def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
sep = models.get_seperator(args.sep)
libraries = models.load_library_tables(args.libraries, sep)
fastqs = dict(find_fastqs(libraries))
dag = generate_star_rsem_analysis(args, libraries, fastqs)
print(dag)
return 0
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
env = Environment(loader=PackageLoader('woldrnaseq', 'templates'))
sep = models.get_seperator(args.sep)
experiments = models.load_experiments([args.experiments], sep)
libraries = models.load_library_tables([args.libraries], sep)
samstats = models.load_all_samstats(libraries)
distribution = models.load_all_distribution(libraries)
coverage = models.load_all_coverage(libraries)
seen_libraries = set()
experiment_report = {}
transcript_library_plots = []
plots = {}
plot_handle = itertools.count()
for experiment in experiments:
scores = models.load_correlations(experiment)
quantifications = models.load_quantifications(experiment, args.quantification)
coverage_handle = str(next(plot_handle))
plots[coverage_handle] = make_coverage_plot(coverage, experiments, experiment)
distribution_handle = str(next(plot_handle))
plots[distribution_handle] = make_distribution_plot(distribution, experiments, experiment)
spearman_filename = make_correlation_heatmap(scores, 'rafa_spearman', experiment)
spike_variance_handle = str(next(plot_handle))
plots[spike_variance_handle] = make_spikein_variance_plot(quantifications, experiments, experiment)
library_ids = experiments[experiment]
seen_libraries.update(set(library_ids))
for library_id in library_ids:
transcript_handle = str(next(plot_handle))
plots[transcript_handle] = make_spikein_per_transcript_plot(
quantifications, library_id, args.quantification)
transcript_library_plots.append(transcript_handle)
experiment_report[experiment] = {
'samstats': samstats.select(lambda x: x in library_ids).to_html(),
'spearman': scores.rafa_spearman.to_html(),
'coverage': coverage_handle,
'distribution': distribution_handle,
'spearman_plot': spearman_filename,
'spike_variance': spike_variance_handle,
}
spare_libraries = set(libraries.index).difference(seen_libraries)
print(libraries.index)
print(seen_libraries)
print(spare_libraries)
script, plot_divs = components(plots)
template = env.get_template('rnaseq.html')
page = template.render(
experiments=experiments,
experiment_report=experiment_report,
transcript_library_plots=transcript_library_plots,
plot_divs=plot_divs,
bokeh_script=script,
)
with open(args.output, 'wt') as outstream:
outstream.write(page)
