def test_load_experiment(self):
mm10tsv = resource_filename(__name__, "experiments-mm10.tsv")
hg38tsv = resource_filename(__name__, "experiments-hg38.tsv")
mm10 = models.load_experiments([mm10tsv])
self.assertEqual(len(mm10), count_valid_records(mm10))
hg38 = models.load_experiments([hg38tsv])
both = models.load_experiments([mm10tsv, hg38tsv])
self.assertEqual(len(mm10) + len(hg38), len(both))
def test_load_experiment(self):
mm10tsv = resource_filename(__name__, 'experiments-mm10.tsv')
hg38tsv = resource_filename(__name__, 'experiments-hg38.tsv')
mm10 = models.load_experiments([mm10tsv])
self.assertIn('replicates', mm10.columns)
self.assertEqual(len(mm10), count_valid_records(mm10tsv))
hg38 = models.load_experiments([hg38tsv])
both = models.load_experiments([mm10tsv, hg38tsv])
self.assertEqual(len(mm10) + len(hg38), len(both))
self.assertEqual(mm10.loc['expm']['replicates'],
['12307', '12308'])
def test_load_experiments_analysis_root(self):
with TemporaryDirectory() as analysis_dir:
with chdir(analysis_dir):
mm10tsv = resource_filename(__name__, 'experiments-mm10.tsv')
tmpname = os.path.join(analysis_dir, 'experiments-mm10.tsv')
shutil.copy(mm10tsv, tmpname)
analysis_root = os.path.dirname(mm10tsv)
mm10 = models.load_experiments([mm10tsv])
mm10tmp = models.load_experiments([tmpname],
analysis_root=analysis_root)
for i in mm10['analysis_dir'].index:
self.assertEqual(mm10['analysis_dir'][i],
mm10tmp['analysis_dir'][i])
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
sep = get_seperator(args.sep)
if args.experiments:
experiments = models.load_experiments([args.experiments], sep=sep)
else:
if args.experiment_name is None:
parser.error(
"Please provide an experiment name. (Used as filename)")
if len(args.replicates) == 0:
parser.error(
"Please provide list of replicates or experiment table")
experiments = {args.experiment_name: args.replicates}
if args.library is None:
parser.error("Please provide library information tables")
for experiment_name in experiments:
replicates = experiments[experiment_name]
logging.info('Processing:', experiment_name, ','.join(replicates))
create_quantification_cache(
args.library,
experiment_name,
replicates,
args.quantification,
sep)
def main(cmdline=None):
parser = ArgumentParser()
parser.add_argument('-n',
'--experiment-name',
required=True,
help='Experiment name to select')
add_metadata_arguments(parser)
add_debug_arguments(parser)
args = parser.parse_args(cmdline)
configure_logging(args)
header_printed = False
libraries = load_library_tables(args.libraries)
experiments = load_experiments(args.experiments)
replicates = experiments.loc[args.experiment_name, 'replicates']
for i, (library_id,
library) in enumerate(libraries.loc[replicates].iterrows()):
filename = find_library_bam_file(library)
LOGGER.info(' Reading %s %d/%d', filename, i + 1, len(replicates))
mode = get_mode(filename, 'r')
with pysam.AlignmentFile(filename, mode) as alignment:
if not header_printed:
print(str(alignment.header))
header_printed = True
for read in alignment:
print(read.to_string())
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
if args.debug:
logging.basicConfig(level=logging.DEBUG)
elif args.verbose:
logging.basicConfig(level=logging.INFO)
else:
logging.basicConfig(level=logging.WARN)
sep = get_seperator(args.sep)
if args.experiments:
experiments = models.load_experiments(args.experiments,
sep=sep,
analysis_root=args.root)
else:
if args.experiment_name is None:
parser.error(
"Please provide an experiment name. (Used as filename)")
if len(args.replicates) == 0:
parser.error(
"Please provide list of replicates or experiment table")
experiments = {args.experiment_name: args.replicates}
if args.libraries is None:
parser.error("Please provide library information tables")
libraries = models.load_library_tables(args.libraries, sep=sep)
for i, experiment in experiments.iterrows():
logging.info('Processing: %s', experiment.name)
create_quantification_cache(experiment, libraries, args.quantification,
args.model, sep)
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
if args.verbose:
logging.basicConfig(level=logging.INFO)
else:
logging.basicConfig(level=logging.ERROR)
experiments = models.load_experiments(args.experiments)
libraries = models.load_library_tables(args.libraries)
coverage = models.load_all_coverage(libraries)
if args.all_experiments:
make_combined_median_normalized_summary(experiments, coverage,
args.output_format, args.bare)
elif args.experiment_median_summary:
make_per_experiment_median_normalized_summary(experiments, coverage,
args.output_format,
args.bare)
elif args.by_experiment:
make_by_experiment_median_summary(experiments, coverage,
args.output_format, args.bare)
elif args.combined_median_summary:
make_combined_experiment_median_summary(experiments, coverage,
args.output_format, args.bare)
else:
make_experiment_by_library_coverage_plots(experiments, coverage,
args.output_format,
args.bare)
def test_create_quantification_cache_tempdir(self):
with tempfile.TemporaryDirectory() as tempdir:
temp_experiments = models.load_experiments([self.exp_tsv],
analysis_root=tempdir)
quant = 'FPKM'
score_filename = models.make_correlation_filename(
temp_experiments.iloc[0])
quant_filename = models.make_quantification_filename(
temp_experiments.iloc[0], quant, 'gene')
print(temp_experiments)
print(tempdir, score_filename)
self.assertTrue(score_filename.startswith(tempdir))
self.assertTrue(quant_filename.startswith(tempdir))
assert not os.path.exists(score_filename)
assert not os.path.exists(quant_filename)
cache = madqc.create_quantification_cache(temp_experiments.iloc[0],
self.libraries, quant,
'gene')
self.assertIsInstance(cache['rafa_spearman'], pandas.DataFrame)
self.assertTrue(os.path.exists(score_filename))
os.remove(score_filename)
self.assertTrue(os.path.exists(quant_filename))
os.remove(quant_filename)
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
experiments = load_experiments(args.experiments)
#libraries = load_library_tables(args.libraries)
return ScoreCorrelationPlot(experiments)
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
configure_logging(args)
sep = get_seperator(args.sep)
experiments = models.load_experiments(args.experiments, sep=sep)
libraries = models.load_library_tables(args.libraries, sep=sep)
output_sep = get_seperator(args.output_format)
output_extension = {"TAB": ".tsv", ",": ".csv"}[args.output_format]
if args.transcriptome:
# isoforms
load_quantifications = madqc.load_transcriptome_quantifications
quantification_extension = "_isoform_" + args.quantification + output_extension
else:
# genes
load_quantifications = madqc.load_genomic_quantifications
quantification_extension = "_gene_" + args.quantification + output_extension
for name in experiments:
filename = name + quantification_extension
replicates = experiments[name]
logger.info("%s %s: %s", name, args.quantification, ",".join(replicates))
quantifications = load_quantifications(replicates, libraries, args.quantification)
quantifications.to_csv(filename, sep=output_sep)
def load_asof_run17_experiments():
experiment_files = list(split_files_text(ASOF_RUN17_experiment_files))
experiments = models.load_experiments(experiment_files)
for experiment_name, row in experiments.iterrows():
row.replicates = [sanitize_library_name(x) for x in row.replicates]
return experiments
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
experiments = load_experiments(args.experiments)
libraries = load_library_tables(args.libraries)
plot = MeanGeneCoverage(experiments, libraries)
plot.use_experiment(args.use_experiment)
return plot
def main(cmdline=None):
parser = ArgumentParser()
parser.add_argument('-s', '--sheet', default=0, help='Sheet to use')
parser.add_argument('--header', default=None, help="header row")
parser.add_argument('filename', nargs=1, help='spreadsheet to look at')
args = parser.parse_args(cmdline)
header = int(args.header) if args.header is not None else None
book = ODFReader(args.filename[0])
data = book.parse(args.sheet, header=header)
server = ENCODED('www.encodeproject.org')
server.load_netrc()
first_experiments = models.load_experiments(
to_files(paper_433_experiment_files))
all_experiments = models.load_experiments(
to_files(ASOF_RUN17_experiment_files))
first_libraries = set(parse_replicates(first_experiments['replicates']))
all_libraries = set(parse_replicates(all_experiments['replicates']))
#print(first_libraries)
#print(all_libraries)
results = []
for i, library_id in enumerate(data[data.columns[0]]):
if library_id in first_libraries:
tranche = 1
elif library_id in all_libraries:
tranche = 2
else:
tranche = 'C'
row = find_library_info(server, library_id)
row['tranche'] = tranche
results.append(row)
if (i + 1) % 10:
print('.', end='', flush=True)
df = pandas.DataFrame(results)
df.to_csv('tranche.csv', index=False)
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
experiments = load_experiments(args.experiments)
libraries = load_library_tables(args.libraries)
if args.use_experiment:
try:
experiments = experiments.loc[[args.use_experiment]]
except KeyError:
print('{} was not found in {}'.format(args.use_experiment, ', '.join(list(experiments.index))))
return None
plot = DistributionPlot(experiments, libraries)
return plot
def main(cmdline=None):
parser = ArgumentParser()
parser.add_argument('-o', '--output', help='output directory')
parser.add_argument('--mode',
default=None,
choices=[
'customtrack',
'trackhub',
'merge_paper_wiggles',
'paper_median_coverage',
'check_bedgraphs',
'localize_tsvs',
'paper_as_single_experiment_tsv',
'paper_as_cluster_experiment_tsv',
])
args = parser.parse_args(cmdline)
experiment_files = [
os.path.expanduser(x.strip())
for x in ASOF_RUN17_experiment_files.split()
]
library_files = [
os.path.expanduser(x.strip())
for x in ASOF_RUN17_library_files.split()
]
experiments = models.load_experiments(experiment_files)
libraries = models.load_library_tables(library_files)
to_include = read_peng_20180710_cluster_memberships()
#print('{} cells to include'.format(len(to_include)))
if args.mode == 'customtrack':
make_custom_tracks()
elif args.mode == 'trackhub':
make_trackhub()
elif args.mode == 'merge_paper_wiggles':
merge_paper_wiggles(to_include, libraries)
elif args.mode == 'paper_median_coverage':
make_paper_median_coverage(to_include, libraries, args.output)
elif args.mode == 'check_bedgraphs':
check_bedgraphs(to_include, libraries)
elif args.mode == 'localize_tsvs':
localize_tsvs(experiments, libraries, args.output)
elif args.mode == 'paper_as_single_experiment_tsv':
paper920_as_single_experiment_tsv(to_include, args.output)
elif args.mode == 'paper_as_cluster_experiment_tsv':
paper920_as_cluster_experiment_tsv(to_include, args.output)
else:
parser.error('Did you want to pick an operation mode?')
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
configure_logging(args)
sep = get_seperator(args.sep)
experiments = models.load_experiments(args.experiments, sep=sep)
libraries = models.load_library_tables(args.libraries, sep=sep)
output_sep = get_seperator(args.output_format)
output_extension = {
'TAB': '.tsv',
',': '.csv',
}[args.output_format]
if args.add_names:
if args.gtf_cache is None:
parser.error('GTF-cache is needed to add names to the quantification file')
else:
logger.info('Loading GTF Cache %s', args.gtf_cache)
annotation = models.load_gtf_cache(args.gtf_cache)
else:
annotation = None
if args.transcriptome:
# isoforms
load_quantifications = madqc.load_transcriptome_quantifications
lookup_ids = models.lookup_gene_name_by_transcript_id
quantification_extension = '_isoform_' + args.quantification + output_extension
else:
# genes
load_quantifications = madqc.load_genomic_quantifications
lookup_ids = models.lookup_gene_name_by_gene_id
quantification_extension = '_gene_' + args.quantification + output_extension
for name in experiments:
filename = name + quantification_extension
replicates = experiments[name]
logger.info("%s %s: %s",
name, args.quantification, ','.join(replicates))
quantifications = load_quantifications(
replicates, libraries, args.quantification)
if annotation is not None:
quantifications = lookup_ids(annotation, quantifications)
quantifications.to_csv(filename, sep=output_sep)
def test_make_quantification_filename_other(self):
"""Does make_quantification_filename work with an alternate analysis_root
"""
results = {
'genome': 'expf_FPKM.h5',
'transcriptome': 'expf_transcriptome_FPKM.h5',
}
for reference_type in results:
mm10tsv = resource_filename(__name__, 'experiments-mm10.tsv')
path = '/tmp'
mm10 = models.load_experiments([mm10tsv], analysis_root=path)
filename = models.make_quantification_filename(
mm10.iloc[0],
reference_type=reference_type,
)
expected = os.path.join(path, results[reference_type])
self.assertEqual(filename, expected)
def test_make_correlation_filename_default(self):
"""Does make_correlation_filename work with default analysis_root
"""
results = {
'genome': 'expf_correlation.h5',
'transcriptome': 'expf_transcriptome_correlation.h5',
}
for reference_type in results:
mm10tsv = resource_filename(__name__, 'experiments-mm10.tsv')
path, _ = os.path.split(mm10tsv)
mm10 = models.load_experiments([mm10tsv])
filename = models.make_correlation_filename(
mm10.iloc[0],
reference_type=reference_type,
)
expected = os.path.join(path, results[reference_type])
self.assertEqual(filename, expected)
def load_filtered_transcripts():
sep = '\t'
cache_file = os.path.expanduser(
'~sau/genomes/mm10-M4-male/mm10-M4-male.h5')
#annotation = models.load_gtf_cache(cache_file)
annotation = None
loader = IsoformRsemLoader('FPKM', annotation)
index_name = 'transcript_id'
# loader = GeneRsemLoader(args.quantification, annotation)
#index_name = 'gene_id'
to_include = generate_to_include_asof_run17()[1:]
experiment_files = [
os.path.expanduser(x.strip())
for x in ASOF_RUN17_experiment_files.split()
]
library_files = [
os.path.expanduser(x.strip())
for x in ASOF_RUN17_library_files.split()
]
quantifications = []
for e, l in zip(experiment_files, library_files):
print('loading', e)
experiments = models.load_experiments([e], sep=sep)
libraries = models.load_library_tables([l], sep=sep)
for i, experiment in experiments.iterrows():
print(experiment)
quantification = loader.load(experiment, libraries)
quantification.columns = list(
filter_columns(quantification.columns))
quantifications.append(quantification)
sheets = pandas.concat(quantifications, axis=1)
print('all', sheets.shape)
# sheets.to_csv('C1_mouse_combined_transcript_asof_run17_unfiltred.tsv', sep='\t')
# was crashing because of _mm10 suffix
filtered = sheets[to_include]
print('filtered', filtered.shape)
return filtered
def main(cmdline=None):
parser = ArgumentParser()
parser.add_argument('-o', '--output-dir')
args = parser.parse_args(cmdline)
experiment_files = [
os.path.expanduser(x.strip())
for x in ASOF_RUN17_experiment_files.split()
]
library_files = [
os.path.expanduser(x.strip())
for x in ASOF_RUN17_library_files.split()
]
experiments = load_experiments(experiment_files)
libraries = load_library_tables(library_files)
#link_rsem(libraries, args.output_dir)
link_genome_bams(libraries, args.output_dir)
def main(cmdline=None):
parser = ArgumentParser()
parser.add_argument('--first-tranche', default=False, action='store_true',
help='Use just the first tranche as experiment list')
parser.add_argument('--name', required=True, help='submission name')
parser.add_argument('-s', '--sheet', default=0, help='Sheet to use')
parser.add_argument('--header', default=None, help="header row")
parser.add_argument('filename', nargs=1, help='driver spreadsheet')
args = parser.parse_args(cmdline)
root_fastq_url = 'http://jumpgate.caltech.edu/runfolders/volvox02/'
desplit = os.path.expanduser('~/proj/htsworkflow/htsworkflow/pipelines/desplit_fastq.py')
header = int(args.header) if args.header is not None else None
data = read_spreadsheet(args.filename[0], args.sheet, header)
print(data.shape)
if args.first_tranche:
experiment_file_list = paper_433_experiment_files.split('\n')
else:
experiment_file_list = ASOF_RUN17_experiment_files.split('\n')
experiment_files = [ os.path.expanduser(x.strip()) for x in experiment_file_list]
experiments = load_experiments(experiment_files)
experiments['replicates'] = experiments['replicates'].apply(lambda l: [x.replace('_mm10', '').replace('_clean', '') for x in l])
current_experiments = find_experiments_to_submit(experiments, data)
aliases_tsv = '{}-aliases.tsv'.format(args.name)
make_library_aliases(current_experiments, aliases_tsv)
submission_fastqs_tsv = '{}-fastqs.tsv'.format(args.name)
if not os.path.exists(submission_fastqs_tsv):
fastq_urls = find_all_fastqs(root_fastq_url, current_experiments, submission_fastqs_tsv)
fastq_urls = pandas.read_csv(submission_fastqs_tsv, sep='\t')
barcodes_tsv = '{}-barcodes.tsv'.format(args.name)
make_library_barcodes(fastq_urls, barcodes_tsv)
metadata_tsv = '{}-flowcell-details.tsv'.format(args.name)
metadata = make_metadata(fastq_urls, root_fastq_url, metadata_tsv)
merge_file = '{}-merge-fastqs.condor'.format(args.name)
make_desplit_condor(fastq_urls, metadata, desplit, root_fastq_url, merge_file)
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
configure_logging(args)
experiments = load_experiments(args.experiments)
libraries = load_library_tables(args.libraries)
if args.use_experiment:
try:
experiments = experiments.loc[[args.use_experiment]]
except KeyError:
logger.error('{} was not found in {}'.format(
args.use_experiment, ', '.join(list(experiments.index))))
return None
if len(args.gene_type_filter) > 0:
logger.info('Limiting to the following gene types {}'.format(','.join(
args.gene_type_filter)))
else:
logger.info('Using all gene types')
# ids will be None if args.gene_list_filter is None
ids = load_gene_id_list(args.gene_list_filter)
plot = GenesDetectedPlot(
experiments,
libraries,
args.genome_dir,
args.quantification,
gene_type_filter=args.gene_type_filter,
gene_list_filter=ids,
)
if __name__ == '__main__':
curdoc().add_root(plot.static_layout())
save(curdoc(), args.output, title=plot.title)
return plot
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
configure_logging(args)
sep = get_seperator(args.sep)
experiments = models.load_experiments(args.experiments, sep=sep)
libraries = models.load_library_tables(args.libraries, sep=sep)
gtf_cache = None
if args.add_names:
if args.genome_dir is None:
parser.error(
'genome-dir is needed to add names to the quantification file')
else:
gtf_cache = GTFCache(libraries, args.genome_dir)
if len(args.quantification) > 0:
quantification_list = args.quantification
else:
quantification_list = ['FPKM']
if args.transcriptome:
# isoforms
RsemLoader = IsoformRsemLoader
else:
# genes
RsemLoader = GeneRsemLoader
for quantification in quantification_list:
logger.info('Building expression matrix for %s', quantification)
for i, experiment in experiments.iterrows():
loader = RsemLoader(quantification, gtf_cache)
matrix = loader.load(experiment, libraries)
loader.save(matrix, args.output_format)
def main(cmdline=None):
parser = make_parser()
args = parser.parse_args(cmdline)
configure_logging(args)
sep = get_seperator(args.sep)
experiments = models.load_experiments(args.experiments, sep=sep)
libraries = models.load_library_tables(args.libraries, sep=sep)
if args.add_names:
if args.gtf_cache is None:
parser.error('GTF-cache is needed to add names to the quantification file')
else:
logger.info('Loading GTF Cache %s', args.gtf_cache)
annotation = models.load_gtf_cache(args.gtf_cache)
else:
annotation = None
loader = StarLoader(args.strand, annotation)
for i, experiment in experiments.iterrows():
quantification = loader.load(experiment, libraries)
loader.save(quantification, args.output_format)
def setUp(self):
self.exp_tsv = resource_filename(__name__, 'experiments-mm10.tsv')
self.lib_tsv = resource_filename(__name__, 'library-mm10-se.tsv')
self.libraries = models.load_library_tables([self.lib_tsv])
self.experiments = models.load_experiments([self.exp_tsv])
def test_load_numeric_experiment(self):
filename = resource_filename(__name__, 'experiments-numeric.tsv')
experiment = models.load_experiments([filename])
for name in experiment.index:
self.assertIsInstance(name, str)
def __init__(self, experiments, sep='\t', analysis_root=None):
self.name = None
self.experiments = load_experiments(experiments, sep)
self.quantification_name = None
self.quantification = None
