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]]
#title = 'Tags in p65 peaks in KLA 1h + DMSO 2h: Distal'
title = 'Diff in tags in p65 peaks in KLA 1h + Dex 2h vs KLA 1h + DMSO 2h: RefSeq'
ax = grapher.boxplot(datasets,
['Down in Dex 2h','No change in Dex 2h','Up in Dex 2h',],
title=title,
xlabel='Condition',
ylabel='Total tags in all peaks overlapping transcript',
show_outliers=False, show_plot=False)
grapher.save_plot(grapher.get_filename(base_dirpath, 'boxplots', 'dex',
title.replace(' ','_')))
grapher.show_plot()
for sub in datasets: print sub.mean()
# Boxplots for gr_kla_dex peaks by lfc in Dex
if False:
#data = data[data['distal'] == 't']
#data = data[data['has_refseq'] == 1]
trans = data[(data['kla_1_lfc'] >= 1) & (data['dex_over_kla_1_lfc'] <= -.58)]
rest = data[(data['kla_1_lfc'] < 1) | (data['dex_over_kla_1_lfc'] > -.58)]
key = 'gr_dex_tag_count'
datasets = [rest[key],trans[key]]
datasets = [d['gr_kla_dex_tag_count'] - d[key] for d in [rest, trans]]
or ''),
add_noise=show_points,
show_points=show_points,
show_2x_range=(not show_points),
show_legend=(not show_points),
plot_regression=(not show_points),
show_count=(not show_points),
show_correlation=False,
show_plot=False,
ax=ax)
#yzer.xlim(ax, min(data[xcolname]), max(data[xcolname]))
#yzer.ylim(ax, min(data[ycolname]), max(data[ycolname]))
yzer.save_plot(
yzer.get_filename(
img_dirpath, '{0}_in_{1}_vs_KLA-Dex_by_{2}.png'.format(
main, basal_cond, compare)))
yzer.show_plot()
if False:
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(1)
data = data.groupby(['id', 'chr_name'], as_index=False).mean()
data['ratio_main'] = data['tag_count'] / data['tag_count_2']
data['ratio_compare'] = data['tag_count_3'] / data['tag_count_4']
raw_data = raw_data.split('\n')
dates = [
datetime.strptime(raw_data[x], '%Y_%m_%d')
for x in xrange(0, len(raw_data), 3)
]
set1 = numpy.array([
map(int, raw_data[x].split(',')) for x in xrange(1, len(raw_data), 3)
]).T
set2 = numpy.array([
map(int, raw_data[x].split(',')) for x in xrange(2, len(raw_data), 3)
]).T
grapher = SeqGrapher()
ax = grapher.timeseries(
dates, [set1, set2],
show_median=True,
colors=['blue', 'red'],
labels=['Control', 'TDB treated'],
title='Blood glucose values in NOD mice after TDB treatment',
xlabel='Date',
ylabel='Blood glucose (mg/dL, via Aviva AccuChek meter)',
show_plot=False,
show_legend=True)
dirpath = '/Users/karmel/Desktop/Projects/GlassLab/Notes_and_Reports/NOD_BALBc/TDB in vitro/'
grapher.save_plot(
os.path.join(
dirpath,
'blood_glucose_values_after_tdb_with_delta_06_04_2012.png'))
grapher.show_plot()
# KLA tag count
vals = [
d['kla_1_tag_count{0}'.format(suffix)] /
d['length{0}'.format(suffix)] for _, d in supersets
]
title = 'GroSeq tag counts in {0}: DMSO 2h + KLA 1h by gene category'.format(
subgroup)
ax = yzer.boxplot(vals,
labels,
title=title,
xlabel=xlabel,
ylabel='KLA 1h Tag Count per basepair',
show_outliers=False,
show_plot=False)
pyplot.setp(ax.get_xticklabels(), fontsize=10)
yzer.save_plot(yzer.get_filename(img_dirpath, title + '.png'))
# KLA LFC
vals = [d['kla_1_lfc{0}'.format(suffix)] for _, d in supersets]
title = 'GroSeq Log Fold Change in {0} in DMSO 2h + KLA 1h by gene category'.format(
subgroup)
ax = yzer.boxplot(vals,
labels,
title=title,
xlabel=xlabel,
ylabel='log2(KLA GRO-seq/DMSO GRO-seq)',
show_outliers=False,
show_plot=False)
pyplot.setp(ax.get_xticklabels(), fontsize=10)
yzer.save_plot(yzer.get_filename(img_dirpath, title + '.png'))
if True:
# non-d
ax = grapher.scatterplot(refseq_up_nond,
'balb_notx_0h_tag_count',
'nod_notx_0h_tag_count_norm',
log=True,
color='blue',
master_dataset=refseq,
title='BALBc vs. NOD BMDC Refseq Transcripts',
show_2x_range=True,
show_legend=False,
show_count=True,
show_correlation=True,
show_plot=False)
grapher.save_plot(
os.path.join(dirpath,
'nondiabetic_balbc_v_nod_up_scatterplot.png'))
grapher.show_plot()
if False:
# diabetic
ax = grapher.scatterplot(
refseq,
'diabetic_balb_notx_0h_tag_count',
'diabetic_nod_notx_0h_tag_count_norm',
log=True,
color='blue',
master_dataset=refseq,
title='Diabetic BALBc vs. NOD BMDC Refseq Transcripts',
show_2x_range=True,
show_legend=False,
ax = grapher.scatterplot(
dataset,
'dmso_tag_count',
'kla_tag_count_norm',
log=True,
color='blue',
title='DMSO vs. KLA tag counts: All runs, {0}'.format(label),
xlabel='DMSO 2h tags',
ylabel='KLA 1h + DMSO 2h tags',
show_2x_range=True,
show_legend=True,
show_count=True,
show_correlation=True,
show_plot=False)
grapher.save_plot(
grapher.get_filename(
scatter_dirpath,
'dmso_vs_kla_all_runs_{0}.png'.format(slug_label)))
grapher.show_plot()
for x in xrange(1, 5):
# By group
ax = grapher.scatterplot(
dataset,
'dmso_{0}_tag_count'.format(x),
'kla_{0}_tag_count_norm'.format(x),
log=True,
color='blue',
title='DMSO vs. KLA tag counts: Group {0} runs, {1}'.
format(x, label),
xlabel='DMSO 2h tags',
ylabel='KLA 1h + DMSO 2h tags',
data['tag_count_2']) / data['tag_count']
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'])
title = 'Difference in {0} peak tag counts by {1}'.format(
main, compare)
names = [
s.format(compare) for s in [
'No {0} in KLA+Dex', 'Loses {0} in KLA+Dex',
'Gains/maintains {0} in KLA+Dex'
]
]
ax = yzer.boxplot(
[
data[cond_1][colname], data[cond_2][colname],
data[cond_3][colname]
],
names,
title=title,
xlabel='Condition',
ylabel='{0} KLA+Dex tags in peak - {0} {1} tags in peak'.
format(main, basal_cond),
show_outliers=False,
show_plot=False)
yzer.save_plot(
yzer.get_filename(img_dirpath, title.replace(' ', '_')))
yzer.show_plot()
data['{0}_kla_to_kla_dex_ratio'.format(peak_type)] = data['{0}_kla_tag_count'.format(peak_type)]\
/data['{0}_kla_dex_nearby_tag_count'.format(peak_type)]
print sum(kla_gt)
print sum(kla_dex_gt)
data[kla_gt].to_csv(yzer.get_filename(img_dirpath, 'enhancer_like_lose_{0}_{1}x_change_dsg_only.txt'.format(
peak_type, ratio)),
sep='\t', header=True, index=False)
data[kla_dex_gt].to_csv(yzer.get_filename(img_dirpath, 'enhancer_like_gain_{0}_{1}x_change_dsg_only.txt'.format(
peak_type, ratio)),
sep='\t', header=True, index=False)
title = 'LFC in KLA + Dex over KLA by change in {0}:\nEnhancer-Like, Has GR in KLA+Dex (DSG only)'.format(peak_type)
if pu_1:
title = 'PU.1 in KLA + Dex over KLA by change in {0}:\nEnhancer-Like, has GR in KLA+Dex, has PU.1'.format(peak_type)
names = [s.format(peak_type) for s in ['No {0}','Loses {0} in KLA+Dex','No change in {0}', 'Gains {0} in KLA+Dex']]
ax = yzer.boxplot([data[none][colname], data[kla_gt][colname], data[nc][colname], data[kla_dex_gt][colname]],
names,
title=title,
xlabel='{0} Status'.format(peak_type),
ylabel=(pu_1 and 'log2(KLA+Dex PU.1/KLA PU.1)')\
or 'log2(KLA+Dex GRO-seq/KLA GRO-seq)',
show_outliers=False, show_plot=False)
if pu_1:
yzer.save_plot(yzer.get_filename(img_dirpath, 'dex_over_kla_pu_1_both_by_{0}_sums_{1}x_change.png'.format(peak_type, ratio)))
else:
yzer.save_plot(yzer.get_filename(img_dirpath, 'dex_over_kla_1_lfc_by_{0}_sums_{1}x_change_dsg_only.png'.format(peak_type, ratio)))
yzer.show_plot()
label='Predicted Coding',
add_noise=False,
show_2x_range=False,
plot_regression=False,
show_count=False,
show_correlation=False,
show_legend=False,
show_plot=False)
ax = grapher.scatterplot(
data_noncoding,
'score_orf',
'score',
log=False,
color='green',
title='CPC-derived Coding Potential Predictions for RefSeq mRNA',
xlabel='ORF score',
ylabel='Coding score',
label='Predicted Non-coding',
add_noise=False,
show_2x_range=False,
plot_regression=False,
show_count=False,
show_correlation=False,
show_legend=True,
show_plot=False,
ax=ax)
pyplot.plot([0, max(data['score_orf'])], [0, 0], '-', color='black')
grapher.save_plot(
os.path.join(dirpath, 'refseq_coding_potential_predictions_zoom.png'))
grapher.show_plot()
regrouped = regrouped[regrouped['kla_1_lfc'] >= 1]
regrouped = regrouped[(regrouped['refseq'] == 'f')
& (regrouped['length'] < 6000)]
regrouped = regrouped.groupby(['id', 'chr_name'],
as_index=False).mean()
transcripts = transcripts[(transcripts['refseq'] == 'f')
& (transcripts['length'] < 6000)]
transcripts = transcripts[transcripts['kla_1_lfc'] >= 1]
all_trans = transcripts['dex_over_kla_1_lfc']
with_p65 = transcripts[
transcripts['p65_kla_tag_count'] > 0]['dex_over_kla_1_lfc']
with_pair = regrouped['dex_over_kla_1_lfc']
title = 'Transcript log fold change at redistribution pairs:\nEnhancer-like, up in KLA alone'
names = [
'All transcripts', 'Transcripts with p65',
'Transcripts with\na redistribution pair'
]
ax = yzer.boxplot([all_trans, with_p65, with_pair],
names,
title=title,
xlabel='Transcript subset',
ylabel='log2(KLA+Dex / KLA)',
show_outliers=False,
show_plot=False)
yzer.save_plot(
yzer.get_filename(
dirpath, 'redistribution', 'boxplots',
'dex_over_kla_lfc_boxplot_1_mean_enhancer_like_up_in_kla.png'))
yzer.show_plot()
groups = [data[none], data[kla_gt], data[nc], data[kla_dex_gt]]
# We want to randomly sample to get equi-sized groups
desired = len(nearby)
for i, g in enumerate(groups):
rows = random.sample(g.index, desired)
groups[i] = g.ix[rows]
to_plot = [g[colname] for g in (groups + [nearby])]
title = 'LFC in KLA + Dex over KLA by change in p65:' \
+ '\nRefSeq, randomly sampled to {0} transcripts'.format(desired)
if pausing:
title = 'Pausing Ratio Ratio by change in p65:' \
+ '\nRefSeq, randomly sampled to {0} transcripts'.format(desired)
ax = yzer.boxplot(to_plot,
names,
title=title,
xlabel='Transcript Status',
ylabel=(pausing and 'PausingRatio(KLA+Dex)/PausingRatio(KLA)')\
or 'log2(KLA+Dex GRO-seq/KLA GRO-seq)',
show_outliers=False, show_plot=False)
yzer.save_plot(
yzer.get_filename(
img_dirpath,
'{2}_with_nearby_unique_{0}x_{3}_sampled_{1}.png'.format(
ratio, random.randint(0, 9999), colname, change_type)))
yzer.show_plot()
#'srf_targets': ['Srf','Cnn2','Lima1','Coro1a','Vcl','Acta2','Actb','Dhcr24','Actg2','Actc1','Lcp1','Jup','Tpm4','Tnni2','Zyx','Tubb3','Pfn1','Gas7','Arpc4','Pstpip1','Bsn','Flna','Actn1'],
#'inflammatory_genes': ['Cxcl1','Cxcl2','Il6','Ptgs2','Tnfsf9','Vegfa','Tnf', 'Siglec1','Mmp9', 'Il10','Il1b','Cxcl10','Tlr4','Il12b',]
}
'''
'clec4e_tlr2': ['Clec4e','Tlr2',],
,
'''
for i, genes in gene_groups.items():
for gene in genes[:]:
if not gene in refseq['gene_names'].values:
print gene
genes.remove(gene)
indices = [refseq[refseq['gene_names'] == gene].index[0] for gene in genes]
for txt in ('notx','kla'):
sorted_by_count = refseq.fillna(0).sort_index(axis=0, by='balb_{0}_1h_reads_per_base'.format(txt)).index.copy()
sort_indexes = list(enumerate(sorted_by_count))
sort_indexes.sort(key=lambda x: x[1])
refseq['rank'] = zip(*sort_indexes)[0]
yzer.bargraph_for_transcripts(refseq, indices, ['balb_nod_{0}_1h_fc'.format(txt)],
bar_names=genes,
title='NOD vs. BALBc {0} 1h GRO-seq'.format(txt=='kla' and txt.upper() or txt.capitalize()),
ylabel='Fold Change in NOD vs. BALBc',
rank_label='Rank of read per base pair value\nin BALBc {0} 1h, ascending'.format(
txt=='kla' and txt.upper() or txt.capitalize()),
show_plot=False)
yzer.save_plot(yzer.get_filename(img_dirpath, 'balbc_nod_{0}_{1}_fold_change_bargraph.png'.format(txt,i)))
#yzer.show_plot()
k27_tf['rpkm'], ctcf_me2_tf['rpkm'], k27_me2_tf['rpkm'], tf['rpkm'],
me2_only['rpkm'], ctcf_only['rpkm'],
k27_only['rpkm'], ctcf_me2['rpkm'], k27_me2['rpkm'], nothing['rpkm']],
bar_names=['All Potential\nEnhancers',
'me2 + TF', 'CTCF + TF',
'K27 + TF',
'CTCF + me2\n+ TF', 'K27 + me2\n+ TF', 'TF',
'me2 only', 'CTCF only',
'K27 only',
'CTCF + me2', 'K27 + me2',
'No peaks',],
title='GRO-seq RPKM at non-genic H3K4me2 regions',
xlabel='', ylabel='Tags per 1000bp in GRO-seq transcript overlapping H3K4me2 peak',
show_outliers=False, show_plot=False)
yzer.save_plot(os.path.join(dirpath, 'groseq_rpkm_at_h3k4me2_peaks.png'))
yzer.show_plot()
'''
-- With H3K27me3
select distinct on (e.id) e.*, e.id as me2, reg.id as refseq, p1.id as pu_1, p2.id as cebpa,
p3.id as p65, p5.id as k27, p6.id as ctcf, t.id as trans, t.tag_count
from thiomac_chipseq_2011.peak_wt_notx_1h_h3k4me2_05_11 e
left outer join genome_reference_mm9.sequence_transcription_region reg
on e.chromosome_id = reg.chromosome_id
and e.start_end && reg.start_end
left outer join thiomac_chipseq_2011.peak_wt_notx_1h_pu_1_05_11 p1
on e.chromosome_id = p1.chromosome_id
and e.start_end && p1.start_end
xlabel='C57Bl6 PU.1 tag counts',
ylabel='NOD PU.1 tag counts',
title=
'C57Bl6 vs. NOD PU.1 peaks\nwhere C57Bl6 has a PU.1 motif and BALBc does not',
label='NOD SNP == BALBc SNP',
add_noise=False,
show_2x_range=False,
show_legend=True,
text_color=True,
show_count=True,
show_correlation=True,
show_plot=False,
ax=ax)
#ax.set_ylim(4,128)
grapher.save_plot(
os.path.join(dirpath,
'bl6_vs_nod_pu_1_peak_tag_counts_bl6_gt_balb.png'))
grapher.show_plot()
if True:
# Boxplots: avg PU.1 in Bl6 for whole set; avg PU.1 in BALB for whole set;
# avg PU.1 for NOD in whole set; avg PU.1 in NOD set with Bl6; avg PU.1 in NOD set with BALB
ax = grapher.boxplot(
[
data['wt_pu_1_tag_count'], data['balb_pu_1_tag_count_norm'],
data['nod_pu_1_tag_count_norm'],
nod_with_bl6['nod_pu_1_tag_count_norm'],
nod_with_balb['nod_pu_1_tag_count_norm']
],
bar_names=[
xlabel='BALBc tag counts',
ylabel='NOD tag counts',
title=
'BALBc vs. NOD GRO-seq tag counts\nwhere C57Bl6 has a PU.1 motif and BALBc does not',
label='NOD SNP == BALBc SNP',
add_noise=False,
show_2x_range=False,
show_legend=True,
text_color=True,
show_count=True,
show_correlation=True,
show_plot=False,
ax=ax)
#ax.set_ylim(4,128)
grapher.save_plot(
os.path.join(
dirpath,
'balbc_vs_nod_pu_1_peak_tag_counts_bl6_gt_balb_unique.png'))
grapher.show_plot()
if True:
# Boxplots
ax = grapher.boxplot(
[
data['wt_tag_count'], data['balb_tag_count_norm'],
data['nod_tag_count_norm'], nod_with_bl6['nod_tag_count_norm'],
nod_with_balb['nod_tag_count_norm']
],
bar_names=[
'C57Bl6 Tags',
'BALBc Tags',
