def cpdb_statistical_analysis_local_method_launcher(
self,
meta_filename: str,
counts_filename: str,
counts_data: str,
project_name: str = '',
iterations: int = 1000,
threshold: float = 0.1,
output_path: str = '',
output_format: Optional[str] = None,
means_filename: str = 'means',
pvalues_filename: str = 'pvalues',
significant_means_filename: str = 'significant_means',
deconvoluted_filename='deconvoluted',
debug_seed: int = -1,
threads: int = -1,
result_precision: int = 3,
pvalue: float = 0.05,
subsampler: Subsampler = None,
) -> None:
output_path = self._set_paths(output_path, project_name)
debug_seed = int(debug_seed)
iterations = int(iterations)
threads = int(threads)
threshold = float(threshold)
result_precision = int(result_precision)
counts, meta = self._load_meta_counts(counts_filename, meta_filename)
pvalues_simple, means_simple, significant_means_simple, deconvoluted_simple = \
self.cellphonedb_app.method.cpdb_statistical_analysis_launcher(
meta,
counts,
counts_data,
iterations,
threshold,
threads,
debug_seed,
result_precision,
pvalue,
subsampler
)
write_to_file(means_simple, means_filename, output_path, output_format)
write_to_file(pvalues_simple, pvalues_filename, output_path,
output_format)
write_to_file(significant_means_simple, significant_means_filename,
output_path, output_format)
write_to_file(deconvoluted_simple, deconvoluted_filename, output_path,
output_format)
def cpdb_analysis_local_method_launcher(
self,
meta_filename: str,
counts_filename: str,
counts_data: str,
project_name: str = '',
threshold: float = 0.1,
output_path: str = '',
output_format: Optional[str] = None,
means_filename: str = 'means',
significant_means_filename: str = 'significant_means',
deconvoluted_filename='deconvoluted',
result_precision: int = 3,
subsampler: Subsampler = None,
) -> None:
output_path = self._set_paths(output_path, project_name)
result_precision = int(result_precision)
threshold = float(threshold)
counts, meta = self._load_meta_counts(counts_filename, meta_filename)
means, significant_means, deconvoluted = \
self.cellphonedb_app.method.cpdb_method_analysis_launcher(meta,
counts,
counts_data,
threshold,
result_precision,
subsampler)
write_to_file(means, means_filename, output_path, output_format)
write_to_file(significant_means, significant_means_filename,
output_path, output_format)
write_to_file(deconvoluted, deconvoluted_filename, output_path,
output_format)
def filter_all(input_path, result_path):
interactions = pd.read_csv(
os.path.join(input_path, 'interaction_input.csv'))
complexes = pd.read_csv(os.path.join(input_path, 'complex_generated.csv'))
proteins = pd.read_csv(os.path.join(input_path, 'protein_generated.csv'))
genes = pd.read_csv(os.path.join(input_path, 'gene_generated.csv'))
output_path = _set_paths(output_dir, result_path)
interacting_partners = pd.concat(
[interactions['partner_a'],
interactions['partner_b']]).drop_duplicates()
filtered_complexes = _filter_complexes(complexes, interacting_partners)
write_to_file(filtered_complexes,
'complex_input.csv',
output_path=output_path)
filtered_proteins, interacting_proteins = _filter_proteins(
proteins, filtered_complexes, interacting_partners)
write_to_file(filtered_proteins,
'protein_input.csv',
output_path=output_path)
filtered_genes = _filter_genes(genes, filtered_proteins['uniprot'])
write_to_file(filtered_genes, 'gene_input.csv', output_path=output_path)
rejected_members = interacting_partners[~(
interacting_partners.isin(filtered_complexes['complex_name'])
| interacting_partners.isin(filtered_proteins['uniprot']))]
if len(rejected_members):
app_logger.warning(
'There are some proteins or complexes not interacting properly: `{}`'
.format(', '.join(rejected_members)))