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_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
def prot_conservation(fs,sp1,sp2, gridsize=30, od11=None, return_data=False,
filter_both=True, use_title=True, extent=[-22,-6,-22,-6], fontsize=18,
**kwargs):
"""
Currently only uses 1 to 1 orthologs, so odict should be a simple flat dict
of genesa:genesb.
"""
if sp1==sp2:
return
fs1,fs2 = [[f for f in fs if ut.shortname(f)[:2]==sp] for sp in sp1,sp2]
odict = orth.odict_1to1(sp1,sp2) if od11 == None else od11
pc1_all,pc2_all = [prot_counts(fs) for fs in fs1,fs2]
if filter_both:
ps_use = [p for p in odict if (pc1_all[p]>0 and pc2_all[odict[p]]>0)]
else:
ps_use = [p for p in pc1_all if p in odict]
pc1,pc2 = zip(*[(pc1_all[p], pc2_all[odict[p]]) for p in ps_use])
logpc1,logpc2 = [np.log2(pc) for pc in pc1,pc2]
plot(extent[:2],extent[2:],'k:', linewidth=1)
hexbin(logpc1,logpc2,gridsize=gridsize,**kwargs)
#if use_title:
#xlabel('%s log2 unique spectral counts' %sp1)
#ylabel('%s log2 unique spectral counts' %sp2)
#title('%s-%s: spearmanR: %0.2f, %s 1-1 nonzero ortholog pairs' %
#(sp1,sp2, scipy.stats.spearmanr(pc1,pc2)[0], len(pc1)))
rval = scipy.stats.spearmanr(pc1,pc2)[0]
annotate('R=%0.2f\nN=%s' % (rval, len(pc1)), xy=(.05,.7),
xycoords=("axes fraction"), fontsize=fontsize)
if return_data:
return pc1,pc2
def maybe_move(fpath, file2folder, remove_final_underscore):
"""
For moving a file into the proper folder based on a text mapping file.
Format: fbase\tfolder
Ex: WAN1100427_OT2_Celegans_HCW_P1A04 Ce_1104
Purpose of remove_final_underscore is for non-exact mappings. If using
the mapping file from the raw files, should not be neede.
"""
if not os.path.exists(fpath):
print "File not found:", fpath
return
basename = ut.shortname(fpath)
if remove_final_underscore:
basename = ('_'.join(basename.split('_')[:3])
if len(basename.split('_'))>2 else basename)
if not basename in file2folder:
print "No mapping for file:", fpath, basename
return
folder = file2folder[basename]
if not os.path.exists(folder):
print "Creating directory", folder
os.mkdir(folder)
newpath = os.path.join(folder, os.path.split(fpath)[1])
if os.path.exists(newpath):
print "File exists:", newpath
else:
print "Moving to", newpath
os.rename(fpath, newpath)
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_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 __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 multi_identities(input_fname, out_dir):
input_list = ut.load_lol(input_fname)
for desc, prots_fname, source_fasta, odict, target in input_list:
print "%s, proteins: %s\n source: %s\n odict: %s\ntarget: %s" % (desc,
prots_fname, source_fasta, odict, target)
prots = ut.load_list(prots_fname)
sims = all_identities(prots, odict, source_fasta, target)
out_fname = os.path.join(out_dir,
ut.shortname(target).split('.')[0] + "_" + desc + ".txt")
ut.write_tab_file(sims, out_fname, islist=True)
def all_filtered_pairs(fnames, score_keys, cutoff=0.5, sp_base=None, verbose=True, allow_singles=True):
allpairs = pd.PairDict([])
for skey, f in it.product(score_keys, fnames):
if verbose:
print skey, cutoff, ut.shortname(f)
elut = load_elution(f)
newpairs = passing_pairs(elut, skey, cutoff, allow_singles)
newpairs = translate_pairs(newpairs, sp_base, file_sp(f))
allpairs = pd.pd_union_novals(allpairs, newpairs)
return allpairs
def all_filtered_pairs(fnames, score_keys, cutoff=0.5, sp_base=None,
verbose=True, allow_singles=True):
allpairs = pd.PairDict([])
for skey,f in it.product(score_keys,fnames):
if verbose: print skey, cutoff, ut.shortname(f)
elut = load_elution(f)
newpairs = passing_pairs(elut, skey, cutoff, allow_singles)
newpairs = translate_pairs(newpairs, sp_base, file_sp(f))
allpairs = pd.pd_union_novals(allpairs, newpairs)
return allpairs
def countfs(fmap, filenames):
fshorts = [ut.shortname(f) for f in filenames]
counts = [count_dict_values(fmap, x) for x in [fmap.keys(), fshorts]]
print "folder original_counts current_counts"
output = [(folder, counts[0][folder], counts[1][folder]) for folder in
sorted(set(fmap.values()))]
for x in output: print x[0], x[1], x[2]
print "\n\nfinished:"
for x in output:
if x[1]==x[2]: print x[0]
def score_whole(gids, norm_eluts):
scores = []
for e in norm_eluts:
if min([i in e.baseid2inds for i in gids])==True:
rows = [ind for gid in gids if gid in e.baseid2inds
for ind in e.baseid2inds[gid]]
score = SubsplitScore(gids, distance_set(e.normarr, rows), None,
ut.shortname(e.filename))
scores.append(score)
return SplitScores(gids, scores)
def supporting_ppis_separate(ppis, fnames, score_keys, sp_base, cutoff=0.5, verbose=True):
sps = set([file_sp(f) for f in fnames])
print "Species:", " ".join(sps)
ppis_support = [dict([(s, pd.PairDict([])) for s in sps]) for p in ppis]
eluts = [load_elution(f) for f in fnames]
for elut, skey in it.product(eluts, score_keys):
sp = file_sp(elut.filename)
if verbose:
print skey, ut.shortname(elut.filename)
od = orth.odict(sp_base, sp)
try:
new_pairs = passing_pairs(elut, skey, cutoff)
except IOError:
print "No file for %s %s" % (ut.shortname(elut.filename), skey)
continue
for p, dsupport in zip(ppis, ppis_support):
for opair in orth.orth_pairs(p[:2], od):
opair = tuple(opair)
if new_pairs.contains(opair):
dsupport[sp].set(opair, None)
return [list(p) + [[dsupport[sp].d.keys()] for sp in sps] for p, dsupport in zip(ppis, ppis_support)]
def check(fasta, protq, do_convert):
p2g = seqs.prots2genes(fasta)
g2p = ut.dict_inverse(p2g)
fprots = el.load_elution(protq).prots
print "checking", ut.shortname(protq)
print "proteins: %s of %s" % (len([p for p in fprots if p in p2g]),
len(fprots))
ngenesfound = len([p for p in fprots if p in g2p])
print "genes: %s of %s" % (ngenesfound,
len(fprots))
if do_convert and ngenesfound < len(fprots):
print "converting prots to genes:", protq
seqs.elut_p2g(protq, p2g)
def supporting_ppis_separate(ppis, fnames, score_keys, sp_base, cutoff=0.5,
verbose=True):
sps = set([file_sp(f) for f in fnames])
print "Species:", ' '.join(sps)
ppis_support = [dict([(s, pd.PairDict([])) for s in sps]) for p in ppis]
eluts = [load_elution(f) for f in fnames]
for elut,skey in it.product(eluts, score_keys):
sp = file_sp(elut.filename)
if verbose: print skey, ut.shortname(elut.filename)
od = orth.odict(sp_base, sp)
try:
new_pairs = passing_pairs(elut, skey, cutoff)
except IOError:
print "No file for %s %s" % (ut.shortname(elut.filename), skey)
continue
for p,dsupport in zip(ppis,ppis_support):
for opair in orth.orth_pairs(p[:2], od):
opair = tuple(opair)
if new_pairs.contains(opair):
dsupport[sp].set(opair,None)
return [list(p) + [[dsupport[sp].d.keys()] for sp in sps]
for p,dsupport in zip(ppis, ppis_support)]
def supporting_ppis(ppis, fnames, score_keys, sp_base, cutoff=0.5, verbose=True):
ppis_support = [pd.PairDict([]) for p in ppis]
eluts = [load_elution(f) for f in fnames]
for elut,skey in it.product(eluts, score_keys):
if verbose: print skey, ut.shortname(elut.filename)
od = orth.odict(sp_base, file_sp(elut.filename))
new_pairs = passing_pairs(elut, skey, cutoff)
for p,pdsupport in zip(ppis,ppis_support):
for opair in orth.orth_pairs(p[:2], od):
opair = tuple(opair)
if new_pairs.contains(opair):
pdsupport.set(opair,None)
return [list(p) + [s.d.keys()] for p,s in zip(ppis, ppis_support)]
def move(fname, fmap):
"""
For renaming a file based on a mapping old_fname to new_fname.
NOT for moving a file to mapped folder. That's the other script.
"""
basename = ut.shortname(fname)
fext = os.path.splitext(fname)[1]
fdir = os.path.split(fname)[0]
if basename in fmap:
newname = os.path.join(fdir,fmap[basename] + fext)
print "moving", fname, newname
os.rename(fname, newname)
else:
print "not found", fname
def supporting_ppis(ppis, fnames, score_keys, sp_base, cutoff=0.5, verbose=True):
ppis_support = [pd.PairDict([]) for p in ppis]
eluts = [load_elution(f) for f in fnames]
for elut, skey in it.product(eluts, score_keys):
if verbose:
print skey, ut.shortname(elut.filename)
od = orth.odict(sp_base, file_sp(elut.filename))
new_pairs = passing_pairs(elut, skey, cutoff)
for p, pdsupport in zip(ppis, ppis_support):
for opair in orth.orth_pairs(p[:2], od):
opair = tuple(opair)
if new_pairs.contains(opair):
pdsupport.set(opair, None)
return [list(p) + [s.d.keys()] for p, s in zip(ppis, ppis_support)]
def msb_filter(proj_dir, msb_out_dir, pq_path):
"""
Filter the pepquant output by keeping only values with spectral counts in
the msblender output.
"""
proj_name = ut.shortname(proj_dir)
msb_quant_file = os.path.join(msb_out_dir, proj_name+MSB_EXT)
assert os.path.exists(msb_quant_file), "No filter elution found: %s" % msb_quant_file
pq_elut, msb_elut = [el.load_elution(f) for f in pq_path,
msb_quant_file]
pq_elut.mat = el.filter_matching_elution(pq_elut, msb_elut)
pq_filt_path = pq_path.replace(PQ_CLEAN, PQ_FILT)
el.write_elution(pq_elut, pq_filt_path)
return pq_filt_path
def score_array_multi(arr, sp_base, elut_fs, scores, cutoff, verbose=False,
remove_multi_base=False, gidscheme=None, allow_singles=True):
"""
- remove_multi_base: This is not the method currently used to filter scores
in cases of orthogroup fan-outs--this is a stricter earlier version. That
filter is feature.py: filter_multi_orths(), applied after scoring.
"""
assert gidscheme=='', "Gidscheme not implemented in scoring."
current_sp = ''
if remove_multi_base:
print ("Filtering orths: only single base gene in orthogroups.")
for e,f in [(el.load_elution(f),f) for f in elut_fs]:
sp_target = ut.shortname(f)[:2]
if sp_target != current_sp: # Just for status output
print "Starting first %s file: %s" % (sp_target, ut.shortname(f))
current_sp = sp_target
baseid2inds = orth_indices(sp_base, sp_target, e.prots,
remove_multi_base)
# singles based on original spec counts
singles = set([]) if allow_singles else prots_singles(e)
for score in scores:
if verbose: print score, f
score_array(arr, e, f, score, cutoff, baseid2inds, singles, lambda prots:
orth_indices(sp_base, sp_target, prots, remove_multi_base))
def score_eluts_in_out(normed_eluts, gid_split, sp='Hs', **kwargs):
scores = []
for e in normed_eluts:
rowid_split = [[ind for gid in gids if gid in e.baseid2inds
for ind in e.baseid2inds[gid]]
for gids in gid_split]
if len(rowid_split[0])>=2 and len(rowid_split[1])>=1:#skip if any empties
cx_dist = distance_set(e.normarr, rowid_split[0])
exclude_dist = distance_exclude(e.normarr, rowid_split[0],
rowid_split[1])
used_ids = [[e.prots[r] for r in rows] for rows in rowid_split]
subscore = SubsplitScore(used_ids, cx_dist, exclude_dist,
ut.shortname(e.filename))
scores.append(subscore)
return scores
def score_array_multi(arr, sp_base, elut_fs, scores, cutoff, verbose=False,
remove_multi_base=False, gidscheme=None, allow_singles=True):
"""
- remove_multi_base: This is not the method currently used to filter scores
in cases of orthogroup fan-outs--this is a stricter earlier version. That
filter is feature.py: filter_multi_orths(), applied after scoring.
"""
assert gidscheme=='', "Gidscheme not implemented in scoring."
current_sp = ''
if remove_multi_base:
print ("Filtering orths: only single base gene in orthogroups.")
for e,f in [(el.load_elution(f),f) for f in elut_fs]:
sp_target = ut.shortname(f)[:2]
if sp_target != current_sp: # Just for status output
print "Starting first %s file: %s" % (sp_target, ut.shortname(f))
current_sp = sp_target
baseid2inds = orth_indices(sp_base, sp_target, e.prots,
remove_multi_base)
# singles based on original spec counts
singles = set([]) if allow_singles else prots_singles(e)
for score in scores:
if verbose: print score, f
score_array(arr, e, f, score, cutoff, baseid2inds, singles, lambda prots:
orth_indices(sp_base, sp_target, prots, remove_multi_base))
def score_apart(self, gi, gj, cutoff=2):
pair = self.pd_clust_feats.find((gi,gj))
if pair:
evs = self.pd_clust_feats.d[pair]
else:
evs = [0]*len(self.feat_names)
cumsum = 0
for elutf in self.elut_max:
maxi,maxj = [self.elut_max[elutf].get(x,0) for x in gi,gj]
if maxi >= cutoff and maxj >= cutoff:
use_inds = [ind for ind,name in enumerate(self.feat_names) if
frac_name(name) == ut.shortname(elutf)]
cumsum += sum([self.avg_ev-evs[ind] for ind in use_inds])
#elif maxi > cutoff or maxj > cutoff:
#pass
return cumsum
def process(proj_dir, msb_out_dir, dirnames):
"""
If a single dirname, just process.
If multiple, merge then process.
"""
proj_name = ut.shortname(proj_dir)
if proj_dir in dirnames and len(dirnames) > 1:
dirnames.remove(proj_dir)
if dirnames != [proj_dir]:
pq_path = os.path.join(proj_dir, PQ_OUTFILE)
merge(proj_dir, dirnames, pq_path)
else:
print "No merging."
pq_path = os.path.join(proj_dir, PQ_FILE)
pq_clean_path = os.path.join(proj_dir, proj_name+PQ_CLEAN)
elut_clean_prots(pq_path, pq_clean_path)
pq_filt_path = msb_filter(proj_dir, msb_out_dir, pq_clean_path)
def merge(proj_dir, dirnames, pq_new_path):
"""
Combine pepquant quantitation from project_1 (etc) PQ_FILE into
project+PQ_NEW.
"""
if not os.path.exists(proj_dir):
os.mkdir(proj_dir)
proj_name = ut.shortname(proj_dir)
assert not os.path.exists(pq_new_path), "%s exists. Exiting." % pq_new_path
dirnames = ut.i0(sort_numbered(dirnames))
#print "Sorted dirnames:", dirnames
pq_files = [os.path.join(d,PQ_FILE) for d in dirnames]
for f in pq_files:
if not os.path.exists(f):
print "No Elution File:", f
eluts = (el.load_elution(f) for f in pq_files if os.path.exists(f))
merged = reduce(el.combine_elutions, eluts)
el.write_elution(merged, pq_new_path)
def name_score(fname, score):
return ut.shortname(fname) + '_' + score
def file_sp(filename):
return ut.shortname(filename)[:2]
def file_sp(filename):
return ut.shortname(filename)[:2]
os.mkdir(proj_dir)
proj_name = ut.shortname(proj_dir)
assert not os.path.exists(pq_new_path), "%s exists. Exiting." % pq_new_path
dirnames = ut.i0(sort_numbered(dirnames))
#print "Sorted dirnames:", dirnames
pq_files = [os.path.join(d,PQ_FILE) for d in dirnames]
for f in pq_files:
if not os.path.exists(f):
print "No Elution File:", f
eluts = (el.load_elution(f) for f in pq_files if os.path.exists(f))
merged = reduce(el.combine_elutions, eluts)
el.write_elution(merged, pq_new_path)
def sort_numbered(filenames):
pairs = [(f, int(f.split('_')[-1])) for f in filenames]
pairs.sort(key=lambda x: x[1])
return pairs
if __name__ == '__main__':
if len(sys.argv) < 2:
sys.exit("usage: python pepquant_post.py proj_dir msb_out_dir directory(s)")
proj_dir = sys.argv[1]
msb_out_dir = sys.argv[2]
dirnames = sys.argv[3:]
#print "Directories:", dirnames
if not os.path.exists(dirnames[0]):
print "First directory doesn't exist:", dirnames[0]
else:
print "Project:", ut.shortname(proj_dir), "First directory", dirnames[0]
process(proj_dir, msb_out_dir, dirnames)
def load_seqs(fasta_fname):
records = [x for x in SeqIO.parse(fasta_fname, "fasta")]
medlen = np.median([len(r.seq) for r in records])
print "%s: %s sequences, median length %s" % (ut.shortname(fasta_fname),
len(records), medlen)
return records
def name_score(fname, score):
return ut.shortname(fname) + '_' + score
