if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/' +\
'Miscellaneous_Collaborations/Rodrigo_CD8s_2014_09/Enhancers_set2'
dirpath = yzer.get_path(dirpath)
save_path = yzer.get_and_create_path(
dirpath, 'Figures', 'Enhancer_counts')
datasets = {}
breed_sets = get_breed_sets()
for i, (samples, short_names) in enumerate(breed_sets):
oth_breed = breed_sets[1 - i]
for j, sample_prefix in enumerate(short_names):
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)
min_thresh = get_threshold('atac')
data = data[data['tag_count'] >= min_thresh]
datasets[sample_prefix] = data
# How many denovo d7 enhancers are also in foxo1 kos?
for celltype in ('hi', 'lo'):
d7 = datasets['klrg{}_d7'.format(celltype)]
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/NOD_BALBc/ThioMacs/Analysis_2013_02/'
dirpath_bmdc = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/NOD_BALBc/BMDCs/Analysis_2013_03/'
dirpath = yzer.get_path(dirpath)
dirpath_bmdc = yzer.get_path(dirpath_bmdc)
img_dirpath = yzer.get_and_create_path(dirpath, 'bmdc_vs_thiomac')
thio = yzer.import_file(
yzer.get_filename(dirpath, 'transcript_vectors.txt'))
bmdc = yzer.import_file(
yzer.get_filename(dirpath_bmdc, 'transcript_vectors.txt'))
sets = []
for data in (thio, bmdc):
data = data.fillna(0)
refseq = yzer.get_refseq(data)
# Remove low tag counts
#refseq = refseq[refseq['transcript_score'] >= 4]
sets.append(refseq)
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'peak_scatterplots')
if True:
for main, compare, basal_cond in (('p65', 'GR', 'KLA'), ('GR', 'p65',
'Dex')):
data = yzer.import_file(
yzer.get_filename(dirpath, 'motifs', 'from_peaks',
'{0}_kla_dex_vectors.txt'.format(main)))
data = data.fillna(0)
data = data.groupby(['id', 'chr_name'], as_index=False).mean()
xcolname, ycolname = 'tag_count_2', 'tag_count' #'p65_kla_tag_count', 'p65_kla_dex_tag_count',
data = data[data[ycolname] >= 10]
cond_1 = (data['tag_count_3'] == 0)
cond_2 = (data['tag_count_3'] > 0) & (data['tag_count_3'] <
data['tag_count_4'])
cond_3 = (data['tag_count_3'] > 0) & (data['tag_count_3'] >=
data['tag_count_4'])
ax = None
for show_points in (True, False):
ax = yzer.scatterplot(
from matplotlib import pyplot
import numpy
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'piecharts_from_p65_gr')
if True:
for main, compare, basal_cond in (
('GR', 'p65', 'Dex'),
('p65', 'GR', 'KLA'),
):
data = yzer.import_file(
yzer.get_filename(dirpath, 'motifs', 'from_peaks',
'{0}_kla_dex_vectors.txt'.format(main)))
# Get nearby peaks first
ids_with_nearby = data[
(data['distance_to_tss_2'].isnull() == False)
& (data['distance_to_peak_2'] <= 1000)]['id']
data = data.fillna(0)
data = data.groupby(['id', 'chr_name'], as_index=False).mean()
data = data[data['tag_count'] >= 10]
total = len(data)
has_nearby_peak = data['id'].isin(ids_with_nearby)
bound_by_main_not_comp_not_basal = data[~has_nearby_peak &
(data['tag_count_3'] < 10)]
'''
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.dataanalysis.misc.rodrigo.samples import sample_name,\
get_threshold
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/' +\
'Miscellaneous_Collaborations/Rodrigo_CD8s_2014_09/Promoters'
dirpath = yzer.get_path(dirpath)
cond, seq, breed = ('naive', 'atac', '')
wt_prefix = sample_name(cond, seq, breed)
ko_prefix = sample_name(cond, seq, 'foxo1_ko_')
wt_dirpath = yzer.get_filename(dirpath, wt_prefix)
ko_dirpath = yzer.get_filename(dirpath, ko_prefix)
wt_filename = yzer.get_filename(wt_dirpath,
wt_prefix + '_promoters.txt')
ko_filename = yzer.get_filename(ko_dirpath,
ko_prefix + '_promoters.txt')
wt_data = yzer.import_file(wt_filename)
wt_data = wt_data.fillna(0)
ko_data = yzer.import_file(ko_filename)
ko_data = ko_data.fillna(0)
min_thresh = get_threshold(seq)
wt_data = wt_data[wt_data['tag_count'] >= min_thresh]
ko_data = ko_data[ko_data['tag_count'] >= min_thresh]
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
from glasslab.dataanalysis.misc.gr_project_2012.v1.enhancer_subsets_for_supershift import ucsc_link_cleanup
import numpy
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = yzer.get_path(dirpath)
peak_type = 'p65'
img_dirpath = yzer.get_and_create_path(dirpath, 'boxplots_non_refseq_by_{0}'.format(peak_type))
transcripts = yzer.import_file(yzer.get_filename(dirpath, 'motifs', 'transcript_vectors_with_nearby_peaks.txt'))
if True:
pu_1 = False
for ratio in (1.5, 2, 3):
data = transcripts[transcripts['refseq'] == 'f']
data = data[data['has_infrastructure'] == 0]
data = data[data['length'] < 6000]
data = data[data['dex_1_lfc'] < 1]
data = data[data['kla_1_lfc'] >= 1]
data = data[data['gr_kla_dex_tag_count'] > 0]
data = data[data['gr_fa_kla_dex_tag_count'] == 0]
print len(data)
if pu_1: data = data[data['pu_1_kla_tag_count'] + data['pu_1_kla_tag_count'] > 0]
'''
Created on Jun 26, 2012
@author: karmel
'''
from glasslab.dataanalysis.misc.gr_project_2012.elongation import set_up_sequencing_run_ids, \
get_sequencing_run_id_sets, get_rep_string, total_tags_per_run
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.dataanalysis.motifs.motif_analyzer import MotifAnalyzer
if __name__ == '__main__':
grapher = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = grapher.get_path(dirpath)
filename = grapher.get_filename(dirpath, 'transcript_vectors.txt')
data = grapher.import_file(filename)
run_ids = set_up_sequencing_run_ids()
dmso, kla, kla_dex, all_dmso, all_kla, all_kla_dex = get_sequencing_run_id_sets(
)
total_tags = total_tags_per_run()
# Norm sum scalars listed for all, group 1, group 2, group 3, group 4
kla_scalars = [1.223906, 1.281572, 1.118363, 1.104860, 1.503260]
kla_dex_scalars = [1.182574, 1.147695, 1.248636, 1.069588, 1.388871]
dex_over_kla_scalars = [1.069073, 0.967659, 1.122628, 1.008758, 0.927466]
for i, scalar in enumerate(kla_scalars):
data = grapher.normalize(data,
'kla_{0}tag_count'.format(get_rep_string(i)),
Note: Made font.weight = bold and axes.titlesize = 24, font.size = 16 in matplotlibrc
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/Glass Atlas/NAR_review_data/vs_homer'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'scatterplots')
data = yzer.import_file(
yzer.get_filename(dirpath, 'tag_count_by_refseq.txt'))
data['sum'] = nonzero(data['sum'].fillna(0))
homer_data = yzer.import_file(
yzer.get_filename(dirpath, 'RNA_GroSeq_CountsGenes.txt'))
homer_data['sequence_identifier'] = homer_data['Gene ID']
homer_data['homer_tag_count'] = nonzero(homer_data[
'ThioMac-GroSeq-notx-110513/ genes (Total: 12166480.0) normFactor 0.82']
.fillna(0))
homer_data = homer_data[['sequence_identifier', 'homer_tag_count']]
merged = data.merge(homer_data, how='inner', on='sequence_identifier')
merged = merged.fillna(1)
if True:
ax = yzer.scatterplot(merged,
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/CD4TCells/Oshea_enhancers/ctcf_stat1_overlap'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'figures')
data = yzer.import_file(
yzer.get_filename(dirpath, 'ctcf_with_stat1_binding.txt')).fillna(0)
with_stat1 = data[data['p2_tag_count'] > 0]
without_stat1 = data[data['p2_tag_count'] == 0]
if True:
ax = yzer.piechart(
[len(with_stat1), len(without_stat1)],
['CTCF sites with STAT1', 'CTCF sites without STAT1'],
title='DP Thymocyte CTCF Sites with STAT1 in Th1 Cells',
save_dir=img_dirpath,
show_plot=True)
data['tag_count_nonzero'] = nonzero(data['tag_count'])
data['p2_tag_count_nonzero'] = nonzero(data['p2_tag_count'])
ax = yzer.scatterplot(
data,
'tag_count_nonzero',
return none, lt, lt_with_gain, nc, nc_with_gain, gt, gt_with_gain
def get_filters_transcript(subdata, xcol, ycol):
down_in_kla = subdata['kla_1_lfc'] <= -1
nc_in_kla = subdata['kla_1_lfc'].abs() < 1
up_in_kla = subdata['kla_1_lfc'] >= 1 & (subdata['dex_over_kla_1_lfc'] > -.58)
trans = up_in_kla & (subdata['dex_over_kla_1_lfc'] <= -.58)
return down_in_kla, nc_in_kla, up_in_kla, trans
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'bargraphs_from_p65_gr')
data = yzer.import_file(yzer.get_filename(dirpath, 'motifs','transcript_vectors.txt'))
data = data[data['has_refseq'] == 1]
if True:
for main, compare, basal_cond, comp_cond in (('p65','GR', 'KLA', 'Dex'),('GR','p65', 'Dex', 'KLA')):
data = data.fillna(0)
data = data.groupby(['id','chr_name'],as_index=False).mean()
tag_count_1 = '{0}_kla_dex_tag_count'.format(main.lower())
tag_count_2 = '{0}_{1}_tag_count'.format(main.lower(), basal_cond.lower())
tag_count_3 = '{0}_kla_dex_tag_count'.format(compare.lower())
tag_count_4 = '{0}_{1}_tag_count'.format(compare.lower(), comp_cond.lower())
datasets = [data[filterset] for filterset in get_filters_many(data, tag_count_1,
tag_count_2, tag_count_3, tag_count_4)]
condition, or with an unequal number of genes in each condition.
For those, we will sort genes in each condition by number
of interactions, and allow for null values when there is a number
mismatch.
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
import numpy
kla_col = 'kla_6h_lfc'
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/HiC/'
dirpath = yzer.get_path(dirpath)
data_dirpath = yzer.get_filename(dirpath, 'enhancer_sets')
img_dirpath = yzer.get_and_create_path(dirpath,
'genes_to_average_enhancer_lfc')
keys = ('all', 'notx', 'kla', 'notx_only', 'kla_only', 'shared_enh')
if True:
interactions = yzer.import_file(
yzer.get_filename(data_dirpath,
'transcript_pairs_refseq_with_me2.txt'))
interactions = interactions[interactions['count'] > 1]
all_transcripts = yzer.import_file(
yzer.get_filename(data_dirpath, 'transcript_vectors.txt'))
transcripts = all_transcripts[['id', 'kla_lfc', 'kla_6h_lfc']]
data['ucsc_link_nod'] = data['ucsc_link_nod'].map(
lambda x: '<a href={0} target="_blank">UCSC</a>'.format(
x.replace('nod_balbc', 'gr_project_2012')))
return data
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = yzer.get_path(dirpath)
save_dirpath = yzer.get_and_create_path(dirpath,
'subgroups_for_supershift')
transcripts = yzer.import_file(
yzer.get_filename(dirpath, 'motifs', 'transcript_vectors.txt'))
data = transcripts[transcripts['refseq'] == 'f']
data = data[data['has_infrastructure'] == 0]
data = data[data['length'] < 6000]
data = data[data['dex_1_lfc'] < 1]
data = data[data['kla_1_lfc'] >= 1]
data = data.fillna(0)
data = ucsc_link_cleanup(data)
if False:
# First get sets for Negative controls
tfs = ['p65', 'pu_1', 'gr', 'gr_fa']
for tf in tfs:
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
from glasslab.dataanalysis.motifs.motif_analyzer import MotifAnalyzer
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/CD4TCells/Oshea_enhancers/ctcf_across_celltypes'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'figures')
dp = yzer.import_file(
yzer.get_filename(dirpath, 'dp_with_thiomac_ctcf.txt')).fillna(0)
thio = yzer.import_file(
yzer.get_filename(dirpath, 'thiomac_with_dp_ctcf.txt')).fillna(0)
# Get venn-diagram sets
only_dp = dp[dp['thiomac_ctcf_tag_count'] == 0]
only_thio = thio[thio['dp_ctcf_tag_count'] == 0]
shared = dp[dp['thiomac_ctcf_tag_count'] != 0]
shared_check = thio[thio['dp_ctcf_tag_count'] != 0]
print len(only_dp), len(only_thio), len(shared), len(shared_check)
data = shared.append(only_dp, ignore_index=True)
data = data.append(only_thio, ignore_index=True)
data['dp_nonzero'] = nonzero(data['dp_ctcf_tag_count'])
data['thio_nonzero'] = nonzero(data['thiomac_ctcf_tag_count'])
data['region_end'] = data.apply(lambda row: int(
max(row['transcription_end'], row['transcription_end_5'])),
axis=1)
# Get rid of pairs that are really just overlapping
data = data[data['region_end'] - data['region_start'] >= 300]
#data = data[data['region_end'] - data['region_start'] <= 10000]
return data
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = yzer.get_path(dirpath)
motif_dirpath = yzer.get_filename(dirpath, 'motifs', 'from_peaks')
transcripts = yzer.import_file(
yzer.get_filename(dirpath, 'motifs', 'transcript_vectors.txt'))
transcripts['glass_transcript_id'] = transcripts['id']
if True:
all_data = yzer.import_file(
yzer.get_filename(
dirpath, 'redistribution',
'p65_peaks_bigger_in_kla_dex_with_nearby_bigger_kla_peaks.txt')
)
data = get_high_quality_pairs(all_data, transcripts)
'''
# Print these out to send to collaborators.
@author: karmel
What do enhancers that are gaining methyl with KLA look like?
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
from collections import OrderedDict
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/HiC/'
dirpath = yzer.get_path(dirpath)
data_dirpath = yzer.get_filename(dirpath, 'enhancer_sets')
kla_col = 'kla_lfc'
tss_only = False
img_dirpath = yzer.get_and_create_path(
dirpath, 'interactions_by_kla_lfc', tss_only and 'genic'
or 'all_interactions', 'lfc_2')
# File generated in novel_me2_sites
enhancers = yzer.import_file(
yzer.get_filename(
data_dirpath,
'all_enhancers_with_me2_and_{0}interaction_stats.txt'.format(
tss_only and 'tss_' or '')))
@author: karmel
What do enhancers that are gaining methyl with KLA look like?
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
from collections import OrderedDict
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/HiC/'
dirpath = yzer.get_path(dirpath)
data_dirpath = yzer.get_filename(dirpath, 'enhancer_sets')
kla_col = 'kla_lfc'
tss_only = False
img_dirpath = yzer.get_and_create_path(
dirpath, 'novel_me2_sites', tss_only and 'genic' or 'all_interactions',
'ratio_10')
if False:
enhancers = yzer.import_file(
yzer.get_filename(data_dirpath,
'all_distal_enhancers_inc_me2.txt'))
all_transcripts = yzer.import_file(
yzer.get_filename(data_dirpath, 'transcript_vectors.txt'))
'''
Created on Jan 3, 2013
@author: karmel
Plot gen-enhancer me2 LFC; do we see correlation?
'''
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
import numpy
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/HiC/'
dirpath = yzer.get_path(dirpath)
data_dirpath = yzer.get_filename(dirpath, 'enhancer_sets')
img_dirpath = yzer.get_and_create_path(dirpath, 'gene_enhancer_me2_lfc',
'scatterplots')
interactions = yzer.import_file(
yzer.get_filename(
data_dirpath,
'transcript_pairs_enhancer_with_anything_with_me2_inc_me2_counts.txt'
))
interactions = interactions[interactions['count'] > 1]
all_transcripts = yzer.import_file(
yzer.get_filename(data_dirpath, 'transcript_vectors.txt'))
for me2_timepoint in ('6h', '24h'):
me2_col = 'me2_{0}_ratio'.format(me2_timepoint)
kla_col = 'kla_lfc'
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
from glasslab.dataanalysis.motifs.motif_analyzer import MotifAnalyzer
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/CD4TCells/Oshea_enhancers/peak_overlaps'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'figures')
peak_pretty = 'p300'
peak = peak_pretty.lower()
th1 = yzer.import_file(
yzer.get_filename(dirpath,
'th1_with_th2_{0}.txt'.format(peak))).fillna(0)
th2 = yzer.import_file(
yzer.get_filename(dirpath,
'th2_with_th1_{0}.txt'.format(peak))).fillna(0)
# Filter out promoters
th1 = th1[th1['tss_id'] == 0]
th2 = th2[th2['tss_id'] == 0]
# Get venn-diagram sets
only_th1 = th1[th1['p2_id'] == 0]
only_th2 = th2[th2['p2_id'] == 0]
shared = th1[th1['p2_id'] != 0]
shared_check = th2[th2['p2_id'] != 0]
print len(only_th1), len(only_th2), len(shared), len(shared_check)
'''
Created on Oct 8, 2012
@author: karmel
'''
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from matplotlib import pyplot
from glasslab.dataanalysis.misc.gr_project_2012.boxplots_redistribution_pairs import get_high_quality_pairs
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
dirpath = yzer.get_path(dirpath)
motif_dirpath = yzer.get_filename(dirpath, 'motifs', 'from_peaks')
transcripts = yzer.import_file(
yzer.get_filename(dirpath, 'motifs', 'transcript_vectors.txt'))
transcripts['glass_transcript_id'] = transcripts['id']
if True:
all_data = yzer.import_file(
yzer.get_filename(
dirpath, 'redistribution',
'p65_peaks_bigger_in_kla_dex_with_nearby_bigger_kla_peaks.txt')
)
data = get_high_quality_pairs(all_data, transcripts)
data = data.groupby(['id', 'chr_name'], as_index=False).mean()
Note: Made font.weight = bold and axes.titlesize = 24 in matplotlibrc
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import pandas_min
from glasslab.dataanalysis.misc.demoatlas.rpkm_to_score import PrettyAxisGrapher
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/Glass Atlas/Post_gene_transcripts'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'scatterplots')
data = yzer.import_file(yzer.get_filename(dirpath,'within_1kb_gap_500bp_with_nc.txt'))
refseq = yzer.import_file(yzer.get_filename(dirpath,'expressed_refseq_gap_500bp.txt'))
refseq_with_runoff = refseq[refseq['id'].isin(data['gene_id'])]
refseq_no_runoff = refseq[~refseq['id'].isin(data['gene_id'])]
if True:
print len(refseq_no_runoff)
print refseq_no_runoff.tail(100).to_string()
# Calculate length of runoff
data['length'] = data['transcription_end'] - data['transcription_start'] + 1
data['gene_length'] = data['gene_end'] - data['gene_start'] + 1
# What might be correlated with length of runoff?
if False:
yzer.scatterplot(data, 'gene_length', 'length', log=True)
'''
Created on Oct 26, 2012
@author: karmel
'''
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from matplotlib import pyplot
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/GR_Analysis/enhancer_classification'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'piecharts_for_genes_by_mechanism')
data = yzer.import_file(yzer.get_filename(dirpath, 'enhancers_with_nearest_gene.txt'))
data['ucsc_link_nod'] = data['ucsc_link_nod'].apply(lambda s: s.replace('nod_balbc','gr_project_2012'))
draw_pies = True
min_tags = 30
ratio = 1.5
# Make sure we have dimethyl
data = data[data.filter(like='h3k4me2').max(axis=1) > min_tags]
data = data[data['minimal_distance'] >= 1000]
#data = yzer.collapse_strands(data)
transcripts = yzer.import_file(yzer.get_filename(dirpath, 'transcript_vectors.txt'))
transcripts['nearest_refseq_transcript_id'] = transcripts['id']
# Join, keeping all transcripts
data = data.merge(transcripts, how='left', on='nearest_refseq_transcript_id', suffixes=['','_trans'])
if __name__ == '__main__':
enhancer_counts = True # Are we looking at enhancer interactions (False) or counts (True)?
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/HiC/enhancers_by_gene_length'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'scatterplots')
counted = enhancer_counts and 'enhancer' or 'interaction'
# The first set has length with interaction counts;
# the second has length for all transcripts, even those without interactions.
# We want to merge such that we add the interaction-less genes with a count of 0.
data = yzer.import_file(yzer.get_filename(dirpath,'{0}_counts_by_refseq.txt'.format(counted)))
all_data = yzer.import_file(yzer.get_filename(dirpath,'refseq_all.txt'))
all_data = all_data[~all_data['id'].isin(data['id'])]
data = pandas.concat([data, all_data])
data = data.reset_index().fillna(0)
notx = data[data['sequencing_run_id'] == 765]
kla_30m = data[data['sequencing_run_id'] == 766]
kla_4h = data[data['sequencing_run_id'] == 773]
no_intxns = data[data['sequencing_run_id'] == 0]
# Zero won't show up in a log plot, so add one.
no_intxns['count'] = 1
ax = yzer.scatterplot(no_intxns,
Note: Made font.weight = bold and axes.titlesize = 24, font.size = 16 in matplotlibrc
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/Glass Atlas/Demo-data'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath,
'refseq_to_homer/large_gap_500bp')
data = yzer.import_file(
yzer.get_filename(dirpath, 'refseq_tag_counts_500bp.txt'))
data['sum'] = nonzero(data['sum'].fillna(0))
homer_data = yzer.import_file(
yzer.get_filename(dirpath, 'RNA_GroSeq_CountsGenes.txt'))
homer_data['sequence_identifier'] = homer_data['Gene ID']
homer_data['homer_tag_count'] = nonzero(homer_data[
'ThioMac-GroSeq-notx-110513/ genes (Total: 12166480.0) normFactor 0.82']
.fillna(0))
homer_data = homer_data[['sequence_identifier', 'homer_tag_count']]
merged = data.merge(homer_data, how='inner', on='sequence_identifier')
merged = merged.fillna(1)
if True:
ax = yzer.scatterplot(merged,
@author: karmel
Note: Made font.weight = normal and axes.titlesize = 24 in matplotlibrc
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.dataanalysis.misc.demoatlas.rpkm_to_score import PrettyAxisGrapher
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/Glass Atlas/NAR_review_data/Post-gene'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'scatterplots')
data = yzer.import_file(
yzer.get_filename(dirpath, 'post_gene_transcripts.txt'))
refseq = yzer.import_file(
yzer.get_filename(dirpath, 'all_expressed_refseq.txt'))
refseq_with_runoff = refseq[refseq['id'].isin(data['gene_id'])]
refseq_no_runoff = refseq[~refseq['id'].isin(data['gene_id'])]
if False:
print len(refseq_no_runoff)
print refseq_no_runoff.tail(100).to_string()
# Calculate length of runoff
data[
'length'] = data['transcription_end'] - data['transcription_start'] + 1
data['gene_length'] = data['gene_end'] - data['gene_start'] + 1
# What might be correlated with length of runoff?
from matplotlib import pyplot
from glasslab.utils.functions import nonzero
from glasslab.dataanalysis.misc.gr_project_2012.v1.elongation import total_tags_per_run
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/GR_Analysis/enhancer_classification'
dirpath = yzer.get_path(dirpath)
consistent = False
img_dirpath = yzer.get_and_create_path(
dirpath, 'boxplots_by_expression', consistent and 'consistent'
or 'rep1')
data = yzer.import_file(
yzer.get_filename(dirpath, 'enhancers_with_nearest_gene.txt'))
data['ucsc_link_nod'] = data['ucsc_link_nod'].apply(
lambda s: s.replace('nod_balbc', 'gr_project_2012'))
draw_pies = True
min_tags = 30
ratio = 1.5
# Make sure we have dimethyl
data = data[data.filter(like='h3k4me2').max(axis=1) > min_tags]
data = data[data['minimal_distance'] >= 1000]
transcripts = yzer.import_file(
yzer.get_filename(dirpath, 'transcript_vectors.txt'))
transcripts['nearest_refseq_transcript_id'] = transcripts['id']
data = data.merge(transcripts,
how='left',
'''
Created on Jan 9, 2013
@author: karmel
Do novel interactions gain or lose me2?
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/HiC/'
dirpath = yzer.get_path(dirpath)
data_dirpath = yzer.get_filename(dirpath, 'enhancer_sets')
img_dirpath = yzer.get_and_create_path(dirpath,
'novel_interactions_kla_lfc',
'all_interactions')
interactions = yzer.import_file(
yzer.get_filename(
data_dirpath,
'transcript_pairs_enhancer_with_anything_with_me2_inc_me2_counts.txt'
))
interactions = interactions[interactions['count'] > 1]
all_transcripts = yzer.import_file(
yzer.get_filename(data_dirpath, 'transcript_vectors.txt'))
kla_col = 'kla_lfc'
'''
Created on Sep 7, 2012
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
if __name__ == '__main__':
grapher = SeqGrapher()
base_dirpath = 'karmel/Desktop/Projects/Classes/Rotations/Finland_2012/GR_Project/'
base_dirpath = grapher.get_path(base_dirpath)
dirpath = grapher.get_filename(base_dirpath, 'motifs')
filename = grapher.get_filename(dirpath, 'transcript_vectors.txt')
data = grapher.import_file(filename)
# Boxplots for gr_dex peaks by lfc in Dex
if False:
#data = data[data['distal'] == 't']
data = data[data['has_refseq'] == 1]
down = data[data['dex_1_lfc'] <= -1]
up = data[data['dex_1_lfc'] >= 1]
nc = data[abs(data['dex_1_lfc']) < 1]
key = 'p65_kla_tag_count'
datasets = [down[key],nc[key],up[key]]
datasets = [d['p65_kla_dex_tag_count'] - d[key] for d in [down, nc, up]]
Created on Feb 12, 2013
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.dataanalysis.motifs.motif_analyzer import MotifAnalyzer
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/NOD_BALBc/ThioMacs/Analysis_2013_02/'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'srf_binding')
data = yzer.import_file(
yzer.get_filename(dirpath, 'transcript_vectors.txt'))
data = data.fillna(0)
data = data[data[['nod_notx_1h_tag_count', 'balb_notx_1h_tag_count']].max(
axis=1) >= 10]
subsets = [
data,
data[(data['has_refseq'] == 1) & (data['transcript_score'] >= 4)],
data[(data['distal'] == 't') & (data['h3k4me2_tag_count'] > 10)]
]
# Add in nearest genes for enhancers
enh = subsets[2].copy()
nearest_genes = yzer.import_file(
yzer.get_filename(dirpath, 'enhancers_with_nearest_genes.txt'))
'''
Created on Jan 30, 2013
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from collections import OrderedDict
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/HiC/'
dirpath = yzer.get_path(dirpath)
data_dirpath = yzer.get_filename(dirpath, 'enhancer_rewiring_lfc')
transcripts = yzer.import_file(
yzer.get_filename(dirpath, 'enhancer_sets', 'transcript_vectors.txt'))
sets = OrderedDict((
('all',
yzer.import_file(yzer.get_filename(data_dirpath, 'all_vectors.cdt'))),
#('all_6h', yzer.import_file(yzer.get_filename(data_dirpath,'kla_6h','all_vectors.cdt'))),
('rewired',
yzer.import_file(
yzer.get_filename(data_dirpath, 'rewired_vectors.cdt'))),
#('rewired_6h', yzer.import_file(yzer.get_filename(data_dirpath,'kla_6h','rewired_vectors.cdt'))),
('shared',
yzer.import_file(yzer.get_filename(data_dirpath,
'shared_vectors.cdt'))),
))
for key, val in sets.items():
@author: karmel
'''
from __future__ import division
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
from glasslab.utils.functions import nonzero
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/CD4TCells/Oshea_enhancers/peak_overlaps'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'figures')
peak = 'p300'
th1 = yzer.import_file(
yzer.get_filename(dirpath,
'th1_with_th2_{0}.txt'.format(peak))).fillna(0)
th2 = yzer.import_file(
yzer.get_filename(dirpath,
'th2_with_th1_{0}.txt'.format(peak))).fillna(0)
# Filter out promoters
th1 = th1[th1['tss_id'] == 0]
th2 = th2[th2['tss_id'] == 0]
th1['th1_tag_count'] = nonzero(th1['tag_count'])
th1['th2_tag_count'] = nonzero(th1['p2_tag_count'])
th2['th1_tag_count'] = nonzero(th2['tag_count'])
th2['th2_tag_count'] = nonzero(th2['p2_tag_count'])
with_ctcf = th1[th1['ctcf_tag_count'] > 0]
without_ctcf = th1[th1['ctcf_tag_count'] == 0]
