def runEmma(phed, p_i, k, snps):
# Assume that the accessions are ordered.
i = phed.getPhenIndex(p_i)
r.library("emma")
phenValues = []
for vals in phed.phenotypeValues:
phenValues.append(float(vals[i]))
phenArray = array([phenValues])
snpsArray = array(snps)
res = r.emma_REML_t(phenArray, snpsArray, k)
# print res
return res
def runEmma(phed, p_i, k, snps):
#Assume that the accessions are ordered.
i = phed.getPhenIndex(p_i)
#r.library("emma")
r.source("emma.R")
phenValues = []
for vals in phed.phenotypeValues:
phenValues.append(float(vals[i]))
phenArray = array([phenValues])
snpsArray = array(snps)
res = r.emma_REML_t(phenArray, snpsArray, k)
#print res
return res
def run_emma_w_missing_data(snpsd, phed, p_i, chromosome, k, missing_val='NA'):
"""
Runs Emma w. missing data.
"""
#Assume that the accessions are ordered.
snps = snpsd.snps
r.source("emma.R")
phen_vals = phed.getPhenVals(p_i, noNAs=False)
if phen_vals.count('NA'):
print "Coordinating SNPs and phenotypes, removing %d accessions which were missing phenotypic values." % phen_vals.count(
'NA')
ai_to_keep = [i for i in range(len(phen_vals)) if phen_vals[i] != 'NA']
#print ai_to_keep
new_snps = []
for snp in snps:
new_snps.append([snp[i] for i in ai_to_keep])
snps = new_snps
#print snps[0]
phen_vals = [phen_vals[i] for i in ai_to_keep]
#print phen_vals
new_k = zeros((len(ai_to_keep), len(ai_to_keep)))
for j1 in range(len(ai_to_keep)):
for j2 in range(len(ai_to_keep)):
new_k[j1, j2] = k[ai_to_keep[j1], ai_to_keep[j2]]
k = new_k
p_vals = []
for snp in snps:
if snp.count(missing_val):
acc_to_keep = []
new_snp = []
for j, nt in enumerate(snp):
if not nt == missing_val:
acc_to_keep.append(j)
new_snp.append(nt)
new_k = zeros((len(acc_to_keep), len(acc_to_keep)))
new_phen_vals = []
for j1 in range(len(acc_to_keep)):
for j2 in range(len(acc_to_keep)):
new_k[j1, j2] = k[acc_to_keep[j1], acc_to_keep[j2]]
new_phen_vals.append(phen_vals[acc_to_keep[j1]])
if len(set(snp)) == 1:
p_vals.append(1)
print phenArray
print snpsArray
print new_k
else:
phenArray = array([new_phen_vals])
snpsArray = array([new_snp])
#print "Running EMMA w. missing data."
try:
p_vals.extend(
r.emma_REML_t(phenArray, snpsArray, new_k)['ps'])
except Exception, err_str:
print err_str
print phenArray
print snpsArray
print new_k
else:
phenArray = array([phen_vals])
snpsArray = array([snp])
#print "Running EMMA w.o. missing data."
try:
p_vals.extend(r.emma_REML_t(phenArray, snpsArray, k)['ps'])
except Exception, err_str:
print err_str
print phenArray
print snpsArray
print k
def _runEmma_(snps, phenValues, k):
phenArray = array([phenValues])
snpsArray = array(snps)
res = r.emma_REML_t(phenArray, snpsArray, k)
#print res
return res
def _runEmma_(snps,phenValues,k): phenArray = array([phenValues]) snpsArray = array(snps) res = r.emma_REML_t(phenArray,snpsArray,k) #print res return res
