def enrichment_no_t_task(
exp,
block,
T,
gs,
patterns,
base_filename,
):
if settings.CELERY_DEBUG:
import sys
sys.path.append(
'/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg'
)
import pydevd
pydevd.settrace('localhost',
port=6901,
stdoutToServer=True,
stderrToServer=True)
gene_set = gs.get_gs()
patterns = patterns.get_gs()
e = EnrichmentInGeneSets(patterns.genes)
enrich = e.getModuleEnrichmentInGeneSets(patterns.genes,
gene_set.genes,
pval_threshold=T)
enrich = dict(
(mod, (genes,
map(lambda x: (gene_set.description[x[0]], x[0], x[1]), terms)))
for (mod, (genes, terms)) in enrich.items())
ds = DictionarySet(exp.get_data_folder(), base_filename)
ds.store_dict(enrich)
return [ds], {}
def threshold_task(exp, block,
es,
T,
base_filename,
):
# def removeTemporaryNegativeFeatures(S, indicator_string = 'negative_feature___'):
# """Remove elements starting with the indicator_string and remove possible duplicates."""
# return S.apply(lambda list_element: set([s.replace(indicator_string, '') for s in list_element]))
"""Computes co-comodules from matrix H by given threshold T."""
if settings.CELERY_DEBUG:
import sys
sys.path.append('/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg')
import pydevd
pydevd.settrace('localhost', port=6901, stdoutToServer=True, stderrToServer=True)
H = es.get_assay_data_frame()
print(H)
# mu = np.mean(H, axis = 1)
# sigma = np.std(H, axis = 1)
# Z = H.apply(lambda z: (z-mu)/sigma, axis = 0)
# S = []
# S.append(removeTemporaryNegativeFeatures(Z.apply(lambda x: Z.columns[x >= T].tolist(), axis = 1)))
# S = pd.DataFrame(S)
# S = S.apply(lambda x: set.union(*x))
# result = pd.DataFrame(S)
from wrappers.snmnmf.evaluation import EnrichmentInGeneSets
z = 1
x = EnrichmentInGeneSets(z)
result = x.getGeneSet(H, T)
cs = ComoduleSet(exp.get_data_folder(), base_filename)
cs.store_set(result)
return [cs], {}
def threshold_task(
exp,
block,
es,
T,
base_filename,
):
# def removeTemporaryNegativeFeatures(S, indicator_string = 'negative_feature___'):
# """Remove elements starting with the indicator_string and remove possible duplicates."""
# return S.apply(lambda list_element: set([s.replace(indicator_string, '') for s in list_element]))
"""Computes co-comodules from matrix H by given threshold T."""
if settings.CELERY_DEBUG:
import sys
sys.path.append(
'/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg'
)
import pydevd
pydevd.settrace('localhost',
port=6901,
stdoutToServer=True,
stderrToServer=True)
H = es.get_assay_data_frame()
print(H)
# mu = np.mean(H, axis = 1)
# sigma = np.std(H, axis = 1)
# Z = H.apply(lambda z: (z-mu)/sigma, axis = 0)
# S = []
# S.append(removeTemporaryNegativeFeatures(Z.apply(lambda x: Z.columns[x >= T].tolist(), axis = 1)))
# S = pd.DataFrame(S)
# S = S.apply(lambda x: set.union(*x))
# result = pd.DataFrame(S)
from wrappers.snmnmf.evaluation import EnrichmentInGeneSets
z = 1
x = EnrichmentInGeneSets(z)
result = x.getGeneSet(H, T)
gene_sets = GeneSets(exp.get_data_folder(), base_filename)
gs = GS(result, result)
gene_sets.store_gs(gs)
# cs = GeneSets(exp.get_data_folder(), base_filename)
# cs.store_set(result)
return [gene_sets], {}
def enrichment_no_t_task(exp, block,
T,
gs,
cs,
base_filename,
):
if settings.CELERY_DEBUG:
import sys
sys.path.append('/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg')
import pydevd
pydevd.settrace('localhost', port=6901, stdoutToServer=True, stderrToServer=True)
gene_set = gs.get_gs()
cs = cs.load_set()
e = EnrichmentInGeneSets(cs)
enrich = e.getModuleEnrichmentInGeneSets(cs, gene_set.genes, pval_threshold=T)
enrich = dict((mod, (genes, map(lambda x: (gene_set.description[x[0]], x[0], x[1]), terms))) for (mod, (genes, terms)) in enrich.items())
ds = DictionarySet(exp.get_data_folder(), base_filename)
ds.store_dict(enrich)
return [ds], {}
def enrichment_task(exp, block,
gs,
H2,
T,
base_filename,
):
if settings.CELERY_DEBUG:
import sys
sys.path.append('/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg')
import pydevd
pydevd.settrace('localhost', port=6901, stdoutToServer=True, stderrToServer=True)
gene_set = gs.get_gs()
h2 = H2.get_assay_data_frame()
e = EnrichmentInGeneSets(h2)
## compute enrichment in GO terms ()
enrich_bpGO = e.getEnrichmentInGeneSetsWithH(gene_set.genes, h2, T)
# sort resultst accodring p-values
# sorted_enrich_bpGO = sorted(enrich_bpGO.iteritems(), key=operator.itemgetter(1))
er_ratio = e.getEnrichmentRatioInGeneSetsWithH(gene_set.genes, h2, T, enrichment_threshold=0.05, N=10)
enrich_bpGO['er_ratio'] = er_ratio
ds = DictionarySet(exp.get_data_folder(), base_filename)
ds.store_dict(enrich_bpGO)
return [ds], {}
