key = "pylmm:results:" + params['temp_uuid']
@@ -873,6 +876,7 @@ def gn2_load_redis(key,species,kinship,pheno,geno,new_code=True):
k = kinship.tolist()
params = dict(pheno_vector = pheno.tolist(),
genotype_matrix = geno.tolist(),
+ num_genotypes = geno.shape[1],
kinship_matrix = k,
covariate_matrix = None,
input_file_name = None,
@@ -881,8 +885,7 @@ def gn2_load_redis(key,species,kinship,pheno,geno,new_code=True):
temp_uuid = "testrun_temp_uuid",
# meta data
- timestamp = datetime.datetime.now().isoformat(),
- )
+ timestamp = datetime.datetime.now().isoformat())
json_params = json.dumps(params)
Redis.set(key, json_params)
@@ -907,7 +910,7 @@ def gn2_load_redis_iter(key,species,kinship,pheno,geno_iterator):
k = kinship.tolist()
params = dict(pheno_vector = pheno.tolist(),
genotype_matrix = "iterator",
- genotypes = i,
+ num_genotypes = i,
kinship_matrix = k,
covariate_matrix = None,
input_file_name = None,
diff --git a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
index 3801529e..f095bb73 100644
--- a/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
+++ b/wqflask/wqflask/my_pylmm/pyLMM/runlmm.py
@@ -21,7 +21,7 @@ from optparse import OptionParser
import sys
import tsvreader
import numpy as np
-from lmm import gn2_load_redis, gn2_load_redis_iter, calculate_kinship_new
+from lmm import gn2_load_redis, gn2_load_redis_iter, calculate_kinship_new, run_gwas
from kinship import kinship, kinship_full
import genotype
import phenotype
@@ -103,7 +103,20 @@ if options.geno and cmd != 'iterator':
g = tsvreader.geno(options.geno)
print g.shape
-if cmd == 'iterator':
+if cmd == 'run':
+ if options.remove_missing_phenotypes:
+ raise Exception('Can not use --remove-missing-phenotypes with LMM2')
+ snp_iterator = tsvreader.geno_iter(options.geno)
+ n = len(y)
+ m = g.shape[1]
+ ps, ts = run_gwas('other',n,m,k,y,g.T)
+ print np.array(ps)
+ print len(ps),sum(ps)
+ # Test results
+ p1 = round(ps[0],4)
+ p2 = round(ps[-1],4)
+
+elif cmd == 'iterator':
if options.remove_missing_phenotypes:
raise Exception('Can not use --remove-missing-phenotypes with LMM2')
snp_iterator = tsvreader.geno_iter(options.geno)