def plot_profiles(prots, eluts, sp='Hs', plot_sums=True, shape=None,
min_count=1):
"""
shape: (m,n) = m rows, n columns
eluts: [el.NormElut(f, sp, norm_rows=False, norm_cols=False) for f in
fs]
"""
import plotting as pl
gt = seqs.GTrans()
use_eluts = elutions_containing_prots(eluts, sp, seqs.names2ids(prots),
min_count)
shape = shape if shape else ut.sqrt_shape(len(use_eluts)+1)
fig = pl.figure()
for i,e in enumerate(use_eluts):
sp_target = ut.shortname(e.filename)[:2]
pl.subplot(shape[0],shape[1],i+1)
pl.title(ut.shortname(e.filename))
pids = [gt.name2id[p] for p in prots]
protsmax = max([np.max(e.normarr[r]) for p in pids if p in e.baseid2inds for
r in e.baseid2inds[p]])
plot_prots(e, pids, e.baseid2inds, protsmax)
if plot_sums:
# plot total spectral counts normalized to match biggest peak
sums = np.sum(e.normarr,axis=0)
fmax = np.max(sums)
pl.plot(range(sums.shape[1]),
np.log2(sums[0,:]).T*np.log2(protsmax)*len(pids)/np.log2(fmax),
color='k', linestyle='-', linewidth=.5)
# make legend with all prots
pl.subplot(shape[0],shape[1],0)
for p in prots: pl.plot(0,label=p)
pl.legend()
def plot_sums(fs, shape=None):
import plotting as pl
shape = shape if shape else ut.sqrt_shape(len(fs))
for i,f in enumerate(fs):
e = el.load_elution(f)
pl.subplot(shape[0],shape[1],i+1)
pl.title(ut.shortname(f))
sums = np.sum(e.mat,axis=0)
pl.plot(range(sums.shape[1]), sums[0,:].T)
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
