def call(meta: pd.DataFrame, counts: pd.DataFrame, interactions: pd.DataFrame, genes: pd.DataFrame,
complexes: pd.DataFrame, complex_compositions: pd.DataFrame, iterations: int = 1000, threshold: float = 0.1,
threads: int = 4, debug_seed=False, round_decimals: int = 1) -> (
pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame):
core_logger.info(
'[Cluster Statistical Analysis Complex] Threshold:{} Iterations:{} Debug-seed:{} Threads:{}'.format(
threshold, iterations, debug_seed, threads))
if debug_seed >= 0:
pd.np.random.seed(debug_seed)
core_logger.warning('Debug random seed enabled. Setted to {}'.format(debug_seed))
cells_names = sorted(counts.columns)
interactions_filtered, counts_filtered, complex_in_counts = prefilters(interactions, counts, genes, complexes,
complex_compositions)
if interactions_filtered.empty:
return pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
complex_significative_protein = get_complex_significative(complex_in_counts, counts_filtered, complex_compositions,
cells_names)
clusters = cpdb_statistical_analysis_helper.build_clusters(meta, counts_filtered)
core_logger.info('Running Real Complex Analysis')
cluster_interactions = cpdb_statistical_analysis_helper.get_cluster_combinations(clusters['names'])
interactions_processed = get_interactions_processed(interactions_filtered, complex_significative_protein)
base_result = cpdb_statistical_analysis_helper.build_result_matrix(interactions_processed, cluster_interactions)
real_mean_analysis = cpdb_statistical_analysis_helper.mean_analysis(interactions_processed, clusters,
cluster_interactions, base_result)
real_percents_analysis = cpdb_statistical_analysis_helper.percent_analysis(clusters, threshold,
interactions_processed,
cluster_interactions,
base_result)
statistical_mean_analysis = cpdb_statistical_analysis_helper.shuffled_analysis(iterations, meta, counts_filtered,
interactions_processed,
cluster_interactions, base_result,
threads)
result_percent = cpdb_statistical_analysis_helper.build_percent_result(real_mean_analysis,
real_percents_analysis,
statistical_mean_analysis,
interactions_processed,
cluster_interactions, base_result)
pvalues_result, means_result, significant_means, mean_pvalue_result, deconvoluted_result = build_results(
interactions_filtered,
real_mean_analysis,
result_percent,
clusters['means'],
complex_compositions,
counts,
genes,
round_decimals
)
return pvalues_result, means_result, significant_means, mean_pvalue_result, deconvoluted_result
def call(
meta: pd.DataFrame,
counts: pd.DataFrame,
counts_data: str,
interactions: pd.DataFrame,
pvalue: float,
separator: str,
iterations: int = 1000,
threshold: float = 0.1,
threads: int = 4,
debug_seed: int = -1,
result_precision: int = 3,
) -> (pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame):
core_logger.info(
'[Cluster Statistical Analysis Simple] '
'Threshold:{} Iterations:{} Debug-seed:{} Threads:{} Precision:{}'.
format(threshold, iterations, debug_seed, threads, result_precision))
if debug_seed >= 0:
pd.np.random.seed(debug_seed)
core_logger.warning(
'Debug random seed enabled. Setted to {}'.format(debug_seed))
interactions_filtered, counts_filtered = prefilters(
counts, interactions, counts_data)
if interactions_filtered.empty or counts_filtered.empty:
return pd.DataFrame(), pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
clusters = cpdb_statistical_analysis_helper.build_clusters(
meta, counts_filtered)
core_logger.info('Running Real Simple Analysis')
cluster_interactions = cpdb_statistical_analysis_helper.get_cluster_combinations(
clusters['names'])
base_result = cpdb_statistical_analysis_helper.build_result_matrix(
interactions_filtered, cluster_interactions, separator)
real_mean_analysis = cpdb_statistical_analysis_helper.mean_analysis(
interactions_filtered,
clusters,
cluster_interactions,
base_result,
separator,
suffixes=('_1', '_2'),
counts_data=counts_data)
real_percent_analysis = cpdb_statistical_analysis_helper.percent_analysis(
clusters,
threshold,
interactions_filtered,
cluster_interactions,
base_result,
separator,
suffixes=('_1', '_2'),
counts_data=counts_data)
statistical_mean_analysis = cpdb_statistical_analysis_helper.shuffled_analysis(
iterations,
meta,
counts_filtered,
interactions_filtered,
cluster_interactions,
base_result,
threads,
separator,
suffixes=('_1', '_2'),
counts_data=counts_data)
result_percent = cpdb_statistical_analysis_helper.build_percent_result(
real_mean_analysis, real_percent_analysis, statistical_mean_analysis,
interactions_filtered, cluster_interactions, base_result, separator)
pvalues_result, means_result, significant_means, deconvoluted_result = build_results(
interactions_filtered, real_mean_analysis, result_percent,
clusters['means'], result_precision, pvalue, counts_data)
return pvalues_result, means_result, significant_means, deconvoluted_result
def call(meta: pd.DataFrame,
counts: pd.DataFrame,
counts_data: str,
interactions: pd.DataFrame,
genes: pd.DataFrame,
complexes: pd.DataFrame,
complex_compositions: pd.DataFrame,
pvalue: float,
separator: str,
iterations: int = 1000,
threshold: float = 0.1,
threads: int = 4,
debug_seed: int = -1,
result_precision: int = 3,
) -> (pd.DataFrame, pd.DataFrame, pd.DataFrame, pd.DataFrame):
core_logger.info(
'[Cluster Statistical Analysis] '
'Threshold:{} Iterations:{} Debug-seed:{} Threads:{} Precision:{}'.format(threshold,
iterations,
debug_seed,
threads,
result_precision))
if debug_seed >= 0:
np.random.seed(debug_seed)
core_logger.warning('Debug random seed enabled. Setted to {}'.format(debug_seed))
cells_names = sorted(counts.columns)
interactions.set_index('id_interaction', drop=True, inplace=True)
interactions_reduced = interactions[['multidata_1_id', 'multidata_2_id']].drop_duplicates()
complex_compositions.set_index('id_complex_composition', inplace=True, drop=True)
# Add id multidata to counts input
counts: pd.DataFrame = counts.merge(genes[['id_multidata', 'ensembl', 'gene_name', 'hgnc_symbol']],
left_index=True, right_on=counts_data)
counts_relations = counts[['id_multidata', 'ensembl', 'gene_name', 'hgnc_symbol']].copy()
counts.set_index('id_multidata', inplace=True, drop=True)
counts = counts[cells_names]
counts = counts.astype('float32')
counts = counts.groupby(counts.index).mean()
if counts.empty:
raise AllCountsFilteredException(hint='Are you using human data?')
# End add id multidata
interactions_filtered, counts_filtered, complex_composition_filtered = \
cpdb_statistical_analysis_helper.prefilters(interactions_reduced,
counts,
complexes,
complex_compositions)
if interactions_filtered.empty:
raise NoInteractionsFound()
clusters = cpdb_statistical_analysis_helper.build_clusters(meta, counts_filtered, complex_composition_filtered)
core_logger.info('Running Real Analysis')
cluster_interactions = cpdb_statistical_analysis_helper.get_cluster_combinations(clusters['names'])
base_result = cpdb_statistical_analysis_helper.build_result_matrix(interactions_filtered,
cluster_interactions,
separator)
real_mean_analysis = cpdb_statistical_analysis_helper.mean_analysis(interactions_filtered,
clusters,
cluster_interactions,
base_result,
separator)
real_percents_analysis = cpdb_statistical_analysis_helper.percent_analysis(clusters,
threshold,
interactions_filtered,
cluster_interactions,
base_result,
separator)
core_logger.info('Running Statistical Analysis')
statistical_mean_analysis = cpdb_statistical_analysis_helper.shuffled_analysis(iterations,
meta,
counts_filtered,
interactions_filtered,
cluster_interactions,
complex_composition_filtered,
base_result,
threads,
separator)
result_percent = cpdb_statistical_analysis_helper.build_percent_result(real_mean_analysis,
real_percents_analysis,
statistical_mean_analysis,
interactions_filtered,
cluster_interactions,
base_result,
separator)
pvalues_result, means_result, significant_means, deconvoluted_result = build_results(
interactions_filtered,
interactions,
counts_relations,
real_mean_analysis,
result_percent,
clusters['means'],
complex_composition_filtered,
counts,
genes,
result_precision,
pvalue,
counts_data
)
return pvalues_result, means_result, significant_means, deconvoluted_result