from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
if __name__ == '__main__':
yzer = SeqGrapher()
dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/Glass Atlas/NAR_review_data/'
dirpath = yzer.get_path(dirpath)
img_dirpath = yzer.get_and_create_path(dirpath, 'hg19_mcf7_pie_charts')
yzer.legend_location = 'lower left'
pie1 = '''Annotated by RefSeq and/or ncRNA.org 14,022
Unannotated 67,046'''
pie1 = [row.split(' ') for row in pie1.split('\n')]
pie1 = zip(*pie1)
yzer.piechart(map(lambda s: int(s.replace(',', '')), pie1[1]),
pie1[0],
title='Hah et al MCF-7 Transcripts\nwith Score >= 1',
save_dir=img_dirpath,
show_plot=True)
pie2 = '''Promoter-associated RNA 7,055
Antisense of RefSeq 7,539
Other RefSeq Proximal 13,664
Distal with H3K4me2 2,352
Distal w/in 2kbp of H3K4me2 5,524
Distal remainder with LINE 16,292
Remainder 14,620'''
pie2 = [row.split(' ') for row in pie2.split('\n')]
pie2 = zip(*pie2)
yzer.legend_columns = 2
yzer.piechart(
map(lambda s: int(s.replace(',', '')), pie2[1]),
]
in_dex_no_p65 = dataset[(dataset['gr_dex_tag_count'] > min_tags) &
(dataset['p65_kla_tag_count'] + dataset['p65_kla_dex_tag_count'] <= min_tags)
]
kla_only_no_p65 = dataset[(dataset['gr_dex_tag_count'] <= min_tags) &
(dataset['gr_kla_dex_tag_count'] > min_tags) &
(dataset['p65_kla_tag_count'] + dataset['p65_kla_dex_tag_count'] <= min_tags)
]
sets = [tethered, direct_comp_gr, indirect_comp_gr,
direct_comp_p65, cobound,
direct_novel, indirect_novel,
in_dex_no_p65, kla_only_no_p65]
id_sets = [d['nearest_refseq_transcript_id'].unique() for d in sets]
for id_set in id_sets: total_gr = total_gr - set(id_set)
counts = [len(id_set) for id_set in id_sets] + [len(total_gr)]
labels = ['Tethered', 'Direct competition, favor to GR', 'Indirect competition, favor to GR',
'Direct competition, favor to p65', 'Directly co-bound without loss',
'Directly bound novel p65 site', 'Indirectly bound novel p65 site',
'Has GR in Dex, no p65', 'Has GR in KLA+Dex only, no p65',
'Other with GR']
if draw_pies:
yzer.piechart(counts, labels,
title='Genes near Enhancer-like Subsets {0} with GR\nby Putative Enhancer Mechanism'.format(name.title()),
small_legend=True,
save_dir=img_dirpath, show_plot=True)
yzer.boxplot([gp['naive_foxo1_tag_count'] for gp in groups],
labels,
title='Foxo1 tags in ATAC-seq regions by group',
ylabel='Foxo1 peak tag count', save_dir=save_path,
show_plot=False)
yzer.boxplot([gp['lcmv_d12_foxo1_tag_count'] for gp in groups],
labels,
title='LCMV d12 Foxo1 tags in ATAC-seq regions by group',
ylabel='Foxo1 peak tag count', save_dir=save_path,
show_plot=False)
if True:
for i, gp in enumerate(groups):
yzer.piechart([sum(gp['naive_foxo1_tag_count'] >= min_thresh),
sum(gp['naive_foxo1_tag_count'] < min_thresh)],
['With Foxo1', 'Without Foxo1'],
title='Co-occurrence with Foxo1- ' + labels[i],
save_dir=save_path, show_plot=False)
yzer.piechart([sum(gp['lcmv_d12_foxo1_tag_count'] >= min_thresh),
sum(gp['lcmv_d12_foxo1_tag_count'] < min_thresh)],
['With Foxo1', 'Without Foxo1'],
title='Co-occurrence with LCMV d12 Foxo1- ' +
labels[i],
save_dir=save_path, show_plot=False)
yzer.histogram(gp['naive_foxo1_tag_count'].tolist(), bins=20,
title='Foxo1 peak tag count distribution- ' +
labels[i],
xlabel='Tag count in Foxo1 peak',
ylabel='Number of peaks',
save_dir=save_path, show_plot=False)
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',
'p2_tag_count_nonzero',
xlabel='CTCF Tag Count',
ylabel='Stat1 Tag Count',
log=True,
color='blue',
title='Tags in CTCF Peaks versus Overlapping Stat1 Peaks',
show_2x_range=False,
show_legend=False,
& (refseq_no_runoff['refseq'] == 't')
& (refseq_no_runoff['percent_covered'] < 1.5)]
if False:
yzer.histogram(mrna_with_runoff['percent_covered'])
yzer.histogram(mrna_no_runoff['percent_covered'])
relationships = ['is contained by', 'contains', 'overlaps with']
with_runoff_counts = [
sum(mrna_with_runoff['relationship'] == rel)
for rel in relationships
]
no_runoff_counts = [
sum(mrna_no_runoff['relationship'] == rel)
for rel in relationships
]
yzer.piechart(with_runoff_counts, labels=relationships)
yzer.piechart(no_runoff_counts, labels=relationships)
if True:
# Filter down to high-expression genes
def distance_to_reg_end(row):
if row['strand'] == 0:
# RefSeq annotated end - transcript end; pos if Refseq is longer
distance = row['transcription_end(2)'] - row[
'transcription_end']
elif row['strand'] == 1:
# transcript start - RefSeq annotated start; pos if Refseq is longer
distance = row['transcription_start'] - row[
'transcription_start(2)']
return distance
transrepressed = data[(data['kla_1_lfc_trans'] >= 1)
& (data['dex_over_kla_1_lfc_trans'] <= -.58)]
not_trans = data[(data['kla_1_lfc_trans'] < 1) |
(data['dex_over_kla_1_lfc_trans'] > -.58)]
up_in_kla = data[(data['kla_1_lfc_trans'] >= 1)
& (data['dex_over_kla_1_lfc_trans'] > -.58)]
supersets = (('All', data), ('Not near transrepressed genes', not_trans),
('Up in KLA', up_in_kla), ('Near transrepressed genes',
transrepressed))
# Plot trans versus not
if draw_pies:
yzer.piechart([len(d) for d in zip(*supersets[1:])[1]],
zip(*supersets[1:])[0],
title='Enhancer-like Subsets by state in KLA+Dex',
save_dir=img_dirpath,
show_plot=False)
tfs = [('PU.1', 'pu_1'), ('p65', 'p65'), ('GR', 'gr')]
contexts = [('DMSO', ''), ('Dex', 'dex'), ('KLA', 'kla'),
('KLA+Dex', 'kla_dex')]
for name, dataset in supersets:
total_for_set = len(dataset)
# Have GR
dataset = dataset[dataset['gr_dex_tag_count'] +
dataset['gr_kla_dex_tag_count'] > min_tags]
total_gr = len(dataset)
if draw_pies:
from glasslab.dataanalysis.graphing.seq_grapher import SeqGrapher
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, 'figure_4_pie_charts')
yzer.legend_location = 'lower left'
pie1 = '''Annotated by RefSeq and/or ncRNA.org 16,945
Unannotated 36,578'''
pie1 = [row.split(' ') for row in pie1.split('\n')]
pie1 = zip(*pie1)
yzer.piechart(map(lambda s: int(s.replace(',', '')), pie1[1]),
pie1[0],
title='Transcripts with Score >= 2',
save_dir=img_dirpath,
show_plot=True)
pie2 = '''Promoter-associated RNA 6,314
Antisense of RefSeq 5,604
Post-TTS, same-strand 6,940
Other RefSeq Proximal 3,119
Distal with H3K4me1 7,458
Distal within 2kbp of H3K4me1 1,639
Remainder 5,504'''
pie2 = [row.split(' ') for row in pie2.split('\n')]
pie2 = zip(*pie2)
yzer.legend_columns = 2
yzer.piechart(map(lambda s: int(s.replace(',', '')), pie2[1]),
pie2[0],
'''.format(total,
acetylated, (acetylated / total) * 100,
foxp3, (foxp3 / total) * 100,
both, (both / total) * 100,
k=k)
print summary
# Draw pie for each group, showing % with foxp3, % with ac, and % with both
relevant_cells = ', '.join([s.title() for s in k.split('_')])
counts = [acetylated - both, both, foxp3 - both, none]
grapher.piechart(
counts=counts,
labels=[
'Has H3K27Ac in Tregs', 'Has Both', 'Has FoxP3 in Tregs',
'Has Neither'
],
title='FoxP3 and H3K27Ac at Enhancers\nwith H3K4me2 in {}'.
format(relevant_cells),
small_legend=False,
colors=['#FFFB97', '#D5F0CB', '#ABE4FF', 'white'],
save_dir=graph_dirpath,
show_plot=False)
if True:
data['with_foxp3'] = data['treg'][
data['treg']['foxp3_tag_count'] >= min_score]
data['without_foxp3'] = data['treg'][
data['treg']['foxp3_tag_count'] < min_score]
for k in ('with_foxp3', ):
first_peak = 'treg'
subset = data[k]
for celltype in ('hi', 'lo'):
d7 = datasets['klrg{}_d7'.format(celltype)]
de_novo = d7[d7['d0_tag_count'] < min_thresh]
all_shared = d7[
'foxo1_ko_klrg{}_d7_tag_count'.format(celltype)] >= min_thresh
all_not_shared = d7[
'foxo1_ko_klrg{}_d7_tag_count'.format(celltype)] < min_thresh
shared = de_novo[
'foxo1_ko_klrg{}_d7_tag_count'.format(celltype)] >= min_thresh
not_shared = de_novo[
'foxo1_ko_klrg{}_d7_tag_count'.format(celltype)] < min_thresh
labels = ['Also in Foxo1 KO', 'Not in Foxo1 KO']
yzer.piechart([sum(all_shared), sum(all_not_shared)],
labels,
title='WT KLRG{} d7 Enhancers'.format(celltype),
save_dir=save_path, show_plot=False)
yzer.piechart([sum(shared), sum(not_shared)],
labels,
title='WT KLRG{} d7 De Novo Enhancers'.format(celltype),
save_dir=save_path, show_plot=False)
yzer.boxplot([d7[all_shared]['tag_count'].tolist(),
d7[all_not_shared]['tag_count'].tolist()],
labels,
title='ATAC-seq tags in WT KLRG{} d7 Enhancers'.format(
celltype),
ylabel='ATAC peak tag count', save_dir=save_path,
show_plot=False)
yzer.boxplot([de_novo[shared]['tag_count'].tolist(),
de_novo[not_shared]['tag_count'].tolist()],
data = data.merge(transcripts, how='left', on='nearest_refseq_transcript_id', suffixes=['','_trans'])
data = data.fillna(0)
transrepressed = data[(data['kla_1_lfc_trans'] >= 1) & (data['dex_over_kla_1_lfc_trans'] <= -.58)]
not_trans = data[(data['kla_1_lfc_trans'] < 1) | (data['dex_over_kla_1_lfc_trans'] > -.58)]
up_in_kla = data[(data['kla_1_lfc_trans'] >= 1) & (data['dex_over_kla_1_lfc_trans'] > -.58)]
supersets = (('All', data),
('Not near transrepressed genes', not_trans),
('Up in KLA', up_in_kla),
('Near transrepressed genes',transrepressed))
# Plot trans versus not
yzer.piechart([len(d) for d in zip(*supersets[1:])[1]], zip(*supersets[1:])[0],
title='Enhancer-like Subsets by state in KLA+Dex',
save_dir=img_dirpath, show_plot=False)
tfs = [('PU.1','pu_1'),('p65','p65'),('GR','gr')]
contexts = [('DMSO',''),('Dex','dex'),('KLA','kla'),('KLA+Dex','kla_dex')]
for name, dataset in supersets:
total_for_set = len(dataset)
for tf_name, tf in tfs:
# Get count for enhancer elements with this TF at all
cols = ['{0}_{1}tag_count'.format(tf, c and (c+'_') or '') for _, c in contexts]
with_tf = dataset[dataset.filter(items=cols).max(axis=1) > min_tags]
without_tf = dataset[dataset.filter(items=cols).max(axis=1) <= min_tags]
# Plot with TF versus not
yzer.piechart([ len(without_tf), len(with_tf)],
transrepressed = data[(data['kla_1_lfc_trans'] >= 1)
& (data['dex_over_kla_1_lfc_trans'] <= -.58)]
not_trans = data[(data['kla_1_lfc_trans'] < 1) |
(data['dex_over_kla_1_lfc_trans'] > -.58)]
up_in_kla = data[(data['kla_1_lfc_trans'] >= 1)
& (data['dex_over_kla_1_lfc_trans'] > -.58)]
supersets = (('All', data), ('Not near transrepressed genes', not_trans),
('Up in KLA', up_in_kla), ('Near transrepressed genes',
transrepressed))
# Plot trans versus not
yzer.piechart([len(d) for d in zip(*supersets[1:])[1]],
zip(*supersets[1:])[0],
title='Enhancer-like Subsets by state in KLA+Dex',
save_dir=img_dirpath,
show_plot=False)
for name, dataset in supersets:
total_for_set = len(dataset)
dataset = dataset[dataset['gr_kla_dex_tag_count'] > min_tags]
# Get count for enhancer elements with/out CpG
with_cpg = dataset[dataset['has_cpg_enh'] == 1]
without_cpg = dataset[dataset['has_cpg_enh'] == 0]
# Plot with TF versus not
yzer.piechart(
[len(without_cpg), len(with_cpg)],
['No CpG Island', 'Has CpG Island'],
title='Enhancer-like Subsets {0}\nby Overlap with CpG Island'.
# can be subsumed by a single H3K4me2 peak
atac_only = atac[(atac['naive_h3k4me2_tag_count'] < me2_thresh)]
atac_me2 = atac[(atac['naive_h3k4me2_tag_count'] >= me2_thresh)]
me2_only = me2[(me2['naive_atac_tag_count'] < atac_thresh)]
me2_atac = me2[(me2['naive_atac_tag_count'] >= atac_thresh)]
print('ATAC only: ', len(atac_only))
print('ATAC with H3K4me2: ', len(atac_me2))
print('H3K4me2 only: ', len(me2_only))
print('H3K4me2 with ATAC: ', len(me2_atac))
save_path = yzer.get_and_create_path(
dirpath, 'Figures', 'me2_atac_overlaps')
yzer.piechart([len(atac_only), len(atac_me2)],
['ATAC only', 'ATAC with H3K4me2'],
title='ATAC-seq region overlaps',
save_dir=save_path)
yzer.piechart([len(me2_only), len(me2_atac)],
['H3K4me2 only', 'H3K4me2 with ATAC'],
title='H3K4me2 overlaps',
save_dir=save_path)
yzer.boxplot([atac_only['tag_count'], atac_me2['tag_count']],
['ATAC only', 'ATAC with H3K4me2'],
title='ATAC-seq tag counts by H3K4me2 overlap',
xlabel='Group', ylabel='Peak tag count',
save_dir=save_path)
yzer.boxplot([me2_only['tag_count'], me2_atac['tag_count']],
['H3K4me2 only', 'H3K4me2 with ATAC'],
title='H3K4me2 tag counts by ATAC-seq overlap',
