'''
Created on Nov 7, 2012
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.misc.cd4tcell_finland_2012.resources import replicate_sets
from glasslab.dataanalysis.motifs.motif_analyzer import MotifAnalyzer
if __name__ == '__main__':
yzer = MotifAnalyzer()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/CD4TCells_Finland_2012/Analysis_2013_02'
dirpath = yzer.get_path(dirpath)
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,
for ratio in (3, 2, 1.5):
enhancers = yzer.import_file(
yzer.get_filename(
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,
peak_type = 'p65_kla_dex'
data = yzer.import_file(
yzer.get_filename(
dirpath,
'from_peaks/{0}_promoter_vectors.txt'.format(peak_type)))
data['id'] = data['peak_id']
thresh = 2
ids = grouped[
grouped['relevant_sets_primary'] >= thresh]['glass_transcript_id']
dataset = data[data['glass_transcript_id'].isin(ids)]
if True:
yzer.prep_files_for_homer(data,
'all_{0}_200'.format(peak_type),
yzer.get_and_create_path(
dirpath, 'from_peaks', peak_type),
center=True,
reverse=False,
preceding=False,
size=200)
yzer.prep_files_for_homer(
dataset,
'paused_in_{0}_at_least_{1}_min_{2}_down_in_dex_{3}_200'.format(
thresh, min_ratio, secondary_min_ratio, peak_type),
yzer.get_and_create_path(dirpath, 'from_peaks', peak_type),
center=True,
reverse=False,
preceding=False,
size=200)
left outer join
chipseq.peak_{} p{counter}
on p1.chromosome_id = p{counter}.chromosome_id
and p1.start_end && p{counter}.start_end
'''.format(oth_breed[0][i], counter=counter))
# Put it all together
sql += ',\n'.join(selects)
sql += '''
from chipseq.peak_{} p1
join genome_reference_mm10.chromosome chr
on p1.chromosome_id = chr.id
'''.format(sample)
sql += ''.join(joins)
sql += '''
left outer join genome_reference_mm10.sequence_transcription_region reg
on p1.chromosome_id = reg.chromosome_id
and p1.start_end && reg.start_site_1000
where reg.id is NULL;
'''
print(sql)
# Set up output dir
sample_path = yzer.get_and_create_path(dirpath, curr_name)
# Get data
data = dataframe_from_query(sql, engine)
output_file = yzer.get_filename(sample_path,
curr_name + '_enhancers.txt')
data.to_csv(output_file, sep='\t', header=True, index=False)
'''
Created on Feb 8, 2013
@author: karmel
'''
from glasslab.dataanalysis.motifs.motif_analyzer import MotifAnalyzer
if __name__ == '__main__':
yzer = MotifAnalyzer()
base_dirpath = yzer.get_path(
'karmel/GlassLab/Notes_and_Reports/NOD_BALBc/ThioMacs/Analysis_2013_02/'
)
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,
#('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)
'''
Created on Feb 20, 2013
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.motifs.motif_analyzer import MotifAnalyzer
if __name__ == '__main__':
yzer = MotifAnalyzer()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/CD4TCells/Rudensky_enhancers'
dirpath = yzer.get_path(dirpath)
motifs_dirpath = yzer.get_and_create_path(dirpath, 'motifs')
peak_pretty = 'Foxp3'
peak = peak_pretty.lower()
foxp3 = yzer.import_file(
yzer.get_filename(dirpath,
'{0}_1_with_naive_me2.txt'.format(peak))).fillna(0)
naive = yzer.import_file(
yzer.get_filename(dirpath,
'naive_me2_with_{0}.txt'.format(peak))).fillna(0)
# Filter out promoters
foxp3 = foxp3[foxp3['tss_id'] == 0]
naive = naive[naive['tss_id'] == 0]
# Get venn-diagram sets for foxp3/me2
only_foxp3 = foxp3[foxp3['naive_id'] == 0]
only_naive = naive[naive['foxp3_1_id'] == 0]
size = 200
if True:
all_data = yzer.import_file(
yzer.get_filename(dirpath,
'{0}_vectors.txt'.format(peak_type)))
all_data = all_data.fillna(0)
for super_name, data in ((
'all',
all_data,
), ):
for name, dataset in ((
'all',
data,
), ):
# We have multiple copies of peaks if they align to different transcripts
curr_path = yzer.get_and_create_path(
dirpath, peak_type, super_name, name)
# Group them after selecting those that we want
dataset = dataset.groupby(['id', 'chr_name'],
as_index=False).mean()
yzer.run_homer(dataset,
name,
curr_path,
center=True,
reverse=False,
preceding=False,
size=size,
cpus=6)
& (data['tag_count_4'] >= 10)]),
('with_gr_either', data[(data['tag_count_3'] >= 10) |
(data['tag_count_4'] >= 10)]),
('with_gr_kla_dex', data[data['tag_count_3'] >= 10]),
('with_gr_dex', data[data['tag_count_4'] >= 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)]),
):
curr_path = yzer.get_and_create_path(
motif_dirpath, 'redistributed_pairs',
'high_quality_pairs_vs_kla_bg', super_name, name)
# Group them after selecting those that we want
dataset = dataset.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(dataset,
name,
curr_path,
center=True,
reverse=False,
