def test_snmnmf(self):
#import scipy as sp
#import numpy as np
#from pandas import DataFrame, Series
### get testing data ###
m_rna, mi_rna, m2g_intction, pp_intction, bpGO = snmnmf.loadData(
full=False)
### factorization ###
# initialize the factorization
snm = ns.NIMFA_SNMNMF(mRNA = m_rna, miRNA = mi_rna, DNAmethyl = None, gene2gene = pp_intction\
, miRNA2gene = m2g_intction, gene2DNAmethylation = None, params={'l1':0.1,'l2':0.1,'g1':0.1,'g2':0.1})
# run factorization
snm.run(seed="random_c", rank=50, max_iter=5)
# get factorization performance evaluation
snm.performance.getResults()
### Results evaluation ###
e = EnrichmentInGeneSets(snm)
# get co-modules
i = e.getGeneSets(T=1)
## compute enrichment in GO terms ()
enrich_bpGO = e.getEnrichmentInGeneSets(bpGO, T=0.1)
# sort resultst accodring p-values
sorted_enrich_bpGO = sorted(enrich_bpGO.iteritems(),
key=operator.itemgetter(1))
# print results
for key in sorted_enrich_bpGO:
print key[0] + "\t:\t" + str(key[1])
## compute enrichment ratio for different
er_ratio = [e.getEnrichmentRatioInGeneSets(bpGO, T = 0.1, enrichment_threshold = 0.05, N = 100) \
for T in [0.1, 0.2, 0.5, 0.8, 1, 2, 5]]
def test_snmnmf(self):
# import scipy as sp
# import numpy as np
# from pandas import DataFrame, Series
### get testing data ###
m_rna, mi_rna, m2g_intction, pp_intction, bpGO = snmnmf.loadData(full=False)
### factorization ###
# initialize the factorization
snm = ns.NIMFA_SNMNMF(
mRNA=m_rna,
miRNA=mi_rna,
DNAmethyl=None,
gene2gene=pp_intction,
miRNA2gene=m2g_intction,
gene2DNAmethylation=None,
params={"l1": 0.1, "l2": 0.1, "g1": 0.1, "g2": 0.1},
)
# run factorization
snm.run(seed="random_c", rank=50, max_iter=5)
# get factorization performance evaluation
snm.performance.getResults()
### Results evaluation ###
e = EnrichmentInGeneSets(snm)
# get co-modules
i = e.getGeneSets(T=1)
## compute enrichment in GO terms ()
enrich_bpGO = e.getEnrichmentInGeneSets(bpGO, T=0.1)
# sort resultst accodring p-values
sorted_enrich_bpGO = sorted(enrich_bpGO.iteritems(), key=operator.itemgetter(1))
# print results
for key in sorted_enrich_bpGO:
print key[0] + "\t:\t" + str(key[1])
## compute enrichment ratio for different
er_ratio = [
e.getEnrichmentRatioInGeneSets(bpGO, T=0.1, enrichment_threshold=0.05, N=100)
for T in [0.1, 0.2, 0.5, 0.8, 1, 2, 5]
]
Created on Thu Jul 10 10:59:34 2014
@author: holecm
"""
from mixgene_project.wrappers.snmnmf import nimfa_snmnmf as ns
import mixgene_project.wrappers.snmnmf.snmnmf as snmnmf
from mixgene_project.wrappers.snmnmf.evaluation import EnrichmentInGeneSets
import operator
if __name__ == "__main__":
# import scipy as sp
#import numpy as np
#from pandas import DataFrame, Series
### get testing data ###
m_rna, mi_rna, m2g_intction, pp_intction, bpGO = snmnmf.loadData(full=False)
### factorization ###
# initialize the factorization
snm = ns.NIMFA_SNMNMF(mRNA=m_rna, miRNA=mi_rna, DNAmethyl=None, gene2gene=pp_intction \
, miRNA2gene=m2g_intction, gene2DNAmethylation=None,
params={'l1': 0.1, 'l2': 0.1, 'g1': 0.1, 'g2': 0.1})
# run factorization
snm.run(seed="random_c", rank=50, max_iter=5)
# get factorization performance evaluation
snm.performance.getResults()
### Results evaluation ###
e = EnrichmentInGeneSets(snm)
# get co-modules
i = e.getGeneSets(T=1)
Created on Thu Jul 10 10:59:34 2014
@author: holecm
"""
from mixgene_project.wrappers.snmnmf import nimfa_snmnmf as ns
import mixgene_project.wrappers.snmnmf.snmnmf as snmnmf
from mixgene_project.wrappers.snmnmf.evaluation import EnrichmentInGeneSets
import operator
if __name__ == "__main__":
# import scipy as sp
#import numpy as np
#from pandas import DataFrame, Series
### get testing data ###
m_rna, mi_rna, m2g_intction, pp_intction, bpGO = snmnmf.loadData(
full=False)
### factorization ###
# initialize the factorization
snm = ns.NIMFA_SNMNMF(mRNA=m_rna, miRNA=mi_rna, DNAmethyl=None, gene2gene=pp_intction \
, miRNA2gene=m2g_intction, gene2DNAmethylation=None,
params={'l1': 0.1, 'l2': 0.1, 'g1': 0.1, 'g2': 0.1})
# run factorization
snm.run(seed="random_c", rank=50, max_iter=5)
# get factorization performance evaluation
snm.performance.getResults()
### Results evaluation ###
e = EnrichmentInGeneSets(snm)
# get co-modules
i = e.getGeneSets(T=1)