def get_structure_motif_fig(filter_weights,
filter_outs,
out_dir,
protein,
seq_targets,
sample_i=0,
structure=None):
print 'plot motif fig', out_dir
#seqs, seq_targets = get_seq_targets(protein)
seqs = structure
if sample_i:
print 'sampling'
seqs = []
for ind, val in enumerate(seqs):
if ind in sample_i:
seqs.append(val)
seq_targets = seq_targets[sample_i]
filter_outs = filter_outs[sample_i]
num_filters = filter_weights.shape[0]
filter_size = 7 #filter_weights.shape[2]
filters_ic = []
meme_out = structure_motifs.meme_intro('%s/filters_meme.txt' % out_dir,
seqs)
for f in range(num_filters):
print 'Filter %d' % f
# plot filter parameters as a heatmap
structure_motifs.plot_filter_heat(
filter_weights[f, :, :], '%s/filter%d_heat.pdf' % (out_dir, f))
# write possum motif file
structure_motifs.filter_possum(filter_weights[f, :, :], 'filter%d' % f,
'%s/filter%d_possum.txt' % (out_dir, f),
False)
structure_motifs.plot_filter_logo(filter_outs[:, :, f],
filter_size,
seqs,
'%s/filter%d_logo' % (out_dir, f),
maxpct_t=0.5)
filter_pwm, nsites = structure_motifs.make_filter_pwm(
'%s/filter%d_logo.fa' % (out_dir, f))
if nsites < 10:
# no information
filters_ic.append(0)
else:
# compute and save information content
filters_ic.append(info_content(filter_pwm))
# add to the meme motif file
structure_motifs.meme_add(meme_out, f, filter_pwm, nsites, False)
meme_out.close()
def get_motif_fig_new(filter_weights, filter_outs, out_dir, seqs, sample_i=0):
print('plot motif fig', out_dir)
# seqs, seq_targets = get_seq_targets(protein)
if sample_i:
print('sampling')
seqs = []
for ind, val in enumerate(seqs):
if ind in sample_i:
seqs.append(val)
# seq_targets = seq_targets[sample_i]
filter_outs = filter_outs[sample_i]
num_filters = filter_weights.shape[0]
filter_size = 7 # filter_weights.shape[2]
# pdb.set_trace()
#################################################################
# individual filter plots
#################################################################
# also save information contents
filters_ic = []
meme_out = meme_intro('%s/filters_meme.txt' % out_dir, seqs)
for f in range(num_filters):
print('Filter %d' % f)
# plot filter parameters as a heatmap
structure_motifs.plot_filter_heat(
filter_weights[f, :, :], '%s/filter%d_heat.pdf' % (out_dir, f))
# write possum motif file
structure_motifs.filter_possum(filter_weights[f, :, :], 'filter%d' % f,
'%s/filter%d_possum.txt' % (out_dir, f),
False)
# plot weblogo of high scoring outputs
structure_motifs.plot_filter_logo(filter_outs[:, :, f],
filter_size,
seqs,
'%s/filter%d_logo' % (out_dir, f),
maxpct_t=0.5)
# make a PWM for the filter
filter_pwm, nsites = structure_motifs.make_filter_pwm(
'%s/filter%d_logo.fa' % (out_dir, f))
if nsites < 10:
# no information
filters_ic.append(0)
else:
# compute and save information content
filters_ic.append(structure_motifs.info_content(filter_pwm))
# add to the meme motif file
structure_motifs.meme_add(meme_out, f, filter_pwm, nsites, False)
meme_out.close()
#################################################################
# annotate filters
#################################################################
# run tomtom #-evalue 0.01
#sp.call('docker stop rnashapes')
docker_start = "docker start memesuite"
sp.check_output(docker_start, shell=True)
sp.call(
'docker exec -it tomtom -dist pearson -thresh 0.05 -eps -oc %s/tomtom %s/filters_meme.txt %s'
% (out_dir, out_dir, 'Ray2013_rbp_RNA.meme'),
shell=True)
sp.check_output("docker stop memesuite", shell=True)
# subprocess.call('tomtom -dist pearson -thresh 0.05 -eps -oc %s/tomtom %s/filters_meme.txt %s' % (out_dir, out_dir, 'Ray2013_rbp_RNA.meme'), shell=True)
# read in annotations
filter_names = structure_motifs.name_filters(
num_filters, '%s/tomtom/tomtom.txt' % out_dir, 'Ray2013_rbp_RNA.meme')
#################################################################
# print a table of information
#################################################################
table_out = open('%s/table.txt' % out_dir, 'w')
# print header for later panda reading
header_cols = ('', 'consensus', 'annotation', 'ic', 'mean', 'std')
print >> table_out, '%3s %19s %10s %5s %6s %6s' % header_cols
for f in range(num_filters):
# collapse to a consensus motif
consensus = structure_motifs.filter_motif(filter_weights[f, :, :])
# grab annotation
annotation = '.'
name_pieces = filter_names[f].split('_')
if len(name_pieces) > 1:
annotation = name_pieces[1]
# plot density of filter output scores
fmean, fstd = structure_motifs.plot_score_density(
np.ravel(filter_outs[:, :, f]),
'%s/filter%d_dens.pdf' % (out_dir, f))
row_cols = (f, consensus, annotation, filters_ic[f], fmean, fstd)
print >> table_out, '%-3d %19s %10s %5.2f %6.4f %6.4f' % row_cols
table_out.close()
if True:
new_outs = []
for val in filter_outs:
new_outs.append(val.T)
filter_outs = np.array(new_outs)
print(filter_outs.shape)
# plot filter-sequence heatmap
structure_motifs.plot_filter_seq_heat(filter_outs,
'%s/filter_seqs.pdf' % out_dir)
