def single_array(gids, unnorm_eluts, sp, min_count=1,
remove_multi_base=False, norm_rows=False):
"""
unnorm_eluts: [el.NormElut(f, sp=sp, norm_cols=False, norm_rows=False) for f in fs]
"""
import plotting as pl
use_eluts = elutions_containing_prots(unnorm_eluts, sp, gids, min_count)
print len(use_eluts), "eluts with proteins"
ncols = sum([e.normarr.shape[1] for e in use_eluts])
bigarr = np.zeros((len(gids), ncols))
startcol = 0
for e in use_eluts:
freqarr = ut.normalize_fracs(e.normarr, norm_rows=norm_rows)
temparr = np.zeros((len(gids), freqarr.shape[1]))
for i, gid in enumerate(gids):
if gid in e.baseid2inds:
inds = list(e.baseid2inds[gid])
rows = freqarr[inds,:]
row = np.max(rows, axis=0)
temparr[i,:] = row
frac_max = np.max(temparr)
temparr = np.clip(np.log2(temparr*100 / frac_max), 0, 10)
bigarr[:, startcol:startcol+freqarr.shape[1]] = temparr
startcol += freqarr.shape[1]
return bigarr
def pdist_score(mat, metric='euclidean', norm_rows=True,
norm_cols=True):
norm_mat = ut.normalize_fracs(mat, norm_rows, norm_cols)
dists = spatial.distance.pdist(norm_mat, metric=metric)
dist_mat = spatial.distance.squareform(dists)
score_mat = 1 - np.nan_to_num(dist_mat)
return score_mat
def pdist_score(mat, metric='euclidean', norm_rows=True,
norm_cols=True):
norm_mat = ut.normalize_fracs(mat, norm_rows, norm_cols)
dists = spatial.distance.pdist(norm_mat, metric=metric)
dist_mat = spatial.distance.squareform(dists)
score_mat = 1 - np.nan_to_num(dist_mat)
return score_mat
def __init__(self, filename, sp_base="Hs", norm_rows=False, norm_cols=False):
e = load_elution(filename)
self.prots = e.prots
self.filename = e.filename
self.normarr = ut.normalize_fracs(e.mat, norm_rows=norm_rows, norm_cols=norm_cols)
self.pinv = ut.list_inv_to_dict(e.prots)
sp_target = ut.shortname(e.filename)[:2]
self.baseid2inds = sc.orth_indices(sp_base, sp_target, e.prots, False)
def __init__(self, filename, sp_base='Hs', norm_rows=False, norm_cols=False):
e = load_elution(filename)
self.prots = e.prots
self.filename = e.filename
self.normarr = ut.normalize_fracs(e.mat, norm_rows=norm_rows,
norm_cols=norm_cols)
self.pinv = ut.list_inv_to_dict(e.prots)
sp_target = ut.shortname(e.filename)[:2]
self.baseid2inds = sc.orth_indices(sp_base, sp_target, e.prots, False)
def plot_bigprofiles(prots, pids, unnorm_eluts, sp='Hs', min_count=1,
remove_multi_base=False, gt=None, eluts_per_plot=10,
do_cluster=True, label_trans=None, do_plot_tree=False,
rename_fracs=None, colors=None, **kwargs):
"""
supply EITHER prots OR protids, set other to None
unnorm_eluts: [el.NormElut(f, sp=sp, norm_cols=False, norm_rows=False) for f in fs]
"""
import plotting as pl
if prots is not None:
pids = [gt.name2id[p] for p in prots]
if do_cluster:
print "clustering"
pids = cluster_ids(pids, unnorm_eluts, sp, gt=gt, do_plot=do_plot_tree,
**kwargs)
if gt is not None:
prots = [gt.id2name[pid] for pid in pids if pid in gt.id2name] #re-order to match
else:
prots = pids
print "No gene names provided--labeling with ids."
if label_trans:
print "Translating names for display."
# Translate displayed names from base ids according to provided dict
#prots = [gt.id2name[pid] for pid in pids]
prots = [label_trans.get(p,p) for p in prots]
prots.reverse(); pids.reverse(); # put them top to bottom
if colors is not None: colors.reverse()
print "%s proteins" % len(pids)
use_eluts = elutions_containing_prots(unnorm_eluts, sp, pids, min_count)
nplots = int(np.ceil(len(use_eluts) / eluts_per_plot))
maxfracs = 0
for iplot in range(nplots):
pl.subplot(nplots, 1, iplot+1)
plot_eluts = use_eluts[iplot*eluts_per_plot: (iplot+1)*eluts_per_plot]
frac_names = [ut.shortname(e.filename) for e in plot_eluts]
if rename_fracs:
frac_names = [rename_fracs.get(n,n) for n in frac_names]
startcols = [0]
for i,e in enumerate(plot_eluts):
freqarr = ut.normalize_fracs(e.normarr, norm_rows=False)
sp_target = ut.shortname(e.filename)[:2]
protsmax = max([np.max(freqarr[r]) for p in pids if p in
e.baseid2inds for r in e.baseid2inds[p]])
plot_big_single(freqarr, pids, e.baseid2inds, protsmax,
startcols[-1], colors=colors)
startcols.append(startcols[-1]+freqarr.shape[1])
label_ys(prots)
label_xs(startcols, frac_names)
pl.grid(False)
maxfracs = maxfracs if maxfracs > startcols[-1] else startcols[-1]
for iplot in range(nplots):
pl.subplot(nplots, 1, iplot+1)
pl.xlim(0,maxfracs)
pl.subplots_adjust(hspace=5/len(prots))
return nplots