def line(parameters):
parser = parsers.line_parser()
args = parser.parse_args(parameters)
regions = [fanc.load(file_name) for file_name in args.regions]
attribute = args.attribute
bin_size = args.bin_size
labels = args.labels
colors = args.colors
fill = args.fill
line_style = args.line_style
ylim = args.ylim
alpha = args.alpha
legend_location = args.legend_location
if labels is not None and len(labels) != len(regions):
parser.error("Number of labels ({}) must be the same as number "
"of datasets ({})".format(len(labels), len(regions)))
p = kplt.LinePlot(regions,
bin_size=bin_size,
fill=fill,
attribute=attribute,
labels=labels,
style=line_style,
ylim=ylim,
colors=colors,
legend_location=legend_location,
plot_kwargs={'alpha': alpha})
return p, args
def test_lineplot_dict_input(self, data_source, bin_size):
data = {k: getattr(self, d) for k, d in data_source.items()}
lplot = kplot.LinePlot(data, bin_size=bin_size)
gfig = kplot.GenomicFigure([lplot])
fig, axes = gfig.plot("chr11:77497000-77500000")
assert all(len(l.get_ydata()) > 5 for a in axes for l in a.get_lines())
assert all(l.get_label() == n_p
for l, n_p in zip(axes[0].get_lines(), data.keys()))
def test_lineplot(self, data_source, bin_size):
data = [getattr(self, d) for d in data_source]
if len(data) == 1:
data = data[0]
lplot = kplot.LinePlot(data, bin_size=bin_size)
gfig = kplot.GenomicFigure([lplot])
fig, axes = gfig.plot("chr11:77497000-77500000")
assert all(len(l.get_ydata()) > 5 for l in axes[0].get_lines())
# polii_chip_early = os.path.join("external_data", "blythe_2015", "aligned",
# "PolII-pSer5_NC14-early_sorted_filtered_merged_canonical_chrs.bw")
# polii_chip_mid = os.path.join("external_data", "blythe_2015", "aligned",
# "PolII-pSer5_NC14-middle_sorted_filtered_merged_canonical_chrs.bw")
polii_chip_late = os.path.join("external_data", "blythe_2015", "aligned",
"PolII-pSer5_NC14-late_sorted_filtered_merged_canonical_chrs.bw")
# polii_early_plot = fancplot.LinePlot(polii_chip_early, fill=True, plot_kwargs={'color': "black"},
# draw_minor_ticks=False, aspect=0.05,
# ylim=polii_ylim, n_yticks=2)
# polii_mid_plot = fancplot.LinePlot(polii_chip_mid, fill=True, plot_kwargs={'color': "black"},
# draw_minor_ticks=False, aspect=0.05,
# ylim=polii_ylim, n_yticks=2)
polii_late_plot = fancplot.LinePlot(polii_chip_late, fill=False,
plot_kwargs={'color': "black"},
draw_minor_ticks=False, aspect=0.05,
ylim=polii_ylim, n_yticks=2)
rnaseq_plot_gd7 = fancplot.LinePlot(rnaseq_dict['gd7'], fill=False,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False, aspect=0.05,
ylim=rnaseq_ylim, n_yticks=2)
h3k27ac_plot_gd7 = fancplot.LinePlot(h3k27ac_dict['gd7'], fill=False,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False, aspect=0.05,
ylim=h3k27ac_ylim, n_yticks=2)
h3k27me3_plot_gd7 = fancplot.LinePlot(h3k27me3_dict['gd7'], fill=False,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False, aspect=0.05,
ylim=h3k27me3_ylim, n_yticks=2)
plt.show()
# end snippet ab fancplot-correlation
fig.savefig('../docsrc/api/analyse/images/ab_1mb_correlation.png')
# start snippet ab ev
ev = ab.eigenvector()
# end snippet ab ev
# start snippet ab gc-ev
gc_ev = ab.eigenvector(genome='hg19_chr18_19.fa', force=True)
# end snippet ab gc-ev
# start snippet ab plot-ev
fig, ax = plt.subplots(figsize=(5, 2))
lp = fancplot.LinePlot(ab, colors=['darkturquoise'])
lp.plot('chr18')
plt.show()
# end snippet ab plot-ev
fig.savefig('../docsrc/api/analyse/images/ab_1mb_ev.png')
# start snippet ab profile
profile, cutoffs = ab.enrichment_profile(hic_1mb, genome='hg19_chr18_19.fa')
# end snippet ab profile
# start snippet ab saddle
fig, axes = fancplot.saddle_plot(profile, cutoffs)
# end snippet ab saddle
fig.savefig('../docsrc/api/analyse/images/ab_1mb_saddle.png')
def plot_regions(regions):
h = fanc.load(os.path.join("data", "hic", "merged", "3-4h", "hic",
"3-4h_2kb.hic"),
mode="r")
h_plot = fancplot.HicPlot(h,
vmin=1e-03,
vmax=1e-01,
norm="log",
draw_minor_ticks=False)
genes = "external_data/flybase/dmel-all-r6.30.gtf.gz"
genes_plot = fancplot.GenePlot(genes,
squash=True,
group_by="gene_symbol",
aspect=0.15,
label_field="gene_symbol",
show_labels=False,
draw_minor_ticks=False)
rnaseq_dict = {
name:
os.path.join("external_data", "koenecke_2016_2017", "rnaseq_aligned",
name + "_sorted_filtered_merged_canonical_chrs_rnaseq.bw")
for name in ["gd7", "tlrm910", "tl10b"]
}
h3k27ac_dict = {
name: os.path.join(
"external_data", "koenecke_2016_2017", "chipseq_aligned",
"H3K27ac_" + name + "_sorted_filtered_merged_canonical_chrs.bw")
for name in ["gd7", "tl10b"]
}
h3k27ac_dict["Tollrm910"] = os.path.join(
"external_data", "extra_chip-seq", "chipseq_aligned",
"H3K27ac_Tollrm910_sorted_filtered_merged_canonical_chrs.bw")
rnaseq_ylim = fancplot.helpers.LimitGroup()
h3k27ac_ylim = fancplot.helpers.LimitGroup()
polii_ylim = fancplot.helpers.LimitGroup()
# polii_chip_early = os.path.join("external_data", "blythe_2015", "aligned",
# "PolII-pSer5_NC14-early_sorted_filtered_merged_canonical_chrs.bw")
# polii_chip_mid = os.path.join("external_data", "blythe_2015", "aligned",
# "PolII-pSer5_NC14-middle_sorted_filtered_merged_canonical_chrs.bw")
polii_chip_late = os.path.join(
"external_data", "blythe_2015", "aligned",
"PolII-pSer5_NC14-late_sorted_filtered_merged_canonical_chrs.bw")
# polii_early_plot = fancplot.LinePlot(polii_chip_early, fill=True, plot_kwargs={'color': "black"},
# draw_minor_ticks=False, aspect=0.05,
# ylim=polii_ylim, n_yticks=2)
# polii_mid_plot = fancplot.LinePlot(polii_chip_mid, fill=True, plot_kwargs={'color': "black"},
# draw_minor_ticks=False, aspect=0.05,
# ylim=polii_ylim, n_yticks=2)
polii_late_plot = fancplot.LinePlot(polii_chip_late,
fill=True,
plot_kwargs={'color': "black"},
draw_minor_ticks=False,
aspect=0.05,
ylim=polii_ylim,
n_yticks=2)
rnaseq_plot_gd7 = fancplot.LinePlot(rnaseq_dict['gd7'],
fill=True,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False,
aspect=0.05,
n_yticks=2)
h3k27ac_plot_gd7 = fancplot.LinePlot(h3k27ac_dict['gd7'],
fill=True,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
rnaseq_plot_Tollrm910 = fancplot.LinePlot(rnaseq_dict['tlrm910'],
fill=True,
plot_kwargs={'color': "#dc267f"},
draw_minor_ticks=False,
aspect=0.05,
n_yticks=2)
h3k27ac_plot_Tollrm910 = fancplot.LinePlot(
h3k27ac_dict['Tollrm910'],
fill=True,
plot_kwargs={'color': "#dc267f"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
rnaseq_plot_toll10b = fancplot.LinePlot(rnaseq_dict['tl10b'],
fill=True,
plot_kwargs={'color': "#ffb000"},
draw_minor_ticks=False,
aspect=0.05,
n_yticks=2)
h3k27ac_plot_toll10b = fancplot.LinePlot(h3k27ac_dict['tl10b'],
fill=True,
plot_kwargs={'color': "#ffb000"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
gd7_enh = "data/supplementary_tables/gd7_candidate_enhancers.bed"
gd7_enh_plot = fancplot.GenomicFeaturePlot(gd7_enh,
aspect=0.02,
color="#648fff",
draw_minor_ticks=False)
Tollrm910_enh = "data/supplementary_tables/Tollrm910_candidate_enhancers.bed"
Tollrm910_enh_plot = fancplot.GenomicFeaturePlot(Tollrm910_enh,
aspect=0.02,
color="#dc267f",
draw_minor_ticks=False)
toll10b_enh = "data/supplementary_tables/Toll10B_candidate_enhancers.bed"
toll10b_enh_plot = fancplot.GenomicFeaturePlot(toll10b_enh,
aspect=0.02,
color="#ffb000",
draw_minor_ticks=False)
plots = [
h_plot,
# ins_plot,
# boundaries_plot,
genes_plot,
# hk_plot,
# polii_early_plot, polii_mid_plot,
polii_late_plot,
rnaseq_plot_gd7,
rnaseq_plot_Tollrm910,
rnaseq_plot_toll10b,
h3k27ac_plot_gd7,
h3k27ac_plot_Tollrm910,
h3k27ac_plot_toll10b,
gd7_enh_plot,
Tollrm910_enh_plot,
toll10b_enh_plot
]
with PdfPages(output_file) as pdf:
with fancplot.GenomicFigure(plots, ticks_last=True) as gfig:
for name, region, rnaseq_ylim in regions:
logging.info(region)
fig, axes = gfig.plot(region)
axes[3].set_ylim([0, rnaseq_ylim])
axes[4].set_ylim([0, rnaseq_ylim])
axes[5].set_ylim([0, rnaseq_ylim])
pdf.savefig()
def compare(self, weight1, weight2):
if weight1 < weight2:
return -1
if weight1 > weight2:
return 1
return 0
# end snippet comparisons custom
# start snippet regions compare
diff_ins_esc_cn_100kb = fanc.DifferenceRegions.from_regions(ins_esc_100kb, ins_cn_100kb)
# end snippet regions compare
# start snippet regions plot
p_orig = fancplot.LinePlot([ins_esc_100kb, ins_cn_100kb], ylim=(-1, 1),
colors=['darkturquoise', 'orange'],
style='mid', fill=False)
p_diff = fancplot.LinePlot(diff_ins_esc_cn_100kb, ylim=(-1., 1.),
colors=['aquamarine'],
style='mid')
gf = fancplot.GenomicFigure([p_orig, p_diff], ticks_last=True)
fig, axes = gf.plot("chr1:167.9mb-168.7mb")
axes[0].set_ylabel("Insulation\nscore")
axes[1].set_ylabel("Insulation\ndifference")
# end snippet regions plot
fig.savefig("../docsrc/api/analyse/images/comparisons_regions.png")
plt.close(fig)
# start snippet scores compare
#fanc_base = '..'
fanc_base = '/Users/kkruse/dev/fanc'
# start snippet fancplot load bed
insulation_scores_1mb = fanc.load(
"architecture/domains/fanc_example_100kb.insulation_1mb.bed")
insulation_scores_2mb = fanc.load(
"architecture/domains/fanc_example_100kb.insulation_2mb.bed")
boundaries_1mb = fanc.load(
"architecture/domains/fanc_example_100kb.insulation_boundaries_1mb.bed")
boundaries_2mb = fanc.load(
"architecture/domains/fanc_example_100kb.insulation_boundaries_2mb.bed")
# end snippet fancplot load bed
# start snippet fancplot line fill
hp = fancplot.LinePlot(insulation_scores_1mb)
hp.plot('chr18:6mb-10mb')
hp.show()
# end snippet fancplot line fill
hp.save(os.path.join(fanc_base, 'docsrc/api/plot/images/plot_line.png'))
# start snippet fancplot line nofill
hp = fancplot.LinePlot(insulation_scores_1mb, fill=False)
hp.plot('chr18:6mb-10mb')
hp.show()
# end snippet fancplot line nofill
hp.save(os.path.join(fanc_base, 'docsrc/api/plot/images/plot_line_nofill.png'))
# start snippet fancplot line mid
def plot_region(name, region):
output_file = os.path.join("figures", "figure_4_panels", name + ".pdf")
logging.info("Working on %s", name)
logging.info("Will write output to %s", output_file)
gd7_nc14_hic = fanc.load(os.path.join("data", "hic", "merged", "gd7-nc14",
"hic", "gd7-nc14_5kb.hic"),
mode="r")
gd7_nc14_hic_plot = fancplot.HicPlot(gd7_nc14_hic,
norm="log",
vmin=1e-03,
vmax=1e-01,
draw_minor_ticks=False,
title="gd7",
max_dist='250kb')
gd7_nc14_diff = fanc.load(os.path.join(
"data", "hic", "merged", "gd7-nc14", "hic",
"diff_control-nc14_gd7-nc14_5kb.hic"),
mode="r")
gd7_nc14_diff_plot = fancplot.HicPlot(gd7_nc14_diff,
norm="lin",
colormap='bwr_r',
vmin=-0.01,
vmax=0.01,
draw_minor_ticks=False,
max_dist='250kb')
Tollrm910_nc14_hic = fanc.load(os.path.join("data", "hic", "merged",
"Tollrm910-nc14", "hic",
"Tollrm910-nc14_5kb.hic"),
mode="r")
Tollrm910_nc14_hic_plot = fancplot.HicPlot(Tollrm910_nc14_hic,
norm="log",
vmin=1e-03,
vmax=1e-01,
draw_minor_ticks=False,
title="Tollrm910",
max_dist='250kb')
Tollrm910_nc14_diff = fanc.load(os.path.join(
"data", "hic", "merged", "Tollrm910-nc14", "hic",
"diff_control-nc14_Tollrm910-nc14_5kb.hic"),
mode="r")
Tollrm910_nc14_diff_plot = fancplot.HicPlot(Tollrm910_nc14_diff,
norm="lin",
colormap='bwr_r',
vmin=-0.01,
vmax=0.01,
draw_minor_ticks=False,
max_dist='250kb')
Toll10B_nc14_hic = fanc.load(os.path.join("data", "hic", "merged",
"Toll10B-nc14", "hic",
"Toll10B-nc14_5kb.hic"),
mode="r")
Toll10B_nc14_hic_plot = fancplot.HicPlot(Toll10B_nc14_hic,
norm="log",
vmin=1e-03,
vmax=1e-01,
draw_minor_ticks=False,
title="Toll10B",
max_dist='250kb')
Toll10B_nc14_diff = fanc.load(os.path.join(
"data", "hic", "merged", "Toll10B-nc14", "hic",
"diff_control-nc14_Toll10B-nc14_5kb.hic"),
mode="r")
Toll10B_nc14_diff_plot = fancplot.HicPlot(Toll10B_nc14_diff,
norm="lin",
colormap='bwr_r',
vmin=-0.01,
vmax=0.01,
draw_minor_ticks=False,
max_dist='250kb')
genes = "external_data/flybase/dmel-all-r6.30.gtf.gz"
genes_plot = fancplot.GenePlot(genes,
squash=True,
group_by="gene_symbol",
aspect=0.15,
label_field="gene_symbol",
show_labels=False,
draw_minor_ticks=False)
rnaseq_dict = {
name:
os.path.join("external_data", "koenecke_2016_2017", "rnaseq_aligned",
name + "_sorted_filtered_merged_canonical_chrs_rnaseq.bw")
for name in ["gd7", "tlrm910", "tl10b"]
}
h3k27ac_dict = {
name: os.path.join(
"external_data", "koenecke_2016_2017", "chipseq_aligned",
"H3K27ac_" + name + "_sorted_filtered_merged_canonical_chrs.bw")
for name in ["gd7", "tl10b"]
}
h3k27ac_dict["Tollrm910"] = os.path.join(
"external_data", "extra_chip-seq", "chipseq_aligned",
"H3K27ac_Tollrm910_sorted_filtered_merged_canonical_chrs.bw")
h3k27me3_dict = {
name: os.path.join(
"external_data", "koenecke_2016_2017", "chipseq_aligned",
"H3K27me3_" + name + "_sorted_filtered_merged_canonical_chrs.bw")
for name in ["gd7", "tl10b"]
}
h3k27me3_dict["Tollrm910"] = os.path.join(
"external_data", "extra_chip-seq", "chipseq_aligned",
"H3K27me3_Tollrm910_sorted_filtered_merged_canonical_chrs.bw")
# ins_dict = {name: os.path.join("data", "boundaries", name + "_2kb_8.bw")
# for name in ["gd7-nc14", "Tollrm910-nc14", "Toll10B-nc14", "3-4h"]}
rnaseq_ylim = fancplot.helpers.LimitGroup()
rnaseq_ylim = [0, 10]
h3k27ac_ylim = fancplot.helpers.LimitGroup()
h3k27me3_ylim = fancplot.helpers.LimitGroup()
rnaseq_plot_gd7 = fancplot.LinePlot(rnaseq_dict['gd7'],
fill=False,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False,
aspect=0.05,
ylim=rnaseq_ylim,
n_yticks=2)
h3k27ac_plot_gd7 = fancplot.LinePlot(h3k27ac_dict['gd7'],
fill=False,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
h3k27me3_plot_gd7 = fancplot.LinePlot(h3k27me3_dict['gd7'],
fill=False,
plot_kwargs={'color': "#648fff"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27me3_ylim,
n_yticks=2)
rnaseq_plot_Tollrm910 = fancplot.LinePlot(rnaseq_dict['tlrm910'],
fill=False,
plot_kwargs={'color': "#dc267f"},
draw_minor_ticks=False,
aspect=0.05,
ylim=rnaseq_ylim,
n_yticks=2)
h3k27ac_plot_Tollrm910 = fancplot.LinePlot(
h3k27ac_dict['Tollrm910'],
fill=False,
plot_kwargs={'color': "#dc267f"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
h3k27me3_plot_Tollrm910 = fancplot.LinePlot(
h3k27me3_dict['Tollrm910'],
fill=False,
plot_kwargs={'color': "#dc267f"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27me3_ylim,
n_yticks=2)
rnaseq_plot_toll10b = fancplot.LinePlot(rnaseq_dict['tl10b'],
fill=False,
plot_kwargs={'color': "#ffb000"},
draw_minor_ticks=False,
aspect=0.05,
ylim=rnaseq_ylim,
n_yticks=2)
h3k27ac_plot_toll10b = fancplot.LinePlot(h3k27ac_dict['tl10b'],
fill=False,
plot_kwargs={'color': "#ffb000"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27ac_ylim,
n_yticks=2)
h3k27me3_plot_toll10b = fancplot.LinePlot(h3k27me3_dict['tl10b'],
fill=False,
plot_kwargs={'color': "#ffb000"},
draw_minor_ticks=False,
aspect=0.05,
ylim=h3k27me3_ylim,
n_yticks=2)
gd7_enh = "data/supplementary_tables/gd7_candidate_enhancers.bed"
gd7_enh_plot = fancplot.GenomicFeaturePlot(gd7_enh,
aspect=0.02,
color="#648fff",
draw_minor_ticks=False)
Tollrm910_enh = "data/supplementary_tables/Tollrm910_candidate_enhancers.bed"
Tollrm910_enh_plot = fancplot.GenomicFeaturePlot(Tollrm910_enh,
aspect=0.02,
color="#dc267f",
draw_minor_ticks=False)
toll10b_enh = "data/supplementary_tables/Toll10B_candidate_enhancers.bed"
toll10b_enh_plot = fancplot.GenomicFeaturePlot(toll10b_enh,
aspect=0.02,
color="#ffb000",
draw_minor_ticks=False)
plots = [
gd7_nc14_hic_plot, gd7_nc14_diff_plot, rnaseq_plot_gd7,
h3k27ac_plot_gd7, gd7_enh_plot, h3k27me3_plot_gd7,
Tollrm910_nc14_hic_plot, Tollrm910_nc14_diff_plot,
rnaseq_plot_Tollrm910, h3k27ac_plot_Tollrm910, Tollrm910_enh_plot,
h3k27me3_plot_Tollrm910, Toll10B_nc14_hic_plot, Toll10B_nc14_diff_plot,
rnaseq_plot_toll10b, h3k27ac_plot_toll10b, toll10b_enh_plot,
h3k27me3_plot_toll10b, genes_plot
]
with fancplot.GenomicFigure(plots, ticks_last=True) as gfig:
fig, axes = gfig.plot(region)
fig.savefig(output_file)
# start snippet insulation multiplot
p = fancplot.GenomicVectorArrayPlot(insulation,
colormap='RdBu_r',
vmin=-1,
vmax=1,
genomic_format=True)
p.plot('chr18:18mb-28mb')
# end snippet insulation multiplot
fig.savefig("../docsrc/api/analyse/images/domains_multi.png")
plt.close(fig)
fig, ax = plt.subplots()
# start snippet insulation singleplot
p = fancplot.LinePlot(insulation,
ylim=(-1, 1),
colors=['darkturquoise'],
style="mid",
attribute="insulation_1000000")
p.plot('chr18:18mb-28mb')
# end snippet insulation singleplot
fig.savefig("../docsrc/api/analyse/images/domains_single.png")
plt.close(fig)
# start snippet boundaries run
boundaries = fanc.Boundaries.from_insulation_score(insulation,
window_size=1000000)
# end snippet boundaries run
# start snippet boundaries regions
for boundary in boundaries.regions:
score = boundary.score
Toll10B_stg10_hic_plot = fancplot.HicPlot(Toll10B_stg10_hic, vmin=1e-03,
vmax=1e-01, norm="log",
draw_minor_ticks=False,
title="Toll10B")
genes = "external_data/flybase/dmel-all-r6.30.gtf.gz"
genes_plot = fancplot.GenePlot(genes, squash=True, group_by="gene_symbol",
aspect=0.15, label_field="gene_symbol",
show_labels=False, draw_minor_ticks=False)
rnaseq_dict = {name: os.path.join("external_data", "modencode_white", "rnaseq_aligned",
name + "_sorted_filtered_merged_canonical_chrs_rnaseq.bw")
for name in ["E0-4", "E4-8"]}
rnaseq_plot_E04 = fancplot.LinePlot(rnaseq_dict['E0-4'], fill=False,
plot_kwargs={'color': "black"},
draw_minor_ticks=False, aspect=0.05,
n_yticks=2)
rnaseq_plot_E48 = fancplot.LinePlot(rnaseq_dict['E4-8'], fill=False,
plot_kwargs={'color': "black"},
draw_minor_ticks=False, aspect=0.05,
n_yticks=2)
def plot_region(name, region, promoter, rnaseq_ylim):
output_file = os.path.join("figures", "figure_stg10_panels", name + ".pdf")
logging.info("Working on %s", name)
logging.info("Will write output to %s", output_file)
control_v4c = fancplot.Virtual4CPlot(control_stg10_hic, viewpoint=promoter,
aspect=0.05, color="black",
draw_minor_ticks=False)
vmin=-1, vmax=1, draw_tick_legend=False,
draw_minor_ticks=False, draw_tick_labels=False)
p_ins_array.plot(triangular_plotting_region)
p_ins_array.colorbar.set_label('Insulation score')
ax_ins_array.set_xticks([triangular_plotting_region.start,
triangular_plotting_region.center,
triangular_plotting_region.end])
ax_ins_array.set_yticks([50000, 200000, 1000000])
ax_ins_array.set_yticklabels(['50kb', '200kb', '1mb'])
ax_ins_array.set_ylabel('Window\nsize (bp)')
# 10. Insulation score
insulation_single = insulation.score_regions(100000)
p_ins = fancplot.LinePlot(insulation_single, ax=ax_ins, style='mid', plot_kwargs={'color': '#79C7C5'},
draw_tick_legend=False, draw_minor_ticks=False)
p_ins.plot(triangular_plotting_region)
ax_ins.set_xticks([triangular_plotting_region.start,
triangular_plotting_region.center,
triangular_plotting_region.end])
ax_ins.set_ylabel('Insulation\nscore 100kb')
# 11. Directionality flame
directionality = fanc.load(directionality_file)
p_dir_array = fancplot.GenomicVectorArrayPlot(directionality, y_scale='log', ax=ax_dir_array,
colormap='PuOr', cax=cax_dir_array,
vmin=-0.1, vmax=0.1,
draw_tick_legend=False,
draw_minor_ticks=False, draw_tick_labels=False)
