def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
annotation = load_gtf_cache(args.gtf_cache)
protein_coding = protein_coding_gene_ids(annotation)
toplot = OrderedDict()
for filename in args.filenames:
all_quantifications = pandas.read_csv(filename, header=0, index_col=0)
if 'gene_name' in all_quantifications.columns:
columns = [
c for c in all_quantifications.columns if c != 'gene_name'
]
all_quantifications = all_quantifications[columns]
protein_quantifications = all_quantifications.loc[protein_coding]
_, filename = os.path.split(filename)
basename, _ = os.path.splitext(filename)
png_name = basename + '.png'
csv_name = 'genes-detected_' + basename + '.csv'
binned_quantifications = bin_library_quantification(
protein_quantifications, args.quantification)
binned_quantifications.to_csv(csv_name)
f = plot_gene_detection_histogram(
binned_quantifications,
basename,
show_genes_detected=not args.hide_detected_sum)
toplot[png_name] = f
save_fixed_height(toplot)
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
annotation = load_gtf_cache(args.gtf_cache)
protein_coding = protein_coding_gene_ids(annotation)
toplot = OrderedDict()
for filename in args.filenames:
all_quantifications = pandas.read_csv(filename, header=0, index_col=0)
if 'gene_name' in all_quantifications.columns:
columns = [ c for c in all_quantifications.columns if c != 'gene_name']
all_quantifications = all_quantifications[columns]
protein_quantifications = all_quantifications.loc[protein_coding]
_, filename = os.path.split(filename)
basename, _ = os.path.splitext(filename)
png_name = basename + '.png'
csv_name = 'genes-detected_' + basename + '.csv'
binned_quantifications = bin_library_quantification(protein_quantifications, 'FPKM')
binned_quantifications.to_csv(csv_name)
f = plot_gene_detection_histogram(binned_quantifications,
basename,
show_genes_detected=not args.hide_detected_sum)
toplot[png_name] = f
save_fixed_height(toplot)
def make_experiment_by_library_coverage_plots(experiments, coverage,
output_format, bare):
"""Coverage plot showing all the libraries for an experiment
"""
tosave = OrderedDict()
for experiment_name, experiment_row in experiments.iterrows():
library_ids = experiment_row['replicates']
image_name = experiment_name + '.coverage.' + output_format
f = make_coverage_plot(experiment_name, coverage[library_ids])
tosave[image_name] = f
save_fixed_height(tosave)
def make_combined_median_normalized_summary(experiments, coverage,
output_format, bare):
"""Coverage plot showing the median +/-sd of all libraries for an experiment
"""
assert isinstance(experiments, pandas.DataFrame)
tosave = OrderedDict()
library_ids = []
for experiment in experiments.index:
library_ids.extend(experiments['replicates'][experiment])
f = make_median_normalized_summary(experiment, library_ids, coverage, bare)
if bare:
plot_suffix = '.median-normalized.coverage.bare.'
else:
plot_suffix = '.median-normalized.coverage.'
image_name = experiment + plot_suffix + output_format
f.savefig(image_name)
tosave[image_name] = f
save_fixed_height(tosave)
def make_by_experiment_median_summary(experiments, coverage, output_format,
bare):
"""Coverage plot showing the median +/-sd of all libraries for an experiment
"""
tosave = OrderedDict()
with pyplot.style.context('seaborn-dark-palette'):
for experiment in experiments.index:
f = pyplot.figure(dpi=100)
ax = f.add_subplot(1, 1, 1)
add_median_plot(ax, experiments, experiment, coverage, bare)
ax.set_title('Median coverage for {}'.format(experiment))
ax.set_xlabel("position quantile (5' to 3')")
ax.set_ylabel('Read depth')
ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.0)
image_name = experiment + '.median.coverage.' + output_format
tosave[image_name] = f
save_fixed_height(tosave)
def make_per_experiment_median_normalized_summary(experiments, coverage,
output_format, bare):
"""Coverage plot showing the median +/-sd of all libraries for each experiment
"""
assert isinstance(experiments, pandas.DataFrame)
tosave = OrderedDict()
library_ids = []
if bare:
plot_suffix = '.median-normalized.coverage.bare.'
else:
plot_suffix = '.median-normalized.coverage.'
for experiment in experiments.index:
library_ids = experiments['replicates'][experiment]
f = make_median_normalized_summary(experiment, library_ids, coverage,
bare)
ax = f.get_axes()[0]
ax.set_title('Median normalized coverage for {}'.format(
experiment.replace('_', ' ')))
image_name = experiment + plot_suffix + output_format
f.savefig(image_name)
tosave[image_name] = f
save_fixed_height(tosave)