def cpdb_statistical_analysis_launcher(
self,
raw_meta: pd.DataFrame,
counts: pd.DataFrame,
counts_data: str,
iterations: int,
threshold: float,
threads: int,
debug_seed: int,
result_precision: int,
pvalue: float,
subsampler: Subsampler = None,
) -> (pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame):
if threads < 1:
core_logger.info('Using Default thread number: %s' %
self.default_threads)
threads = self.default_threads
if threshold < 0 or threshold > 1:
raise ThresholdValueException(threshold)
meta = method_preprocessors.meta_preprocessor(raw_meta)
counts = self._counts_validations(counts, meta)
if subsampler is not None:
counts = subsampler.subsample(counts)
meta = meta.filter(items=(list(counts)), axis=0)
interactions = self.database_manager.get_repository(
'interaction').get_all_expanded(include_gene=False)
genes = self.database_manager.get_repository('gene').get_all_expanded()
complex_composition = self.database_manager.get_repository(
'complex').get_all_compositions()
complex_expanded = self.database_manager.get_repository(
'complex').get_all_expanded()
deconvoluted, means, pvalues, significant_means = \
cpdb_statistical_analysis_method.call(meta,
counts,
counts_data,
interactions,
genes,
complex_expanded,
complex_composition,
iterations,
threshold,
threads,
debug_seed,
result_precision,
pvalue,
self.separator)
return pvalues, means, significant_means, deconvoluted
def cpdb_statistical_analysis_launcher(self,
raw_meta: pd.DataFrame,
counts: pd.DataFrame,
iterations: int,
threshold: float,
threads: int,
debug_seed: int,
result_precision: int
) -> (pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame):
if threads < 1:
core_logger.info('Using Default thread number: %s' % self.default_threads)
threads = self.default_threads
if threshold < 0 or threshold > 1:
raise ThresholdValueException(threshold)
meta = method_preprocessors.meta_preprocessor(raw_meta)
counts = self._counts_validations(counts, meta)
interactions = self.database_manager.get_repository('interaction').get_all_expanded(
only_cellphonedb_interactor=True)
genes = self.database_manager.get_repository('gene').get_all_expanded()
complex_composition = self.database_manager.get_repository('complex').get_all_compositions()
complex_expanded = self.database_manager.get_repository('complex').get_all_expanded()
deconvoluted, mean_pvalue, means, pvalues, significant_means = \
cpdb_statistical_analysis_method.call(meta,
counts,
interactions,
genes,
complex_expanded,
complex_composition,
iterations,
threshold,
threads,
debug_seed,
result_precision)
return pvalues, means, significant_means, mean_pvalue, deconvoluted
def cpdb_statistical_analysis_launcher(
self, raw_meta: pd.DataFrame, counts: pd.DataFrame, threads: int,
debug_seed: int, result_precision: int, log2_transform: bool
) -> (pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame
):
if threads < 1:
core_logger.info('Using Default thread number: %s' %
self.default_threads)
threads = self.default_threads
meta = method_preprocessors.meta_preprocessor(raw_meta)
counts = self._counts_validations(counts, meta)
winsorized = \
cpdb_statistical_analysis_method.call(meta,
counts,
threads,
debug_seed,
result_precision,
log2_transform)
return winsorized