def plot_cdf_pos_randoms(pospairs, ppis):
import plotting as pl
pl.figure()
pos,neg1 = pl.hist_pairs_nonpairs(ppis, pospairs, negmult=1, do_plot=False)
pos,neg100 = pl.hist_pairs_nonpairs(ppis, pospairs, negmult=100,
do_plot=False)
for pairs in pos, neg1, neg100:
pl.cdf(pairs,bins=np.arange(0,1.01,.01))
pl.xlabel("PPI score")
pl.ylabel("Cumulative fraction of population")
pl.title('Several percent of sequential enzymes are high-scoring,\ncompared to much less than one percent for random shufflings')
pl.legend(['Sequentials','Size-matched reshuffled','100x larger set of reshuffled'],loc=4)
def plot_pairs_randoms_etc(sequentials, score_ppis, plusns=None):
import plotting as pl
pl.figure()
plus2s, plus3s4s = plusns or plusn(sequentials)
#plus2s = plusns or plusn(sequentials)
rand_pairs = random_pairs(sequentials, len(sequentials))
scores = [pl.hist_pairs_nonpairs(score_ppis, pairs, negmult=10,
do_plot=False)
for pairs in sequentials, plus2s, plus3s4s, rand_pairs]
#for pairs in sequentials, plus2s, plus3s, plus4s, rand_pairs]
ks_pvals = [ks_2samp(pos,neg)[1] for pos,neg in scores] # [1] is p-value
logvals = [-np.log10(pval) for pval in ks_pvals]
#pl.bar_plot(['%s\np < %0.3g' % (x,y) for x,y in zip('n,n+1 n,n+2 n,n+3 random'.split(), ks_pvals)], logvals)
pl.bar_plot(['%s\np < %0.3g' % (x,y) for x,y in zip('n,n+1 n,n+2 n,n+3/4 random'.split(), ks_pvals)], logvals)
pl.ylabel('-log10(p-value) : two-sample K/S test')
pl.title('Intersection of recon and kegg sequential pairs\nNpairs=%s; %s n+2s, %s n+3s,n+4s' % (len(sequentials), len(plus2s), len(plus3s4s)))
return logvals
