def ppis_gold_standard(ppis, cxs_splits, species):
pdppis = pd.PairDict([p[:3] for p in ppis])
print len(pdppis.d), "predicted interactions"
ppi_cxs,_,all_cxs = ppi.load_training_complexes(species, None,'') #conv doesn't matter
pdcorum = pd.PairDict([(i[0],i[1],'gold') for i in
co.pairs_from_complexes(ut.i1(all_cxs))])
print len(pdcorum.d), "total gold standard"
pdcomb = pd.pd_union_disjoint_vals(pdppis, pdcorum)
unmr_splits = cp.unmerged_splits_from_merged_splits(ppi_cxs,cxs_splits)
print "unmerged split assignment lengths", [len(s) for s in unmr_splits]
pdtrainpos = pd.PairDict([(t[0],t[1]) for t in
co.pairs_from_complexes(unmr_splits[0])])
print len(pdtrainpos.d), "total train interactions"
counterrs = 0
for tpair in pdtrainpos.d:
cpair = pdcomb.find(tpair)
#assert cpair is not None, "Gold standard problem--filter_methods changed since run?"
if cpair is None or pdcomb.d[cpair][1] != 'gold':
#print 'error: train should be subset', tpair
counterrs += 1
else:
pdcomb.d[cpair][1] = 'train'
if counterrs: print "number of training not found in gold std:", counterrs
comblist = [list(k)+list(v) for k,v in pdcomb.d.items()]
print (len([1 for p in comblist if p[2] and p[3]=='gold']),
"ppis in gold not train")
print len([1 for p in comblist if p[2] and p[3]=='train']), "ppis in train"
# only return those that are predictions
return [p for p in comblist if p[2]]
def triple_venn_consv():
hints = co.load_havug_ints()
ppi_cxs, clean_cxs, corconsv = ppi.load_training_complexes("Hs", "Dm")
cints = co.pairs_from_complexes(ut.i1(ppi_cxs)) # exclude huge ones
ints23 = ut.loadpy(ut.bigd("../23_collapsenodes/Hs_filtorth025_withsc_2sp_refilt2sp_cxs_cxppis_clust27_532cxs"))[1]
ints3 = [cp.consv_pairs(i, h2d) for i in ints23, hints, cints]
cp.triple_venn(ints3, ["map23", "havug", "corum"])
def tested_ppis(gold_cxs, ppis):
gold_ints = co.pairs_from_complexes(gold_cxs)
ntest_pos = len(gold_ints)
pdtrues = pd.PairDict(gold_ints)
ppis = [(p[0],p[1],p[2],1 if pdtrues.contains(tuple(p[:2])) else 0) for p in
ppis]
return ppis, ntest_pos
def arrfeats_prep_all_data(arrfeats, ppis, sp="Hs", gold_consv="Dm", cutoff=0.5):
print "Adding species summary."
arrfeats = fe.arr_add_spsummary(arrfeats, cutoff)
print "Adding ppis."
arrfeats = fe.arrfeats_add_ppis(arrfeats, ppis)
_, _, all_cxs = ppi.load_training_complexes(sp, None, gold_consv)
pdgold = pd.PairDict(co.pairs_from_complexes(ut.i1(all_cxs)))
print "Setting trues."
arrfeats = fe.arrfeats_set_gold(arrfeats, pdgold)
return arrfeats
def hpa_stats(ppis, locs, max_set_size=None):
s = attr_to_sets(locs)
if max_set_size is not None:
s = [c for c in s if len(c) < max_set_size]
plocs = co.pairs_from_complexes(s)
ppiprots = set(ut.i0(ppis)+ut.i1(ppis))
anprots = set(ut.i0(locs))
intprots = set.intersection(ppiprots, anprots)
print len(ppiprots), len(anprots), len(intprots)
return ppis_stats(ppis, plocs, intprots)
def _filter_ints(inlist, cxs):
pairs = co.pairs_from_complexes(cxs)
pdp = pd.PairDict(pairs)
return [tup for tup in inlist if pdp.contains((tup[0],tup[1]))]
def clique_score(cx, pdints):
cx_ints = co.pairs_from_complexes([cx])
return len([1 for edge in cx_ints if pdints.contains(edge)])/len(cx_ints)
def gold_label_ppis(ppis, merged_splits, sp, gold_nsp):
gold_consv = 'Dm' if gold_nsp>1 else ''
ppi_cxs,_,_ = ppi.load_training_complexes(sp, '', gold_consv)
train_cxs = unmerged_splits_from_merged_splits(ppi_cxs, merged_splits)[0]
ppis = cv.gold_label_ppis(ppis, co.pairs_from_complexes(train_cxs))
return ppis