for k, count in stats.iteritems():
print '{}\t{}\t{:.2f}\t{:.2f}'.format(
k,
count,
count / stats['all'] * 100,
count / stats['enhancers'] * 100,
)
subsets = {}
if True:
subsets['with_me2'] = data[(data['me2_id'] > 0)]
subsets['with_ac'] = data[(data['ac_id'] > 0)]
subsets['inactive'] = data[(data['ac_id'] == 0)
& (data['me2_id'] == 0)]
for k, subset in subsets.iteritems():
first_peak = 'foxp3'
subset['id'] = subset[first_peak + '_id']
subset['start'] = subset[first_peak + '_start']
subset['end'] = subset[first_peak + '_end']
yzer.run_homer(subset,
first_peak + '_enh_' + k,
motif_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15])
yzer = MotifAnalyzer()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/' +\
'Miscellaneous_Collaborations/Rodrigo_CD8s_2014_09/Enhancers'
dirpath = yzer.get_path(dirpath)
for cond, seq, breed in SAMPLES:
sample_prefix = sample_name(cond, seq, breed)
sample_dirpath = yzer.get_filename(dirpath, sample_prefix)
filename = yzer.get_filename(sample_dirpath,
sample_prefix + '_enhancers.txt')
data = yzer.import_file(filename)
data = data.fillna(0)
if True:
min_thresh = get_threshold(seq)
subdata = data[data['tag_count'] >= min_thresh]
yzer.run_homer(subdata,
'all',
sample_dirpath,
cpus=10,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12],
mock=True)
for peptide in ('K99A', 'NoPep', 'PCC'):
pep_dirpath = yzer.get_filename(dirpath,
'{}_{}'.format(peptide, ab))
if False:
filename = yzer.get_filename(
pep_dirpath, '{}_{}_enhancers.txt'.format(peptide, ab))
data = yzer.import_file(filename)
data = data.fillna(0)
yzer.run_homer(data,
'all',
pep_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15],
mock=True)
yzer.run_homer(data[data['tag_count'] >= 10],
'tag_thresh_10',
pep_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15],
mock=True)
'{}_{}'.format(condition, ab))
if False:
filename = yzer.get_filename(
cond_dirpath, '{}_{}_enhancers.txt'.format(condition, ab))
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = 20
data = data[data['tag_count'] > min_thresh]
yzer.run_homer(data,
'filtered_{}'.format(min_thresh),
cond_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15],
mock=True)
if True:
filename = yzer.get_filename(
cond_dirpath, '{}_{}_enhancers.txt'.format(condition, ab))
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = 20
subdata = data[data['tag_count'] > min_thresh]
subdata = subdata[subdata['tag_count(2)'] <= min_thresh]
filename = yzer.get_filename(sample_dirpath,
sample_prefix + '_enhancers.txt')
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = get_threshold(seq)
data = data[data['tag_count'] >= min_thresh]
fold = 2
if True:
# ATAC peaks that are absent in the FOXO1 competent
ko_new = data[
data['naive_atac_tag_count'] < min_thresh]
yzer.run_homer(ko_new,
'ko_new', sample_dirpath,
cpus=10, center=True, reverse=False, preceding=False,
size=200, length=[8, 10, 12], mock=True)
print('ko_new', len(ko_new))
# ATAC peaks exist, but are less than half as big
ko_grows = data[
(data['naive_atac_tag_count'] >= min_thresh) &
(data['naive_atac_tag_count'] * fold < data['tag_count'])]
yzer.run_homer(ko_grows,
'ko_grows', sample_dirpath,
cpus=10, center=True, reverse=False, preceding=False,
size=200, length=[8, 10, 12], mock=True)
print('ko_grows', len(ko_grows))
# The sum of new and bigger
ko_dependent = data[
)
dirpath = yzer.get_and_create_path(base_dirpath, 'motifs/')
filename = yzer.get_filename(base_dirpath, 'transcript_vectors.txt')
data = yzer.import_file(filename)
data = data.fillna(0)
# Promoters
if False:
refseq = data[data['has_refseq'] == 1]
refseq = refseq[refseq['transcript_score'] >= 4]
if True:
yzer.run_homer(refseq,
'refseq_promoter',
dirpath,
cpus=6,
center=False,
reverse=False,
preceding=True,
size=400,
length=[8, 10, 12, 15])
bg = yzer.get_filename(
dirpath, 'refseq_promoter/refseq_promoter_regions_for_homer.txt')
subset = refseq[refseq['balb_nod_notx_1h_fc'] <= -1]
yzer.run_homer(subset,
'promoter_overlap_notx_1h_nod_down',
dirpath,
cpus=6,
center=False,
reverse=False,
pep_dirpath = yzer.get_filename(dirpath,
'{}_{}'.format(peptide, ab))
if True:
filename = yzer.get_filename(
pep_dirpath, '{}_{}_enhancers.txt'.format(peptide, ab))
data = yzer.import_file(filename)
data = data.fillna(0)
if True:
yzer.run_homer(data,
'all',
pep_dirpath,
cpus=8,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12],
mock=True)
if True:
subset = data[(data['id(2)'] == 0) & (data['id(3)'] == 0)]
yzer.run_homer(subset,
'only',
pep_dirpath,
cpus=8,
center=True,
reverse=False,
preceding=False,
size=200,
# Get venn-diagram sets for foxp3/me2
only_treg = treg[treg['naive_id'] == 0]
only_naive = naive[naive['treg_id'] == 0]
shared = treg[treg['naive_id'] > 0]
print len(only_treg), len(only_naive), len(shared)
datasets = [treg, naive, only_treg, only_naive, shared]
main_peak = ['treg', 'naive', 'treg', 'naive', 'treg']
names = [
x.format(antibody)
for x in ('all_treg_{0}_enhancers', 'all_naive_{0}_enhancers',
'only_treg_{0}_enhancers', 'only_naive_{0}_enhancers',
'shared_{0}_enhancers')
]
for i, subset in enumerate(datasets):
if i > 1: continue
subset['id'] = subset['{0}_id'.format(main_peak[i])]
subset['start'] = subset['{0}_start'.format(main_peak[i])]
subset['end'] = subset['{0}_end'.format(main_peak[i])]
yzer.run_homer(subset,
names[i],
motif_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15])
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = 20
if False:
subdata = data[data['tag_count'] > min_thresh]
subdata = subdata[subdata['tag_count(2)'] > min_thresh]
subdata = subdata[subdata['tag_count(3)'] <= 0]
subdata = subdata[subdata['tag_count(4)'] <= 0]
subdir = 'treg_shared_' + ab
yzer.run_homer(subdata,
subdir,
motif_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15],
mock=True)
output_file = yzer.get_filename(motif_dirpath, subdir,
subdir + '_enhancers.txt')
subdata.to_csv(output_file, header=True, index=False, sep='\t')
subdata = data[data['tag_count'] > min_thresh]
subdata = subdata[subdata['tag_count(2)'] <= 0]
subdata = subdata[subdata['tag_count(3)'] <= 0]
subdata = subdata[subdata['tag_count(4)'] <= 0]
subdir = 'ntreg_only_' + ab
yzer.run_homer(subdata,
subdir,
'{}_{}'.format(condition, ab))
if False:
filename = yzer.get_filename(cond_dirpath,
'{}_{}_enhancers.txt'.format(condition, ab))
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = 0
cutoff = 10000
data = data[data['tag_count'] > min_thresh]
data = data.sort('tag_count', ascending=False)[:cutoff]
yzer.run_homer(data,
'top_{}'.format(cutoff), cond_dirpath,
cpus=6, center=True, reverse=False, preceding=False,
size=200, length=[8, 10, 12, 15], mock=True)
for condition in ('itreg','treg'):
cond_dirpath = yzer.get_filename(dirpath,
'{}_{}'.format(condition, ab))
if False:
filename = yzer.get_filename(cond_dirpath,
'{}_{}_enhancers.txt'.format(condition, ab))
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = 0
cutoff = 10000
data = data[data['tag_count'] > min_thresh]
data['all'] = data['treg'][data['treg']\
[[c + '_id' for c in celltypes]].min(axis=1) > 0]
# Special cases
# Treg and Th1 shared and not shared, regardless of others
data['treg_th1_shared'] = data['treg'][data['treg']['th1_id'] > 0]
data['treg_not_th1'] = data['treg'][data['treg']['th1_id'] == 0]
data['th1_not_treg'] = data['th1'][data['th1']['treg_id'] == 0]
for k in sorted(data.keys()):
subset = data[k]
print k, len(subset)
if k in celltypes or len(subset) < 1000: continue
first_peak = k == 'all' and 'naive' or k.split('_')[0]
subset['id'] = subset[first_peak + '_id']
subset['start'] = subset[first_peak + '_start']
subset['end'] = subset[first_peak + '_end']
if k in ('treg_th1_shared', 'treg_not_th1', 'th1_not_treg'):
yzer.run_homer(subset,
'four_way_venn_' + k,
motif_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15])
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = get_threshold(seq)
data = data[data['tag_count'] >= min_thresh]
fold = 2
if True:
# ATAC peaks that are absent in the FOXO1 KO
foxo1_critical = data[
data['foxo1_ko_naive_atac_tag_count'] < min_thresh]
yzer.run_homer(foxo1_critical,
'foxo1_critical',
sample_dirpath,
cpus=10,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12],
mock=True)
print('foxo1_critical', len(foxo1_critical))
# ATAC peaks that don't change with KO of Foxo1
foxo1_independent = data[
(data['tag_count'] * fold >= data['foxo1_ko_naive_atac_tag_count'])
& (data['foxo1_ko_naive_atac_tag_count'] *
fold >= data['tag_count'])]
yzer.run_homer(foxo1_independent,
'foxo1_independent',
sample_dirpath,
cpus=10,
sample_prefix + '_enhancers.txt')
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = get_threshold(seq)
data = data[data['tag_count'] >= min_thresh]
fold = 2
if True:
naive_only = data[data['lcmv_d12_foxo1_tag_count'] < min_thresh]
yzer.run_homer(naive_only,
'naive_only',
sample_dirpath,
cpus=10,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12],
mock=True)
print('naive_only', len(naive_only))
shared = data[
(data['tag_count'] * fold >= data['lcmv_d12_foxo1_tag_count'])
& (data['lcmv_d12_foxo1_tag_count'] * fold >= data['tag_count'])]
yzer.run_homer(shared,
'shared',
sample_dirpath,
cpus=10,
center=True,
reverse=False,
go_path = yzer.get_and_create_path(dirpath, 'with_me3', 'go_analysis',
'0_8_min_lfc')
data = yzer.import_file(
yzer.get_filename(dirpath, 'transcript_vectors.txt'))
data = data.fillna(0)
data = data[data['naive_me3_tag_count'] + data['act_me3_tag_count'] > 0]
if False:
curr_path = yzer.get_and_create_path(dirpath, 'with_me3',
'motif_analysis')
yzer.run_homer(data,
'all_refseq_preceding',
curr_path,
center=False,
reverse=False,
preceding=True,
size=200,
cpus=6)
yzer.run_homer(data,
'all_refseq',
curr_path,
center=False,
reverse=False,
preceding=False,
size=200,
cpus=6)
yzer.run_homer(data,
'all_refseq',
curr_path,
center=True,
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/CD4TCells/H3K4me2/Analysis'
dirpath = yzer.get_path(dirpath)
motif_dirpath = yzer.get_filename(dirpath, 'motifs')
filename = yzer.get_filename(dirpath, 'thio_peak_vectors.txt')
data = yzer.import_file(filename)
data = data.fillna(0)
# me2
if True:
if False:
yzer.run_homer(data,
'thio_all',
motif_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15])
data = data[data['tss_id'] == 0]
if True:
yzer.run_homer(data,
'thio_h3k4me2_distal',
motif_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
for condition in ('treg', 'itreg', 'activated'):
cond_dirpath = yzer.get_filename(dirpath,
'{}_{}'.format(condition, ab))
if True:
filename = yzer.get_filename(
cond_dirpath, '{}_{}_enhancers.txt'.format(condition, ab))
data = yzer.import_file(filename)
data = data.fillna(0)
yzer.run_homer(data,
'all',
cond_dirpath,
cpus=6,
center=True,
reverse=False,
preceding=False,
size=200,
length=[8, 10, 12, 15],
mock=True)
for condition in ('itreg', 'treg'):
cond_dirpath = yzer.get_filename(dirpath,
'{}_{}'.format(condition, ab))
if False:
filename = yzer.get_filename(
cond_dirpath, '{}_{}_enhancers.txt'.format(condition, ab))
data = yzer.import_file(filename)
data = data.fillna(0)
#('with_pu_1_kla_dex', data[data['tag_count_5'] >= 10]),
('no_pu_1_kla_dex', data[data['tag_count_5'] < 10]),
('gt_partner', data[data['tag_count'] > 1.2*data['tag_count_2']]),
#('lt_partner', data[data['tag_count']*1.2 < data['tag_count_2']]),
('with_partner', data[data['tag_count_2'] >= 10]),
('no_partner', data[data['tag_count_2'] < 10]),
#('down_in_dex', data[data['dex_1_lfc'] <= -1]),
#('down_in_kla_dex', data[data['kla_dex_1_lfc'] <= -1]),
#('down_in_kla', data[data['kla_1_lfc'] <= -1]),
#('up_in_dex', data[data['dex_1_lfc'] >= 1]),
#('up_in_kla_dex', data[data['kla_dex_1_lfc'] >= 1]),
#('up_in_kla', data[data['kla_1_lfc'] >= 1]),
#('transrepressed', data[(data['kla_1_lfc'] >= 1) & (data['dex_over_kla_1_lfc'] <= -.58)]),
#('up_in_dex_down_in_kla_dex', data[(data['dex_1_lfc'] >= 1) & (data['kla_dex_1_lfc'] - data['dex_1_lfc'] <= -.58)]),
):
# We have multiple copies of peaks if they align to different transcripts
parent_path = yzer.get_and_create_path(motif_dirpath,
'peak_motifs_by_transcript_lfc',
peak_type, super_name)
curr_path = yzer.get_and_create_path(parent_path, name)
# Group them after selecting those that we want
dataset = dataset.groupby(['id','chr_name'],as_index=False).mean()
if name != 'all': bg = yzer.get_filename(parent_path, 'all','all','all_regions_for_homer.txt')
else: bg = None
yzer.run_homer(dataset, name, curr_path,
center=True, reverse=False, preceding=False, size=size,
cpus=6, bg=bg)
dirpath, 'boxplots_non_refseq_by_p65',
'enhancer_like_lose_p65_{0}x_change_dsg_only.txt'.format(
ratio)))
enhancers['glass_transcript_id'] = enhancers['id']
# Limit to peaks and touching transcripts, then pull out peaks that intersect our enhancer set
data = all_data[all_data['touches'] == 't']
data = data.merge(enhancers,
how='right',
on='glass_transcript_id',
suffixes=['', 'trans'])
curr_path = yzer.get_and_create_path(motif_dirpath,
'enhancer_like_lose_p65',
'ratio_{0}'.format(ratio))
# Group them after selecting those that we want
data = data.groupby(['id', 'chr_name'], as_index=False).mean()
#bg = yzer.get_filename(motif_dirpath,
# 'peak_motifs_by_transcript_lfc', 'p65_kla',
# 'all','all','all','all_regions_for_homer.txt')
yzer.run_homer(data,
'ratio_{0}'.format(ratio),
curr_path,
center=False,
reverse=False,
preceding=False,
size=size,
bg=None,
cpus=7)
filename = yzer.get_filename(sample_dirpath,
sample_prefix + '_enhancers.txt')
data = yzer.import_file(filename)
data = data.fillna(0)
min_thresh = get_threshold('atac')
data = data[data['tag_count'] >= min_thresh]
datasets[sample_prefix] = data
if False:
yzer.run_homer(data,
'all', sample_dirpath,
cpus=10, center=True, reverse=False, preceding=False,
size=200, length=[8, 10, 12], mock=True)
if True:
# Versus comparable sample in other breed
subdata = data[
data['{}_tag_count'.format(oth_breed[1][j])] < min_thresh]
yzer.run_homer(subdata,
'not_in_' + oth_breed[1][j], sample_dirpath,
cpus=10, center=True, reverse=False,
preceding=False,
size=200, length=[8, 10, 12], mock=True)
# Versus hi/lo sample and prior sample if not naive.
if j > 0:
if 'klrghi' in sample_prefix:
other = sample_prefix.replace('hi', 'lo')
