def _method_call(self, data: str, iterations: str, project_name: str, threshold: float, debug_seed: str):
result_means_filename = self._get_result_filename('means', data, iterations)
result_pvalues_filename = self._get_result_filename('pvalues', data, iterations)
result_significant_means_filename = self._get_result_filename('significant_means', data, iterations)
result_pvalues_means_filename = self._get_result_filename('pvalues_means', data, iterations)
result_deconvoluted_filename = self._get_result_filename('deconvoluted', data, iterations)
meta_filename = os.path.realpath('{}/hi_{}_meta.txt'.format(data_test_dir, data))
counts_filename = os.path.realpath('{}/hi_{}_counts.txt'.format(data_test_dir, data))
LocalMethodLauncher(cellphonedb_app.cellphonedb).cpdb_statistical_analysis_local_method_launcher(meta_filename,
counts_filename,
project_name,
iterations,
threshold,
output_test_dir,
result_means_filename,
result_pvalues_filename,
result_significant_means_filename,
result_pvalues_means_filename,
result_deconvoluted_filename,
debug_seed)
self._assert_result('means', data, iterations, project_name, result_means_filename)
self._assert_result('pvalues', data, iterations, project_name, result_pvalues_filename)
self._assert_result('significant_means', data, iterations, project_name, result_significant_means_filename)
self._assert_result('pvalues_means', data, iterations, project_name, result_pvalues_means_filename)
self._assert_result('deconvoluted', data, iterations, project_name, result_deconvoluted_filename)
def analysis(meta_filename: str,
counts_filename: str,
counts_data: str,
project_name: str,
threshold: float,
result_precision: int,
output_path: str,
output_format: str,
means_result_name: str,
significant_means_result_name: str,
deconvoluted_result_name: str,
verbose: bool,
database: Optional[str],
subsampling: bool,
subsampling_log: bool,
subsampling_num_pc: int,
subsampling_num_cells: Optional[int]
):
try:
subsampler = Subsampler(subsampling_log,
subsampling_num_pc,
subsampling_num_cells,
verbose) if subsampling else None
LocalMethodLauncher(cpdb_app.create_app(verbose,
database)).cpdb_analysis_local_method_launcher(meta_filename,
counts_filename,
counts_data,
project_name,
threshold,
output_path,
output_format,
means_result_name,
significant_means_result_name,
deconvoluted_result_name,
result_precision,
subsampler,
)
except (ReadFileException, ParseMetaException, ParseCountsException, ThresholdValueException,
AllCountsFilteredException) as e:
app_logger.error(str(e) +
(':' if (hasattr(e, 'description') and e.description) or (
hasattr(e, 'hint') and e.hint) else '') +
(' {}.'.format(e.description) if hasattr(e, 'description') and e.description else '') +
(' {}.'.format(e.hint) if hasattr(e, 'hint') and e.hint else '')
)
except EmptyResultException as e:
app_logger.warning(str(e) +
(':' if (hasattr(e, 'description') and e.description) or (
hasattr(e, 'hint') and e.hint) else '') +
(' {}.'.format(e.description) if hasattr(e, 'description') and e.description else '') +
(' {}.'.format(e.hint) if hasattr(e, 'hint') and e.hint else '')
)
except:
app_logger.error('Unexpected error')
if verbose:
traceback.print_exc(file=sys.stdout)
def _method_call(self, data: str, project_name: str, threshold: float,
result_precision: int):
result_means_filename = self._get_result_filename(
'means', data, threshold, result_precision)
result_significant_means_filename = self._get_result_filename(
'significant_means', data, threshold, result_precision)
result_deconvoluted_filename = self._get_result_filename(
'deconvoluted', data, threshold, result_precision)
meta_filename = os.path.realpath('{}/hi_{}_meta.txt'.format(
data_test_dir, data))
counts_filename = os.path.realpath('{}/hi_{}_counts.txt'.format(
data_test_dir, data))
LocalMethodLauncher(
cellphonedb_app.cellphonedb).cpdb_analysis_local_method_launcher(
meta_filename, counts_filename, project_name, threshold,
output_test_dir, result_means_filename,
result_significant_means_filename,
result_deconvoluted_filename, result_precision)
self._assert_result('means', data, project_name, result_means_filename,
threshold, result_precision)
self._assert_result('significant_means', data, project_name,
result_significant_means_filename, threshold,
result_precision)
self._assert_result('deconvoluted', data, project_name,
result_deconvoluted_filename, threshold,
result_precision)
def analysis_scanpy(adata,
var_names,
obs_names,
obs_key,
var_key=None,
gene_id_format=None,
project_name='',
threshold=0.1,
result_precision='3',
output_path='',
output_format='csv',
means_result_name='means',
significant_means_result_name='significant_means',
deconvoluted_result_name='deconvoluted',
verbose=True,
database='latest',
subsampling=False,
subsampling_log=True,
subsampling_num_pc=100,
subsampling_num_cells=None,
write=False,
add_to_uns=True):
try:
subsampler = Subsampler(subsampling_log, subsampling_num_pc,
subsampling_num_cells,
verbose) if subsampling else None
out = LocalMethodLauncher(cpdb_app.create_app(
verbose, database)).cpdb_analysis_local_method_launcher_scanpy(
adata, var_names, obs_names, obs_key, var_key, gene_id_format,
project_name, threshold, output_path, output_format,
means_result_name, significant_means_result_name,
deconvoluted_result_name, result_precision, subsampler, write,
add_to_uns)
return out
except (ReadFileException, ParseMetaException, ParseCountsException,
ThresholdValueException, AllCountsFilteredException) as e:
app_logger.error(
str(e) + (':' if (hasattr(e, 'description') and e.description) or
(hasattr(e, 'hint') and e.hint) else '') +
(' {}.'.format(e.description)
if hasattr(e, 'description') and e.description else '') +
(' {}.'.format(e.hint) if hasattr(e, 'hint') and e.hint else ''))
except EmptyResultException as e:
app_logger.warning(
str(e) + (':' if (hasattr(e, 'description') and e.description) or
(hasattr(e, 'hint') and e.hint) else '') +
(' {}.'.format(e.description)
if hasattr(e, 'description') and e.description else '') +
(' {}.'.format(e.hint) if hasattr(e, 'hint') and e.hint else ''))
except:
app_logger.error('Unexpected error')
if verbose:
traceback.print_exc(file=sys.stdout)
def statistical_analysis(meta_filename: str,
counts_filename: str,
project_name: str,
iterations: int,
threshold: float,
result_precision: int,
output_path: str,
means_result_name: str,
pvalues_result_name: str,
significant_mean_result_name: str,
means_pvalues_result_name: str,
deconvoluted_result_name: str,
debug_seed: int,
threads: int,
verbose: bool,
) -> None:
try:
LocalMethodLauncher(cpdb_app.create_app(verbose)). \
cpdb_statistical_analysis_local_method_launcher(meta_filename,
counts_filename,
project_name,
iterations,
threshold,
output_path,
means_result_name,
pvalues_result_name,
significant_mean_result_name,
means_pvalues_result_name,
deconvoluted_result_name,
debug_seed,
threads,
result_precision
)
except (ReadFileException, ParseMetaException, ParseCountsException, ThresholdValueException,
AllCountsFilteredException) as e:
app_logger.error(str(e) +
(':' if (hasattr(e, 'description') and e.description) or (
hasattr(e, 'hint') and e.hint) else '') +
(' {}.'.format(e.description) if hasattr(e, 'description') and e.description else '') +
(' {}.'.format(e.hint) if hasattr(e, 'hint') and e.hint else '')
)
except EmptyResultException as e:
app_logger.warning(str(e) +
(':' if (hasattr(e, 'description') and e.description) or (
hasattr(e, 'hint') and e.hint) else '') +
(' {}.'.format(e.description) if hasattr(e, 'description') and e.description else '') +
(' {}.'.format(e.hint) if hasattr(e, 'hint') and e.hint else '')
)
except:
app_logger.error('Unexpected error')
if (verbose):
traceback.print_exc(file=sys.stdout)
def analysis(meta_filename: str, counts_filename: str, project_name: str,
threshold: float, output_path: str, means_result_name: str,
deconvoluted_result_name: str, verbose: bool):
LocalMethodLauncher(
cpdb_app.create_app(verbose)).cpdb_analysis_local_method_launcher(
meta_filename,
counts_filename,
project_name,
threshold,
output_path,
means_result_name,
deconvoluted_result_name,
)
def statistical_analysis(meta_filename: str, counts_filename: str,
project_name: str, iterations: str, threshold: float,
output_path: str, means_result_name: str,
pvalues_result_name: str,
significant_mean_result_name: str,
means_pvalues_result_name: str,
deconvoluted_result_name: str, debug_seed: str,
threads: int, verbose: bool):
print(verbose)
LocalMethodLauncher(cpdb_app.create_app(
verbose)).cpdb_statistical_analysis_local_method_launcher(
meta_filename, counts_filename, project_name, iterations,
threshold, output_path, means_result_name, pvalues_result_name,
significant_mean_result_name, means_pvalues_result_name,
deconvoluted_result_name, debug_seed, threads)
def _method_call(self,
data: str,
project_name: str,
threshold: float,
result_precision: int,
subsampler: Optional[Subsampler] = None,
counts_data: str = 'ensembl'):
result_names_as_fixture = False
if result_names_as_fixture:
result_deconvoluted_filename, result_means_filename, result_significant_means_filename = self._original_names(
data, result_precision, threshold)
else:
result_means_filename = self._get_result_filename(
'means', data, threshold, result_precision)
result_significant_means_filename = self._get_result_filename(
'significant_means', data, threshold, result_precision)
result_deconvoluted_filename = self._get_result_filename(
'deconvoluted', data, threshold, result_precision)
meta_filename = os.path.realpath('{}/hi_{}_meta.txt'.format(
data_test_dir, data))
if counts_data == 'ensembl':
counts_file = '{}/hi_{}_counts.txt'.format(data_test_dir, data)
else:
counts_file = '{}/hi_{}_counts_{}.txt'.format(
data_test_dir, data, counts_data)
counts_filename = os.path.realpath(counts_file)
LocalMethodLauncher(
cellphonedb_app.cellphonedb).cpdb_analysis_local_method_launcher(
meta_filename, counts_filename, counts_data, project_name,
threshold, output_test_dir, 'txt', result_means_filename,
result_significant_means_filename,
result_deconvoluted_filename, result_precision, subsampler)
self._assert_result('means', data, project_name, result_means_filename,
threshold, result_precision)
self._assert_result('significant_means', data, project_name,
result_significant_means_filename, threshold,
result_precision)
self._assert_result('deconvoluted', data, project_name,
result_deconvoluted_filename, threshold,
result_precision)
def analysis(meta_filename: str, counts_filename: str, project_name: str,
threshold: float, output_path: str, means_result_name: str,
deconvoluted_result_name: str, verbose: bool):
try:
LocalMethodLauncher(
cpdb_app.create_app(verbose)).cpdb_analysis_local_method_launcher(
meta_filename,
counts_filename,
project_name,
threshold,
output_path,
means_result_name,
deconvoluted_result_name,
)
except (ReadFileException, ParseMetaException) as e:
app_logger.error(e)
except:
app_logger.error('Unexpected error')
def statistical_analysis(meta_filename: str, counts_filename: str,
project_name: str, iterations: str, threshold: float,
output_path: str, means_result_name: str,
pvalues_result_name: str,
significant_mean_result_name: str,
means_pvalues_result_name: str,
deconvoluted_result_name: str, debug_seed: str,
threads: int, verbose: bool):
print(verbose)
try:
LocalMethodLauncher(cpdb_app.create_app(
verbose)).cpdb_statistical_analysis_local_method_launcher(
meta_filename, counts_filename, project_name, iterations,
threshold, output_path, means_result_name, pvalues_result_name,
significant_mean_result_name, means_pvalues_result_name,
deconvoluted_result_name, debug_seed, threads)
except (ReadFileException, ParseMetaException) as e:
app_logger.error(e)
except:
app_logger.error('Unexpected error')
def _method_call(
self,
data: str,
iterations: int,
project_name: str,
threshold: float,
debug_seed: int,
result_precision: int,
subsampler: Optional[Subsampler] = None,
):
result_names_as_fixture = False
if result_names_as_fixture:
result_deconvoluted_filename, result_means_filename, result_pvalues_filename, result_significant_means_filename = self._original_names(
data, debug_seed, iterations, result_precision, threshold)
else:
result_means_filename = self._get_result_filename(
'means', data, iterations, debug_seed, threshold,
result_precision)
result_pvalues_filename = self._get_result_filename(
'pvalues', data, iterations, debug_seed, threshold,
result_precision)
result_significant_means_filename = self._get_result_filename(
'significant_means', data, iterations, debug_seed, threshold,
result_precision)
result_deconvoluted_filename = self._get_result_filename(
'deconvoluted', data, iterations, debug_seed, threshold,
result_precision)
meta_filename = os.path.realpath('{}/hi_{}_meta.txt'.format(
data_test_dir, data))
counts_filename = os.path.realpath('{}/hi_{}_counts.txt'.format(
data_test_dir, data))
LocalMethodLauncher(cellphonedb_app.cellphonedb). \
cpdb_statistical_analysis_local_method_launcher(meta_filename,
counts_filename,
'ensembl',
project_name,
iterations,
threshold,
output_test_dir,
'txt',
result_means_filename,
result_pvalues_filename,
result_significant_means_filename,
result_deconvoluted_filename,
debug_seed,
result_precision=result_precision,
subsampler=subsampler,
)
self._assert_result('means', data, iterations, project_name,
result_means_filename, debug_seed, threshold,
result_precision)
self._assert_result('pvalues', data, iterations, project_name,
result_pvalues_filename, debug_seed, threshold,
result_precision)
self._assert_result('significant_means', data, iterations,
project_name, result_significant_means_filename,
debug_seed, threshold, result_precision)
self._assert_result('deconvoluted', data, iterations, project_name,
result_deconvoluted_filename, debug_seed,
threshold, result_precision)
def statistical_analysis(meta_filename: str,
counts_filename: str,
counts_data='ensembl',
project_name='',
threshold=0.1,
result_precision='3',
output_path='',
output_format='csv',
means_result_name='means',
significant_means_result_name='significant_means',
deconvoluted_result_name='deconvoluted',
verbose=True,
database='latest',
subsampling=False,
subsampling_log=True,
subsampling_num_pc=100,
subsampling_num_cells=None,
debug_seed='-1',
pvalue=0.05,
pvalues_result_name='pvalues',
iterations=1000,
threads=4) -> None:
database = choose_database(None, None, value=database)
try:
subsampler = Subsampler(subsampling_log, subsampling_num_pc,
subsampling_num_cells,
verbose) if subsampling else None
LocalMethodLauncher(cpdb_app.create_app(verbose, database)). \
cpdb_statistical_analysis_local_method_launcher(meta_filename,
counts_filename,
counts_data,
project_name,
iterations,
threshold,
output_path,
output_format,
means_result_name,
pvalues_result_name,
significant_means_result_name,
deconvoluted_result_name,
debug_seed,
threads,
result_precision,
pvalue,
subsampler,
)
except (ReadFileException, ParseMetaException, ParseCountsException,
ThresholdValueException, AllCountsFilteredException) as e:
app_logger.error(
str(e) + (':' if (hasattr(e, 'description') and e.description) or
(hasattr(e, 'hint') and e.hint) else '') +
(' {}.'.format(e.description)
if hasattr(e, 'description') and e.description else '') +
(' {}.'.format(e.hint) if hasattr(e, 'hint') and e.hint else ''))
except EmptyResultException as e:
app_logger.warning(
str(e) + (':' if (hasattr(e, 'description') and e.description) or
(hasattr(e, 'hint') and e.hint) else '') +
(' {}.'.format(e.description)
if hasattr(e, 'description') and e.description else '') +
(' {}.'.format(e.hint) if hasattr(e, 'hint') and e.hint else ''))
except:
app_logger.error('Unexpected error')
if verbose:
traceback.print_exc(file=sys.stdout)