def duplicate_rate_plots(data, outdir):
"""Make plots with duplicate read rate percentages."""
meta = {
'dup_report_all': {
'key1': 'dup_report',
'key2': 'dup_all',
'title': 'Percentage of Duplicate Marked Reads',
'xlab': 'Percentage of Reads',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'dup_all.pdf'
},
'dup_report_q40': {
'key1': 'dup_report',
'key2': 'dup_q40',
'title': 'Percentage of Duplicate Marked Reads',
'xlab': 'Percentage of Reads',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'dup_q40.pdf'
}
}
for vals in meta.values():
plot_data = utils.retrieve_single_element_data_from_dict_one_output(
data, vals['key1'], vals['key2'], True)
plotting.create_rep_avg_plot(plot_data, vals['title'], vals['xlab'],
vals['ylab'], vals['xlims'],
vals['figname'])
return 0
def read_alignment_plot(data, outdir):
"""Make plot with percentage of optimally/sub-optimally/not aligned reads."""
A, B = utils.retrieve_aligned_reads(data, True)
plotting.create_read_alignment_plot(A,
'Fragment Mapping Overview: Sample A',
'Percentage of Fragments', '',
(0, 100, 10),
outdir + 'read_mapping_sample_A.pdf')
plotting.create_read_alignment_plot(B,
'Fragment Mapping Overview: Sample B',
'Percentage of Fragments', '',
(0, 100, 10),
outdir + 'read_mapping_sample_B.pdf')
A, B = utils.retrieve_data_points_from_dict_in_dict(
data, 'aligned_reads', 'mapq_percent', False)
plotting.create_line_chart(A, 'Distribution of MAPQ Scores: Sample A',
'MAPQ Score', 'Percent of Reads', (0, 60, 10),
(0, 100, 20), 3, 'upper left',
outdir + 'mapq_dist_sample_A.pdf')
plotting.create_line_chart(B, 'Distribution of MAPQ Scores: Sample B',
'MAPQ Score', 'Percent of Reads', (0, 60, 10),
(0, 100, 20), 3, 'upper left',
outdir + 'mapq_dist_sample_B.pdf')
plot_data = utils.retrieve_aligned_reads_total_only(data, True)
plotting.create_rep_avg_plot([(samp, i / 1000000.)
for samp, i in plot_data],
'Number of Aligned Read Fragments',
'Aligned Read Fragments (Millions)', '',
(0, 1800, 300), outdir + 'aligned_reads.pdf')
def create_trinucleotide_methylation_plots(data, outdir):
"""Make plots with CpWpN retention rate percentages."""
meta = {
'cah_methylation_percent': {
'key': 'cah_methylation_percent',
'title': 'Base-averaged CpApH Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'cah_meth.pdf'
},
'cag_methylation_percent': {
'key': 'cag_methylation_percent',
'title': 'Base-averaged CpApG Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'cag_meth.pdf'
},
'cth_methylation_percent': {
'key': 'cth_methylation_percent',
'title': 'Base-averaged CpTpH Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'cth_meth.pdf'
},
'ctg_methylation_percent': {
'key': 'ctg_methylation_percent',
'title': 'Base-averaged CpTpG Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'ctg_meth.pdf'
},
}
for vals in meta.values():
plot_data = utils.retrieve_single_element_data_one_output(
data, vals['key'], True)
plotting.create_rep_avg_plot(plot_data, vals['title'], vals['xlab'],
vals['ylab'], vals['xlims'],
vals['figname'])
return 0
def create_obs_exp_ratio_plots(data, outdir):
"""Make plots to show the observed/expected ratio."""
meta = {
'obsexp_mappy_cpgs': {
'key': 'mappy_cpgs_obs_exp_ratio',
'title': 'Coverage Ratio for All CpGs',
'xlab': 'Observed Coverage / Expected Coverage',
'ylab': '',
'xlims': (0, 2, 0.25),
'figname': outdir + 'obsexp_mappy_cpgs.pdf'
},
'obsexp_mappy_cgis': {
'key': 'mappy_cpis_obs_exp_ratio',
'title': 'Coverage Ratio for CpG Islands',
'xlab': 'Observed Coverage / Expected Coverage',
'ylab': '',
'xlims': (0, 2, 0.25),
'figname': outdir + 'obsexp_mappy_cgis.pdf'
},
'obsexp_mappy_rmsk': {
'key': 'mappy_rmsk_obs_exp_ratio',
'title': 'Coverage Ratio for Repeat-Masked Regions',
'xlab': 'Observed Coverage / Expected Coverage',
'ylab': '',
'xlims': (0, 2, 0.25),
'figname': outdir + 'obsexp_mappy_rmsk.pdf'
},
'obsexp_mappy_exon': {
'key': 'mappy_exon_obs_exp_ratio',
'title': 'Coverage Ratio for Exonic Regions',
'xlab': 'Observed Coverage / Expected Coverage',
'ylab': '',
'xlims': (0, 2, 0.25),
'figname': outdir + 'obsexp_mappy_exon.pdf'
},
'obsexp_mappy_gene': {
'key': 'mappy_gene_obs_exp_ratio',
'title': 'Coverage Ratio for Genic Regions',
'xlab': 'Observed Coverage / Expected Coverage',
'ylab': '',
'xlims': (0, 2, 0.25),
'figname': outdir + 'obsexp_mappy_gene.pdf'
},
'obsexp_mappy_intr': {
'key': 'mappy_intr_obs_exp_ratio',
'title': 'Coverage Ratio for Intergenic Regions',
'xlab': 'Observed Coverage / Expected Coverage',
'ylab': '',
'xlims': (0, 2, 0.25),
'figname': outdir + 'obsexp_mappy_intr.pdf'
}
}
for vals in meta.values():
plot_data = utils.retrieve_single_element_data_one_output(
data, vals['key'], True)
plotting.create_rep_avg_plot(plot_data,
vals['title'],
vals['xlab'],
vals['ylab'],
vals['xlims'],
vals['figname'],
add_line=True)
return 0
def cpn_retention_plots(data, outdir):
"""Make plots with CpN retention rate percentages."""
meta = {
'read_rtn_a': {
'key1': 'read_rtn',
'key2': 'rca',
'title': 'Read-averaged CpA Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'read_rtn_cpa.pdf'
},
'read_rtn_c': {
'key1': 'read_rtn',
'key2': 'rcc',
'title': 'Read-averaged CpC Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'read_rtn_cpc.pdf'
},
'read_rtn_g': {
'key1': 'read_rtn',
'key2': 'rcg',
'title': 'Read-averaged CpG Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'read_rtn_cpg.pdf'
},
'read_rtn_t': {
'key1': 'read_rtn',
'key2': 'rct',
'title': 'Read-averaged CpT Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'read_rtn_cpt.pdf'
},
'base_rtn_a': {
'key1': 'base_rtn',
'key2': 'bca',
'title': 'Base-averaged CpA Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'base_rtn_cpa.pdf'
},
'base_rtn_c': {
'key1': 'base_rtn',
'key2': 'bcc',
'title': 'Base-averaged CpC Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'base_rtn_cpc.pdf'
},
'base_rtn_g': {
'key1': 'base_rtn',
'key2': 'bcg',
'title': 'Base-averaged CpG Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'base_rtn_cpg.pdf'
},
'base_rtn_t': {
'key1': 'base_rtn',
'key2': 'bct',
'title': 'Base-averaged CpT Retention',
'xlab': 'Percent Retained',
'ylab': '',
'xlims': (0, 5, 1),
'figname': outdir + 'base_rtn_cpt.pdf'
}
}
for vals in meta.values():
plot_data = utils.retrieve_single_element_data_from_dict_one_output(
data, vals['key1'], vals['key2'], True)
plotting.create_rep_avg_plot(plot_data, vals['title'], vals['xlab'],
vals['ylab'], vals['xlims'],
vals['figname'])
return 0
def trimmed_stats_plots(data, outdir):
"""Make plots for post-trimming results."""
meta = {
'r1_trimmed_bp': {
'key': 'r1_trimmed_bp',
'title': 'Read 1 Percentage of Bases Trimmed',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 10, 1),
'figname': outdir + 'r1_trimmed_bp.pdf'
},
'r1_quality_bp': {
'key': 'r1_quality_bp',
'title': 'Read 1 Percentage of Bases Trimmed for Low Quality',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 1, 0.1),
'figname': outdir + 'r1_quality_bp.pdf'
},
'r1_adapter_re': {
'key': 'r1_adapter_re',
'title': 'Read 1 Percentage of Reads with Adapter Contamination',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'r1_adapter_re.pdf'
},
'r2_trimmed_bp': {
'key': 'r2_trimmed_bp',
'title': 'Read 2 Percentage of Bases Trimmed',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 10, 1),
'figname': outdir + 'r2_trimmed_bp.pdf'
},
'r2_quality_bp': {
'key': 'r2_quality_bp',
'title': 'Read 2 Percentage of Bases Trimmed for Low Quality',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 1, 0.1),
'figname': outdir + 'r2_quality_bp.pdf'
},
'r2_adapter_re': {
'key': 'r2_adapter_re',
'title': 'Read 2 Percentage of Reads with Adapter Contamination',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'r2_adapter_re.pdf'
}
}
for vals in meta.values():
plot_data = utils.retrieve_single_element_data_one_output(
data, vals['key'], True)
plotting.create_rep_avg_plot([(s, 100 * i) for s, i in plot_data],
vals['title'], vals['xlab'], vals['ylab'],
vals['xlims'], vals['figname'])
return 0
def raw_read_base_qual_plots(data, outdir):
"""Make the raw read base quality plots."""
meta = {
'r1_read_base_20': {
'key': 'r1_read_base_20',
'title': 'Read 1 Reads with Avg. Base Quality >= 20',
'xlab': 'Percentage of Reads',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'r1_read_base_20.pdf'
},
'r1_read_base_30': {
'key': 'r1_read_base_30',
'title': 'Read 1 Reads with Avg. Base Quality >= 30',
'xlab': 'Percentage of Reads',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'r1_read_base_30.pdf'
},
'r1_low_base_qual': {
'key': 'r1_low_base_qual',
'title': 'Read 1 Bases with Base Quality < 20',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 10, 1),
'figname': outdir + 'r1_low_base_qual.pdf'
},
'r1_med_base_qual': {
'key': 'r1_med_base_qual',
'title': 'Read 1 Bases with Base Quality >= 20 and <= 30',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 10, 1),
'figname': outdir + 'r1_med_base_qual.pdf'
},
'r1_hi_base_qual': {
'key': 'r1_hi_base_qual',
'title': 'Read 1 Bases with Base Quality > 30',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'r1_hi_base_qual.pdf'
},
'r2_read_base_20': {
'key': 'r2_read_base_20',
'title': 'Read 2 Reads with Avg. Base Quality >= 20',
'xlab': 'Percentage of Reads',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'r2_read_base_20.pdf'
},
'r2_read_base_30': {
'key': 'r2_read_base_30',
'title': 'Read 2 Reads with Avg. Base Quality >= 30',
'xlab': 'Percentage of Reads',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'r2_read_base_30.pdf'
},
'r2_low_base_qual': {
'key': 'r2_low_base_qual',
'title': 'Read 2 Bases with Base Quality < 20',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 10, 1),
'figname': outdir + 'r2_low_base_qual.pdf'
},
'r2_med_base_qual': {
'key': 'r2_med_base_qual',
'title': 'Read 2 Bases with Base Quality >= 20 and <= 30',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 10, 1),
'figname': outdir + 'r2_med_base_qual.pdf'
},
'r2_hi_base_qual': {
'key': 'r2_hi_base_qual',
'title': 'Read 2 Bases with Base Quality > 30',
'xlab': 'Percentage of Bases',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'r2_hi_base_qual.pdf'
},
}
for vals in meta.values():
plot_data = utils.retrieve_single_element_data_one_output(
data, vals['key'], True)
plotting.create_rep_avg_plot(plot_data, vals['title'], vals['xlab'],
vals['ylab'], vals['xlims'],
vals['figname'])
return 0
def cpg_region_plots(data, outdir):
"""Make CpG region plots."""
meta = {
'ExonicCpGs_percent_covered_all': {
'key1': 'ExonicCpGs_percent_covered',
'key2': 'All',
'title': 'CpG Coverage of Exonic Regions',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'exon_cpgs_all.pdf'
},
'ExonicCpGs_percent_covered_q40': {
'key1': 'ExonicCpGs_percent_covered',
'key2': 'Q40',
'title': 'CpG Coverage of Exonic Regions',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'exon_cpgs_q40.pdf'
},
'RepeatCpGs_percent_covered_all': {
'key1': 'RepeatCpGs_percent_covered',
'key2': 'All',
'title': 'CpG Coverage of Repeat-Masked Regions',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'rmsk_cpgs_all.pdf'
},
'RepeatCpGs_percent_covered_q40': {
'key1': 'RepeatCpGs_percent_covered',
'key2': 'Q40',
'title': 'CpG Coverage of Repeat-Masked Regions',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'rmsk_cpgs_q40.pdf'
},
'GenicCpGs_percent_covered_all': {
'key1': 'GenicCpGs_percent_covered',
'key2': 'All',
'title': 'CpG Coverage of Genic Regions',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'gene_cpgs_all.pdf'
},
'GenicCpGs_percent_covered_q40': {
'key1': 'GenicCpGs_percent_covered',
'key2': 'Q40',
'title': 'CpG Coverage of Genic Regions',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'gene_cpgs_q40.pdf'
},
'CGICpGs_percent_covered_all': {
'key1': 'CGICpGs_percent_covered',
'key2': 'All',
'title': 'CpG Coverage of CpG Island Regions',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'cgis_cpgs_all.pdf'
},
'CGICpGs_percent_covered_q40': {
'key1': 'CGICpGs_percent_covered',
'key2': 'Q40',
'title': 'CpG Coverage of CpG Island Regions',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'cgis_cpgs_q40.pdf'
},
'TotalCpGs_percent_covered_all': {
'key1': 'TotalCpGs_percent_covered',
'key2': 'All',
'title': 'Coverage for All CpGs in Genome',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'tote_cpgs_all.pdf'
},
'TotalCpGs_percent_covered_q40': {
'key1': 'TotalCpGs_percent_covered',
'key2': 'Q40',
'title': 'Coverage for All CpGs in Genome',
'xlab': 'Percentage of CpGs',
'ylab': '',
'xlims': (0, 100, 10),
'figname': outdir + 'tote_cpgs_q40.pdf'
}
}
for vals in meta.values():
plot_data = utils.retrieve_single_element_data_from_dict_one_output(
data, vals['key1'], vals['key2'], True)
plotting.create_rep_avg_plot(plot_data, vals['title'], vals['xlab'],
vals['ylab'], vals['xlims'],
vals['figname'])
return 0