From dc30289487ca332ef099486f01211f831eeb926e Mon Sep 17 00:00:00 2001 From: Daniel Domingo-Fernandez Date: Sat, 15 May 2021 09:24:09 +0200 Subject: [PATCH] Push code --- .../l1000_harmonized_expression.json | 92566 ++++++++++++++++ src/data_preprocess/README.md | 13 + src/data_preprocess/chemicals.py | 188 + src/data_preprocess/diseases.py | 251 + src/data_preprocess/gene_count.py | 289 + src/data_preprocess/resolver.py | 81 + src/notebooks/1.0-plot_kg_distributions.ipynb | 455 + .../1.1-plot_permute_kg_distributions.ipynb | 454 + src/notebooks/1.2-dataset_statistics.ipynb | 236 + src/notebooks/2.0-dataset_probabilities.ipynb | 449 + src/notebooks/3.0-kg_statistics.ipynb | 600 + .../4.0-subgraph_full_analysis.ipynb | 416 + src/notebooks/4.1-analysis_of_pairs.ipynb | 511 + ....0-transcriptomics_specific_analysis.ipynb | 476 + src/notebooks/6.0-benchmark_analysis.ipynb | 728 + src/notebooks/utils.py | 558 + 16 files changed, 98271 insertions(+) create mode 100644 data/transcriptomics/l1000_harmonized_expression.json create mode 100644 src/data_preprocess/README.md create mode 100644 src/data_preprocess/chemicals.py create mode 100644 src/data_preprocess/diseases.py create mode 100644 src/data_preprocess/gene_count.py create mode 100644 src/data_preprocess/resolver.py create mode 100644 src/notebooks/1.0-plot_kg_distributions.ipynb create mode 100644 src/notebooks/1.1-plot_permute_kg_distributions.ipynb create mode 100644 src/notebooks/1.2-dataset_statistics.ipynb create mode 100644 src/notebooks/2.0-dataset_probabilities.ipynb create mode 100644 src/notebooks/3.0-kg_statistics.ipynb create mode 100644 src/notebooks/4.0-subgraph_full_analysis.ipynb create mode 100644 src/notebooks/4.1-analysis_of_pairs.ipynb create mode 100644 src/notebooks/5.0-transcriptomics_specific_analysis.ipynb create mode 100644 src/notebooks/6.0-benchmark_analysis.ipynb create mode 100644 src/notebooks/utils.py diff --git 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"ncbigene:115207": 0 + } +} \ No newline at end of file diff --git a/src/data_preprocess/README.md b/src/data_preprocess/README.md new file mode 100644 index 0000000..c195cbb --- /dev/null +++ b/src/data_preprocess/README.md @@ -0,0 +1,13 @@ +# Data pre-processing + +This folder contains all the files involving pre-processing and creating of expression dataset into a desired format. It also includes scripts for analysis. + +This desired format is a dictionary of dictionaries. Each dictionary key is a chemical or diseases and the value consist of dictionary with gene and its respective fold changes. + +1. [chemicals.py](chemicals.py) notebook consists of pre-processing of chemical datasets. + +1. [diseases.py](diseases.py) notebook consists of pre-processing of various disease datasets. + +1. [gene_count.py](gene_count.py) notebook for analysis of genes in various datasets + +1. [resolver](gene_count.py) notebook for normalizing of different namespaces into specific ones (`mondo` for diseases, `pubchem.compound` for chemicals and `ncbigene` for genes) diff --git a/src/data_preprocess/chemicals.py b/src/data_preprocess/chemicals.py new file mode 100644 index 0000000..42f1a45 --- /dev/null +++ b/src/data_preprocess/chemicals.py @@ -0,0 +1,188 @@ +# -*- coding: utf-8 -*- + +""" + Get chemical mapping from various gene expression files +""" + +import os +import pandas as pd +import json +import pickle +import logging +from tqdm import tqdm + +from pyenveda.resolver import canonicalize_smiles, canonicalize_curie, canonicalize_name +from resolver import get_local_xrefs + +logger = logging.getLogger(__name__) + +protein_map = get_local_xrefs(class_name='protein') + +DATA_DIR = '../../data' + + +def get_creed_chemicals(): + """ + Get chemical mapping from CREED + """ + chemical_df = pd.read_json( + os.path.join(DATA_DIR, 'creeds', 'raw', 'data', 'drug_data.json'), + orient='records', + ) + + chemical_df.drop( + [ + 'cell_type', + 'pert_ids', + 'platform', + 'curator', + 'geo_id', + 'version', + 'ctrl_ids', + 'id', + ], + axis=1, + inplace=True, + ) + + chemical_df = chemical_df[chemical_df['organism'] == 'human'] + chemical_df.drop('organism', axis=1, inplace=True) + + CACHE_PATH = os.path.join(DATA_DIR, 'creeds', 'chemical_cache.pickle') + + if os.path.exists(CACHE_PATH): + with open(CACHE_PATH, 'rb') as file: + cache_dict = pickle.load(file) + else: + cache_dict = {} + + counter_cache = 0 + + expression_data = cache_dict + + unmapped_protein = set() + total_protein = set() + + for line in tqdm(chemical_df.values, desc='Extracting information from CREED'): + ( + smiles, + drugbank_id, + pubchem_id, + chem_name, + down_regulated_genes, + up_regulated_gene, + ) = line + + counter_cache += 1 + + if pd.isna(pubchem_id) or pubchem_id == 'nan': + can_curie = canonicalize_curie(f'drugbank:{drugbank_id}') + if can_curie is None: + if pd.notna(smiles): + can_curie = canonicalize_smiles(smiles) + if can_curie is None: + pubchem_data = canonicalize_name(chem_name, entity_class='chemical') + can_curie = pubchem_data.curie + else: + pubchem_id = str(pubchem_id) + can_curie = f"pubchem.compound:{pubchem_id.split('.')[0]}" + + if can_curie is None: + continue + + if can_curie in expression_data: + continue + + down_regulated_genes.extend(up_regulated_gene) + + logger.info(f'Populating {len(down_regulated_genes)} genes') + + for gene_fold_change_pair in down_regulated_genes: + hgnc_sym = gene_fold_change_pair[0] + val = gene_fold_change_pair[1] + + total_protein.add(hgnc_sym) + + ncbigene_id = protein_map.get(f'hgnc:{hgnc_sym}') + + if ncbigene_id: + if can_curie not in expression_data: + expression_data[can_curie] = {} + + expression_data[can_curie][ncbigene_id] = val + + cache_dict[can_curie][ncbigene_id] = val + else: + unmapped_protein.add(hgnc_sym) + + # export cache to pickle and reset + if counter_cache == 15: + with open(CACHE_PATH, 'wb') as handle: + pickle.dump(cache_dict, handle, protocol=pickle.HIGHEST_PROTOCOL) + + counter_cache = 0 + + logger.info(f'Extracted {len(expression_data)} chemicals from CREED') + logger.info(f'Unmapped {len(unmapped_protein)} proteins out of {len(total_protein)} protein') + + with open(os.path.join(DATA_DIR, 'creeds', 'normalized', 'chemical_expression.json'), 'w') as f: + json.dump(expression_data, f, ensure_ascii=False, indent=2) + + +def get_l1000_chemicals(): + df = pd.read_csv( + os.path.join(DATA_DIR, 'lc1000', 'raw', 'data', 'l1000.csv'), + usecols=[ + 'pubchem.id', + 'gene.symbol', + 'direction', + ] + ) + + df['pubchem.id'] = df['pubchem.id'].apply(lambda x: f'pubchem.compound:{x}') + + total_protein = set() + unmapped_protein = set() + + ncbigene_ids = [] + for i in df['gene.symbol']: + total_protein.add(i) + idx = protein_map.get(f'hgnc:{i}') + if not idx: + unmapped_protein.add(i) + ncbigene_ids.append(idx) + + df['ncbigene'] = ncbigene_ids + df.drop('gene.symbol', axis=1, inplace=True) + + logger.info(f'Unmapped {len(unmapped_protein)} protein out of {len(total_protein)} proteins') + + expression_data = {} + + for pubchem_id, val, gene in tqdm(df.values, desc='Extracting values'): + if pubchem_id: + if pubchem_id not in expression_data: + expression_data[pubchem_id] = {} + + if gene: + expression_data[pubchem_id][gene] = val + + logger.info(f'Extracted {len(expression_data)} chemicals from L1000 dataset.') + + with open(os.path.join(DATA_DIR, 'lc1000', 'normalized', 'chemical_expression.json'), 'w') as f: + json.dump(expression_data, f, ensure_ascii=False, indent=2) + + +def main( + creed: bool = False, + l1000: bool = False +): + if creed: + get_creed_chemicals() + + if l1000: + get_l1000_chemicals() + + +if __name__ == '__main__': + main() diff --git a/src/data_preprocess/diseases.py b/src/data_preprocess/diseases.py new file mode 100644 index 0000000..1f9e955 --- /dev/null +++ b/src/data_preprocess/diseases.py @@ -0,0 +1,251 @@ +# -*- coding: utf-8 -*- + +""" + Get disease mapping from various gene expression files +""" + +import os +import pickle +import pandas as pd +import logging +import json + +from tqdm import tqdm + +from resolver import get_local_xrefs +from pyenveda.resolver import canonicalize_name + +logger = logging.getLogger(__name__) + +disease_map = get_local_xrefs(class_name='pathology') +protein_map = get_local_xrefs(class_name='protein') + +DATA_DIR = '../../data' + + +def get_creed_disease(): + df = pd.read_json( + os.path.join(DATA_DIR, 'creeds', 'raw', 'data', 'disease_data.json'), + orient='records', + ) + + df.drop( + [ + 'cell_type', + 'pert_ids', + 'geo_id', + 'platform', + 'curator', + 'geo_id', + 'version', + 'ctrl_ids', + 'id', + ], + axis=1, + inplace=True, + ) + + df = df[df['organism'] == 'human'] + df.drop('organism', axis=1, inplace=True) + + # export cache to pickle each 15 iterations + CACHE_PATH = os.path.join(DATA_DIR, 'creeds', 'disease_cache.pickle') + + if os.path.exists(CACHE_PATH): + with open(CACHE_PATH, 'rb') as file: + cache_dict = pickle.load(file) + else: + cache_dict = {} + + counter_cache = 0 + + disease_expression_data = cache_dict + + unmapped_proteins = set() + total_proteins = set() + + for line in tqdm(df.values, desc='Extracting disease information from CREED'): + ( + doid, + uml_id, + down_regulated_genes, + up_regulated_gene, + disease_name, + ) = line + + counter_cache += 1 + + if pd.isna(doid) or doid == 'None': + can_curie = disease_map.get(f'umls:{uml_id}') + else: + can_curie = disease_map.get(f'doid:{doid.split(":")[1]}') + + if can_curie in disease_expression_data: + continue + + if not can_curie: + continue + + down_regulated_genes.extend(up_regulated_gene) + + logger.info(f'Populating {len(down_regulated_genes)} genes') + + for gene_fold_change_pair in down_regulated_genes: + hgnc_sym = gene_fold_change_pair[0] + val = gene_fold_change_pair[1] + total_proteins.add(hgnc_sym) + + ncbigene_id = protein_map.get(f'hgnc:{hgnc_sym}') + + if ncbigene_id: + if can_curie not in disease_expression_data: + disease_expression_data[can_curie] = {} + + disease_expression_data[can_curie][ncbigene_id] = val + + cache_dict[can_curie][ncbigene_id] = val + else: + unmapped_proteins.add(hgnc_sym) + + # export cache to pickle and reset + if counter_cache == 15: + with open(CACHE_PATH, 'wb') as handle: + pickle.dump(cache_dict, handle, protocol=pickle.HIGHEST_PROTOCOL) + + counter_cache = 0 + + logger.info(f'Extracted {len(disease_expression_data)} diseases from CREED') + logger.info(f'Unmapped {len(unmapped_proteins)} proteins out of {len(total_proteins)} proteins') + + with open(os.path.join(DATA_DIR, 'creeds', 'normalized', 'disease_expression.json'), 'w') as f: + json.dump(disease_expression_data, f, ensure_ascii=False, indent=2) + + +def get_geo_disease(): + DATA_FILE = os.path.join(DATA_DIR, 'geo', 'raw', 'DEgenes') + + expression = {} + + total_proteins = set() + unmapped_proteins = set() + + for file in tqdm(os.listdir(DATA_FILE)): + can_id = disease_map.get(f'doid:{file}') + + if not can_id: + continue + + if can_id not in expression: + expression[can_id] = {} + + df = pd.read_csv( + os.path.join(DATA_FILE, file, 'DEgenes.tsv'), + sep='\t', + usecols=[ + 'HGNC', + 'logFC' + ] + ) + + ncbigene_ids = [] + for val, hgnc_id in df.values: + total_proteins.add(hgnc_id) + ncbigene_id = protein_map.get(f'hgnc:{hgnc_id}') + if ncbigene_id: + ncbigene_ids.append(ncbigene_id) + else: + unmapped_proteins.add(hgnc_id) + ncbigene_ids.append(None) + df['ncbigene_id'] = ncbigene_ids + df.drop('HGNC', axis=1, inplace=True) + df.dropna(inplace=True) + df.set_index('ncbigene_id', inplace=True) + + expression[can_id] = df.to_dict()['logFC'] + + logger.info(f'Unmapped {len(unmapped_proteins)} proteins out of {len(total_proteins)} proteins') + + with open(os.path.join(DATA_DIR, 'geo', 'normalized', 'disease_expression.json'), 'w') as f: + json.dump(expression, f, ensure_ascii=False, indent=2) + + +def get_open_targets_disease(): + df = pd.read_csv( + os.path.join(DATA_DIR, 'open_targets', 'raw', 'data', 'open_targets.csv'), + usecols=[ + 'efo.term', + 'gene.symbol', + 'lfc', + ] + ) + + diseases = df['efo.term'].unique() + + # export cache to pickle each 15 iterations + CACHE_PATH = os.path.join(DATA_DIR, 'open_targets', 'disease_cache.pickle') + + if os.path.exists(CACHE_PATH): + with open(CACHE_PATH, 'rb') as file: + efo_to_mondo = pickle.load(file) + else: + efo_to_mondo = {} + + counter_cache = 0 + + for i in tqdm(diseases, desc='Normalizing diseases'): + counter_cache += 1 + + if i in efo_to_mondo: + continue + efo_to_mondo[i] = canonicalize_name(i, entity_class='pathology').curie + + # export cache to pickle and reset + if counter_cache == 15: + with open(CACHE_PATH, 'wb') as handle: + pickle.dump(efo_to_mondo, handle, protocol=pickle.HIGHEST_PROTOCOL) + + counter_cache = 0 + + expression = {} + + total_proteins = set() + unmapped_protein = set() + + for disease_name, gene, val in tqdm(df.values, desc='Exporting data'): + if disease_name in efo_to_mondo: + idx = efo_to_mondo[disease_name] + if idx not in expression: + expression[idx] = {} + + ncbigene_id = protein_map.get(f'hgnc:{gene}') + total_proteins.add(gene) + if ncbigene_id: + expression[idx][ncbigene_id] = val + else: + unmapped_protein.add(gene) + + logger.info(f'Extracted {len(expression)} diseases from Open Targets dataset') + logger.info(f'Unmapped {len(unmapped_protein)} protein out of {len(total_proteins)} proteins') + + with open(os.path.join(DATA_DIR, 'open_targets', 'normalized', 'disease_expression.json'), 'w') as f: + json.dump(expression, f, ensure_ascii=False, indent=2) + + +def main( + creed: bool = False, + geo: bool = False, + open_target: bool = False, +): + + if creed: + get_creed_disease() + + if geo: + get_geo_disease() + + if open_target: + get_open_targets_disease() + + +if __name__ == '__main__': + main() diff --git a/src/data_preprocess/gene_count.py b/src/data_preprocess/gene_count.py new file mode 100644 index 0000000..3d94e34 --- /dev/null +++ b/src/data_preprocess/gene_count.py @@ -0,0 +1,289 @@ +# -*- coding: utf-8 -*- + +""" + Gene level analysis +""" + +import os +import pandas as pd +import json +from tqdm import tqdm +import logging +import matplotlib.pyplot as plt + +logger = logging.getLogger(__name__) + +DATA_DIR = '../../data' + + +def get_kg_data(): + """Combine the data of both networks in json. """ + + openbio_kg = pd.read_csv( + os.path.join(DATA_DIR, 'kg', 'normalized', 'openbiolink_kg_normalized.tsv'), + sep='\t', + dtype=str, + usecols=['source', 'target'] + ) + + custom_kg = pd.read_csv( + os.path.join(DATA_DIR, 'kg', 'normalized', 'custom_kg_normalized.tsv'), + sep='\t', + dtype=str, + usecols=['source', 'target'] + ) + + data = { + 'openbio': { + 'chemicals': [], + 'diseases': [], + 'genes': [] + }, + 'custom': { + 'chemicals': [], + 'diseases': [], + 'genes': [] + }, + } + + for source, target in tqdm(openbio_kg.values, desc='Exporting data from OpenBioLink KG'): + if ('pubchem.compound:' in source) & ('ncbigene:' in target): + data['openbio']['chemicals'].append(source) + data['openbio']['genes'].append(target) + elif ('mondo:' in target) & ('ncbigene:' in source): + data['openbio']['diseases'].append(target) + data['openbio']['genes'].append(source) + else: + data['openbio']['genes'].append(source) + data['openbio']['genes'].append(target) + + for source, target in tqdm(custom_kg.values, desc='Exporting data from Custom KG'): + if ('pubchem.compound:' in source) & ('ncbigene:' in target): + data['custom']['chemicals'].append(source) + data['custom']['genes'].append(target) + elif ('mondo:' in target) & ('ncbigene:' in source): + data['custom']['diseases'].append(target) + data['custom']['genes'].append(source) + else: + data['custom']['genes'].append(source) + data['custom']['genes'].append(target) + + with open(os.path.join(DATA_DIR, 'kg', 'normalized', 'data.json'), 'w') as f: + json.dump(data, f, ensure_ascii=False, indent=2) + + +def gene_count( + data_file: str, + data_type: str, + data_info: dict +): + """Get statistics on gene data for different datasets. """ + with open(data_file) as f: + data_dict = json.load(f) + + count_dict = {} + + for i in data_dict: + if i in data_info[data_type]: + count_dict[i] = len(data_dict[i]) + + folder = data_file.split('/')[-2] + + counter = count_dict.values() + + logger.warning(f'Successfully found {len(counter)} from {len(data_dict)}') + logger.warning(f'Maximum gene count - {max(counter)}') + logger.warning(f'Minimum gene count - {min(counter)}') + logger.warning(f'Average gene count - {sum(counter) / len(counter)}') + + plt.hist(counter) + plt.title(f'Gene distribution for {folder}') + plt.savefig(f'{folder}-{data_type}.jpg') + plt.show() + + return None + + +def combined_data( + data_sets: dict, + data_info: dict +): + """Method to get all data from all the datasets and save the combined set. """ + + disease_genes = set() + chemical_genes = set() + diseases = set() + chemicals = set() + + for el_type in data_sets: + if el_type == 'diseases': + file_name = 'disease_expression.json' + else: + file_name = 'chemical_expression.json' + + for folder_name in data_sets[el_type]: + logger.warning(f'#####{folder_name}-{el_type}######') + with open(os.path.join(DATA_DIR, folder_name, 'normalized', file_name)) as f: + data_dict = json.load(f) + + for i in data_dict: + # Add chemicals and diseases in graph + if i in data_info[el_type]: + if el_type == 'diseases': + diseases.add(i) + else: + chemicals.add(i) + + # Add genes for those chemicals + for gene_idx in data_dict[i]: + if el_type == 'diseases': + disease_genes.add(gene_idx) + else: + chemical_genes.add(gene_idx) + + logger.warning(f'Total disease genes - {len(disease_genes)}') + logger.warning(f'Total chemical genes - {len(chemical_genes)}') + logger.warning(f'Total diseases - {len(diseases)}') + logger.warning(f'Total chemicals - {len(chemicals)}') + + if not os.path.exists(os.path.join(DATA_DIR, 'combined')): + os.makedirs(os.path.join(DATA_DIR, 'combined')) + + with open(os.path.join(DATA_DIR, 'combined', 'dataset_genes.json'), 'w') as file: + info = { + 'disease_gene': list(disease_genes), + 'chemical_gene': list(chemical_genes), + 'diseases': list(diseases), + 'chemicals': list(chemicals) + } + json.dump(info, file, ensure_ascii=False, indent=2) + + return None + + +def analyze_gene( + data_sets: dict, + data_info: dict +): + """Overview of gene with respect to network. """ + + with open(os.path.join(DATA_DIR, 'combined', 'dataset_genes.json')) as file: + info = json.load(file) + + for el_type in data_sets: + if el_type == 'diseases': + file_name = 'disease_expression.json' + else: + file_name = 'chemical_expression.json' + + for folder_name in data_sets[el_type]: + logger.warning(f'#####{folder_name}-{el_type}######') + d = 0 + c = 0 + u = 0 + + with open(os.path.join(DATA_DIR, folder_name, 'normalized', file_name)) as f: + data_dict = json.load(f) + total = len(data_dict) + for i in data_dict: + if i in data_info[el_type]: + if el_type == 'diseases': + if i in info['diseases']: + d += 1 + else: + u += 1 + + if el_type == 'chemicals': + if i in info['chemicals']: + c += 1 + else: + u += 1 + + logger.warning(f'Found {d} diseases, {c} chemicals and {u} unmapped out of {total}') + + +def get_target_data( + get_chemicals: bool, + get_diseases: bool, + custom_kg: bool, +): + """Get specific data from combined data based on network. """ + + with open(os.path.join(DATA_DIR, 'combined', 'dataset_genes.json'), 'r') as f: + data_dict = json.load(f) + + with open(os.path.join(DATA_DIR, 'kg', 'normalized', 'data.json'), 'r') as f: + if custom_kg: + kg_data = json.load(f)['custom'] + else: + kg_data = json.load(f)['openbio'] + + filtered_dict = { + 'chemicals': [], + 'diseases': [] + } + + if get_chemicals: + chemicals = data_dict['chemicals'] + + for chemical in tqdm(chemicals, desc='Filtering chemicals'): + if chemical in kg_data['chemicals']: + filtered_dict['chemicals'].append(chemical) + + if get_diseases: + chemicals = data_dict['diseases'] + + for disease in tqdm(chemicals, desc='Filtering diseases'): + if disease in kg_data['diseases']: + filtered_dict['diseases'].append(disease) + + if custom_kg: + g = 'customkg' + else: + g = 'openbiokg' + + with open(os.path.join(DATA_DIR, 'others', f'overlap_{g}.json'), 'w') as file: + json.dump(filtered_dict, file, ensure_ascii=False, indent=2) + + +def main( + do_gene_count: bool = False, + get_data_list: bool = False, + compare_with_network: bool = False +): + try: + with open(os.path.join(DATA_DIR, 'network', 'normalized', 'data.json')) as file: + data_info = json.load(file) + except FileNotFoundError: + raise FileNotFoundError("Please get get_kg_data function first!") + + data_sets = { + 'diseases': ['creeds', 'geo', 'open_targets'], + 'chemicals': ['creeds', 'l1000'] + } + + if get_data_list: + combined_data(data_sets=data_sets, data_info=data_info) + + if compare_with_network: + analyze_gene(data_sets=data_sets, data_info=data_info) + + if do_gene_count: + for el_type in data_sets: + if el_type == 'diseases': + file_name = 'disease_expression.json' + else: + file_name = 'chemical_expression.json' + + for folder_name in data_sets[el_type]: + logger.warning(f'#####{folder_name}-{el_type}######') + + gene_count( + data_file=os.path.join(DATA_DIR, folder_name, 'normalized', file_name), + data_type=el_type, + data_info=data_info + ) + + +if __name__ == '__main__': + get_target_data(get_chemicals=True, get_diseases=True, custom_kg=True) diff --git a/src/data_preprocess/resolver.py b/src/data_preprocess/resolver.py new file mode 100644 index 0000000..ffc4903 --- /dev/null +++ b/src/data_preprocess/resolver.py @@ -0,0 +1,81 @@ +# -*- coding: utf-8 -*- + +""" + Normalization of entities +""" + +import pandas as pd +from typing import Optional +from ratelimit import limits + +import requests + +CLASS_NAME_MAP = { + 'protein': 'ncbigene', + 'pathology': 'pathologies', + 'others': 'others', +} + +CLASS_XREF_MAP = { + 'protein': 'proteins', + 'pathology': 'pathologies', +} + + +def local_curies_to_names(class_name): + df = pd.read_csv( + f'../../data/xref/{CLASS_NAME_MAP[class_name]}.tsv', + sep='\t', + header=None + ) + if df.shape[1] == 3: + df.columns = ['curie', 'name_count', 'name'] + elif df.shape[1] == 2: + df.columns = ['curie', 'name'] + else: + raise ValueError('wrong number of columns') + + return dict(zip(df['curie'], df['name'])) + + +def get_local_xrefs(class_name): + df = pd.read_csv(f'../../data/xref/{CLASS_XREF_MAP[class_name]}.tsv', sep='\t', header=None) + df.columns = ['source', 'target'] + return dict(zip(df['source'], df['target'])) + + +# Get information from PubChem + +def _extract_cid_from_response(response: requests.Response) -> Optional[str]: + if response.status_code == 200: + response_json = response.json() + identifier_list = response_json.get("IdentifierList") + if identifier_list: + pubchem_compound_ids = identifier_list.get("CID", []) + if pubchem_compound_ids and len(pubchem_compound_ids) > 0: + # TODO add explanation of why there might be multiple and why its only taking the first + return pubchem_compound_ids[0] + return None + + +@limits(calls=5, period=1) +def _call_api_helper(url, method="GET", data=None) -> requests.Response: + if method == "GET": + return requests.get(url) + else: + return requests.post( + url, + headers={"Content-Type": "application/x-www-form-urlencoded"}, + data=data, + ) + + +@limits(calls=5, period=1) +def _call_api(url, method="GET", data=None) -> Optional[str]: + response = _call_api_helper(url=url, method=method, data=data) + return _extract_cid_from_response(response) + + +def get_cid_by_smiles(smiles: str) -> Optional[str]: + link = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/{smiles}/cids/JSON" + return _call_api(link) diff --git a/src/notebooks/1.0-plot_kg_distributions.ipynb b/src/notebooks/1.0-plot_kg_distributions.ipynb new file mode 100644 index 0000000..46264e5 --- /dev/null +++ b/src/notebooks/1.0-plot_kg_distributions.ipynb @@ -0,0 +1,455 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distributions of nodes and edges in Custom KG and OpenBioLink KG" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook plots the distributions of nodes and edges in the Custom KG and OpenBioLink KG." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", + "import sys\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns \n", + "from utils import KG_DATA_PATH, create_graph_from_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load networks" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "custom = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'custom_filtered_kg.tsv'), \n", + " sep='\\t'\n", + ")\n", + "\n", + "openbiolink = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'openbiolink_filtered_kg.tsv'),\n", + " sep='\\t'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Report on the number of relations: {1: 43578, -1: 8045}\n", + "Report on the number of relations: {-1: 12477, 1: 29022}\n" + ] + } + ], + "source": [ + "custom_graph = create_graph_from_df(custom)\n", + "openbiolink_graph = create_graph_from_df(openbiolink)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get nodes in Custom KG" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "custom_drugs = set()\n", + "custom_proteins = set()\n", + "custom_diseases = set()\n", + "\n", + "for node in custom_graph.nodes():\n", + " if node.startswith('pubchem.compound:'):\n", + " custom_drugs.add(node)\n", + " elif node.startswith('ncbigene:'):\n", + " custom_proteins.add(node)\n", + " elif node.startswith('mondo:'):\n", + " custom_diseases.add(node)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get edges in custom network " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "drug_protein_custom = 0\n", + "protein_protein_custom = 0\n", + "protein_disease_custom = 0\n", + "\n", + "for source, target in custom_graph.edges():\n", + " if 'pubchem.compound' in source and 'ncbigene' in target:\n", + " drug_protein_custom += 1\n", + " elif 'ncbigene' in source and 'ncbigene' in target:\n", + " protein_protein_custom += 1\n", + " elif 'ncbigene' in source and 'mondo' in target:\n", + " protein_disease_custom += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary stats" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "##### Number of nodes in Custom kg #####\n", + "8489: total # of nodes\n", + "218: # of unique drugs\n", + "8228: # of unique proteins\n", + "43: # of unique diseases\n", + "\n", + "##### Number of edges in Custom kg ######\n", + "51617: total # of interactions\n", + "743: # of drug-protein interactions\n", + "50651: # of protein-protein interactions\n", + "223: # of protein-disease interactions\n" + ] + } + ], + "source": [ + "print('##### Number of nodes in Custom kg #####')\n", + "print(f'{len(custom_graph.nodes())}: total # of nodes')\n", + "print(f'{len(custom_drugs)}: # of unique drugs')\n", + "print(f'{len(custom_proteins)}: # of unique proteins')\n", + "print(f'{len(custom_diseases)}: # of unique diseases')\n", + "\n", + "print('\\n##### Number of edges in Custom kg ######') \n", + "print(f'{len(custom_graph.edges())}: total # of interactions')\n", + "print(f'{drug_protein_custom}: # of drug-protein interactions')\n", + "print(f'{protein_protein_custom}: # of protein-protein interactions')\n", + "print(f'{protein_disease_custom}: # of protein-disease interactions')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get nodes in OpenBioLink KG" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "openbiolink_drugs = set()\n", + "openbiolink_proteins = set()\n", + "openbiolink_diseases = set()\n", + "\n", + "for node in openbiolink_graph.nodes():\n", + " if node.startswith('pubchem.compound:'):\n", + " openbiolink_drugs.add(node)\n", + " elif node.startswith('ncbigene:'):\n", + " openbiolink_proteins.add(node)\n", + " elif node.startswith('mondo:'):\n", + " openbiolink_diseases.add(node)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get edges in OpenBioLink network" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "drug_protein_openbio = 0\n", + "protein_protein_openbio = 0\n", + "protein_disease_openbio = 0\n", + "\n", + "for source, target in openbiolink_graph.edges():\n", + " if 'pubchem.compound' in source and 'ncbigene' in target:\n", + " drug_protein_openbio += 1\n", + " elif 'ncbigene' in source and 'ncbigene' in target:\n", + " protein_protein_openbio += 1\n", + " elif 'ncbigene' in source and 'mondo' in target:\n", + " protein_disease_openbio += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary stats" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "##### Number of nodes in OpenBioLink KG #####\n", + "4831: total # of nodes\n", + "20: # of unique diseases\n", + "209: # of unique drugs\n", + "4602: # of unique proteins \n", + "\n", + "##### Number of edges in OpenBioLink KG #####\n", + "41474: total # of edges\n", + "3399: # of drug-protein interactions\n", + "38019: # of protein-protein interactions\n", + "56: # of protein-disease interactions\n" + ] + } + ], + "source": [ + "print('##### Number of nodes in OpenBioLink KG #####')\n", + "print(f'{len(openbiolink_graph.nodes())}: total # of nodes')\n", + "print(f'{len(openbiolink_diseases)}: # of unique diseases')\n", + "print(f'{len(openbiolink_drugs)}: # of unique drugs')\n", + "print(f'{len(openbiolink_proteins)}: # of unique proteins \\n')\n", + "\n", + "print('##### Number of edges in OpenBioLink KG #####')\n", + "print(f'{len(openbiolink_graph.edges())}: total # of edges')\n", + "print(f'{drug_protein_openbio}: # of drug-protein interactions')\n", + "print(f'{protein_protein_openbio}: # of protein-protein interactions')\n", + "print(f'{protein_disease_openbio}: # of protein-disease interactions')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot distributions of nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data_nodes = {\n", + " 'Network': [\n", + " 'Custom KG',\n", + " 'OpenBioLink KG',\n", + " 'Custom KG',\n", + " 'OpenBioLink KG',\n", + " 'Custom KG',\n", + " 'OpenBioLink KG'\n", + " ],\n", + " 'Count':[\n", + " len(custom_drugs),\n", + " len(openbiolink_drugs), \n", + " len(custom_proteins), \n", + " len(openbiolink_proteins), \n", + " len(custom_diseases), \n", + " len(openbiolink_diseases)\n", + " ],\n", + " 'Node Types':['Drugs','Drugs','Genes/Proteins','Genes/Proteins','Diseases','Diseases']\n", + "}\n", + "\n", + "node_df = pd.DataFrame(data=data_nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data_edges = {\n", + " 'Network': [\n", + " 'Custom KG',\n", + " 'OpenBioLink KG',\n", + " 'Custom KG',\n", + " 'OpenBioLink KG',\n", + " 'Custom KG',\n", + " 'OpenBioLink KG'\n", + " ],\n", + " 'Count':[\n", + " drug_protein_custom,\n", + " drug_protein_openbio, \n", + " protein_protein_custom,\n", + " protein_protein_openbio,\n", + " protein_disease_custom, \n", + " protein_disease_openbio\n", + " ],\n", + " 'Edge Types':[\n", + " 'drug-protein',\n", + " 'drug-protein',\n", + " 'protein-protein',\n", + " 'protein-protein',\n", + " 'protein-disease',\n", + " 'protein-disease'\n", + " ]\n", + "}\n", + "\n", + "edges_df = pd.DataFrame(data=data_edges)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(rc={'figure.figsize':(15,5)})\n", + "f, axis = plt.subplots(1, 2, figsize=(15,5))\n", + "\n", + "# grid lines\n", + "sns.set_style(\"darkgrid\")\n", + "\n", + "bx = sns.barplot(x=\"Node Types\", y=\"Count\", hue='Network', data=node_df, palette=['#EE442F','#63ACBE'], ax=axis[0]) \n", + "bx = sns.barplot(x=\"Edge Types\", y=\"Count\", hue='Network', data=edges_df, palette=['#EE442F','#63ACBE'], ax=axis[1]) \n", + "\n", + "# title\n", + "axis[0].set_title(\n", + " 'Distribution of Node Types', \n", + " fontsize=15, \n", + " fontweight=\"bold\"\n", + ")\n", + "axis[1].set_title(\n", + " 'Distribution of Relationships', \n", + " fontsize=15, \n", + " fontweight=\"bold\"\n", + ")\n", + "\n", + "# axis labels\n", + "axis[0].set_xlabel(\"Node Type\", fontsize=14, fontweight=\"bold\")\n", + "axis[0].set_ylabel(\"Count\", fontsize=14, fontweight=\"bold\")\n", + "axis[1].set_xlabel(\"Edge Type\", fontsize=14, fontweight=\"bold\")\n", + "axis[1].set_ylabel(\"\") \n", + " \n", + "# axis ticks\n", + "axis[0].set_xticklabels(labels=['drug', 'protein', 'disease'])\n", + "axis[1].set_xticklabels(labels=['drug-protein','protein-protein','protein-disease'])\n", + "\n", + "# limit\n", + "axis[0].set_ylim(10, 15000)\n", + "axis[1].set_ylim(10, 80000)\n", + "\n", + "# Add counts above bars\n", + "for p in axis[0].patches:\n", + " _x = p.get_x() + p.get_width() / 2\n", + " _y = p.get_y() + p.get_height() + 50\n", + " value = int(p.get_height())\n", + " axis[0].text(_x, _y, value, ha=\"center\")\n", + " \n", + "\n", + "for p in axis[1].patches:\n", + " _x = p.get_x() + p.get_width() / 2\n", + " _y = p.get_y() + p.get_height() + 50\n", + " value = int(p.get_height())\n", + " axis[1].text(_x, _y, value, ha=\"center\") \n", + "\n", + "\n", + "plt.tight_layout()\n", + "axis[0].set_yscale(\"log\") #the log transformation\n", + "axis[1].set_yscale(\"log\") #the log transformation\n", + "\n", + "plt.savefig(\n", + " ('graph-stats.png'),\n", + " dpi=400,\n", + " bbox_inches='tight', \n", + ")\n", + "plt.show()\n", + " \n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/1.1-plot_permute_kg_distributions.ipynb b/src/notebooks/1.1-plot_permute_kg_distributions.ipynb new file mode 100644 index 0000000..3cd99ce --- /dev/null +++ b/src/notebooks/1.1-plot_permute_kg_distributions.ipynb @@ -0,0 +1,454 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Distributions of nodes and edges in permuted Custom KG and OpenBioLink KG" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook plots the distributions of nodes and edges in the permuted Custom KG and OpenBioLink KG." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns \n", + "\n", + "from utils import KG_DATA_PATH, create_graph_from_df, create_df_from_graph" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load networks" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "custom = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'shuffled_custom.tsv'), \n", + " sep='\\t'\n", + ")\n", + "custom.rename(columns={\"relation\": \"polarity\"}, inplace=True)\n", + "\n", + "openbiolink = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'shuffled_openbiolink.tsv'),\n", + " sep='\\t'\n", + ")\n", + "openbiolink.rename(columns={\"relation\": \"polarity\"}, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Report on the number of relations: {1: 43578, -1: 8045}\n", + "Report on the number of relations: {-1: 12477, 1: 29022}\n" + ] + } + ], + "source": [ + "custom_graph = create_graph_from_df(custom)\n", + "openbiolink_graph = create_graph_from_df(openbiolink)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get nodes in Custom KG" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "custom_drugs = set()\n", + "custom_proteins = set()\n", + "custom_diseases = set()\n", + "\n", + "for node in custom_graph.nodes():\n", + " if node.startswith('pubchem.compound:'):\n", + " custom_drugs.add(node)\n", + " elif node.startswith('ncbigene:'):\n", + " custom_proteins.add(node)\n", + " elif node.startswith('mondo:'):\n", + " custom_diseases.add(node)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get edges in custom network " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "drug_protein_custom = 0\n", + "protein_protein_custom = 0\n", + "protein_disease_custom = 0\n", + "\n", + "for source, target in custom_graph.edges():\n", + " if 'pubchem.compound' in source and 'ncbigene' in target:\n", + " drug_protein_custom += 1\n", + " elif 'ncbigene' in source and 'ncbigene' in target:\n", + " protein_protein_custom += 1\n", + " elif 'ncbigene' in source and 'mondo' in target:\n", + " protein_disease_custom += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary stats" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "##### Number of nodes in Custom kg #####\n", + "8470: total # of nodes\n", + "212: # of unique drugs\n", + "8213: # of unique proteins\n", + "43: # of unique diseases\n", + "\n", + "##### Number of edges in Custom kg ######\n", + "51611: total # of interactions\n", + "734: # of drug-protein interactions\n", + "50652: # of protein-protein interactions\n", + "220: # of protein-disease interactions\n" + ] + } + ], + "source": [ + "print('##### Number of nodes in Custom kg #####')\n", + "print(f'{len(custom_graph.nodes())}: total # of nodes')\n", + "print(f'{len(custom_drugs)}: # of unique drugs')\n", + "print(f'{len(custom_proteins)}: # of unique proteins')\n", + "print(f'{len(custom_diseases)}: # of unique diseases')\n", + "\n", + "print('\\n##### Number of edges in Custom kg ######') \n", + "print(f'{len(custom_graph.edges())}: total # of interactions')\n", + "print(f'{drug_protein_custom}: # of drug-protein interactions')\n", + "print(f'{protein_protein_custom}: # of protein-protein interactions')\n", + "print(f'{protein_disease_custom}: # of protein-disease interactions')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get nodes in OpenBioLink KG" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "openbiolink_drugs = set()\n", + "openbiolink_proteins = set()\n", + "openbiolink_diseases = set()\n", + "\n", + "for node in openbiolink_graph.nodes():\n", + " if node.startswith('pubchem.compound:'):\n", + " openbiolink_drugs.add(node)\n", + " elif node.startswith('ncbigene:'):\n", + " openbiolink_proteins.add(node)\n", + " elif node.startswith('mondo:'):\n", + " openbiolink_diseases.add(node)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Get edges in OpenBioLink network" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "drug_protein_openbio = 0\n", + "protein_protein_openbio = 0\n", + "protein_disease_openbio = 0\n", + "\n", + "for source, target in openbiolink_graph.edges():\n", + " if 'pubchem.compound' in source and 'ncbigene' in target:\n", + " drug_protein_openbio += 1\n", + " elif 'ncbigene' in source and 'ncbigene' in target:\n", + " protein_protein_openbio += 1\n", + " elif 'ncbigene' in source and 'mondo' in target:\n", + " protein_disease_openbio += 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary stats" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "##### Number of nodes in OpenBioLink KG #####\n", + "4862: total # of nodes\n", + "22: # of unique diseases\n", + "211: # of unique drugs\n", + "4627: # of unique proteins \n", + "\n", + "##### Number of edges in OpenBioLink KG #####\n", + "41496: total # of edges\n", + "3393: # of drug-protein interactions\n", + "38043: # of protein-protein interactions\n", + "50: # of protein-disease interactions\n" + ] + } + ], + "source": [ + "print('##### Number of nodes in OpenBioLink KG #####')\n", + "print(f'{len(openbiolink_graph.nodes())}: total # of nodes')\n", + "print(f'{len(openbiolink_diseases)}: # of unique diseases')\n", + "print(f'{len(openbiolink_drugs)}: # of unique drugs')\n", + "print(f'{len(openbiolink_proteins)}: # of unique proteins \\n')\n", + "\n", + "print('##### Number of edges in OpenBioLink KG #####')\n", + "print(f'{len(openbiolink_graph.edges())}: total # of edges')\n", + "print(f'{drug_protein_openbio}: # of drug-protein interactions')\n", + "print(f'{protein_protein_openbio}: # of protein-protein interactions')\n", + "print(f'{protein_disease_openbio}: # of protein-disease interactions')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot distributions of nodes" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "data_nodes = {\n", + " 'Network': [\n", + " 'Custom KG',\n", + " 'OpenBioLink KG',\n", + " 'Custom KG',\n", + " 'OpenBioLink KG',\n", + " 'Custom KG',\n", + " 'OpenBioLink KG'\n", + " ],\n", + " 'Count':[\n", + " len(custom_drugs),\n", + " len(openbiolink_drugs), \n", + " len(custom_proteins), \n", + " len(openbiolink_proteins), \n", + " len(custom_diseases), \n", + " len(openbiolink_diseases)\n", + " ],\n", + " 'Node Types':['Drugs','Drugs','Genes/Proteins','Genes/Proteins','Diseases','Diseases']\n", + "}\n", + "\n", + "node_df = pd.DataFrame(data=data_nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "data_edges = {\n", + " 'Network': [\n", + " 'Custom KG',\n", + " 'OpenBioLink KG',\n", + " 'Custom KG',\n", + " 'OpenBioLink KG',\n", + " 'Custom KG',\n", + " 'OpenBioLink KG'\n", + " ],\n", + " 'Count':[\n", + " drug_protein_custom,\n", + " drug_protein_openbio, \n", + " protein_protein_custom,\n", + " protein_protein_openbio,\n", + " protein_disease_custom, \n", + " protein_disease_openbio\n", + " ],\n", + " 'Edge Types':[\n", + " 'drug-protein',\n", + " 'drug-protein',\n", + " 'protein-protein',\n", + " 'protein-protein',\n", + " 'protein-disease',\n", + " 'protein-disease'\n", + " ]\n", + "}\n", + "\n", + "edges_df = pd.DataFrame(data=data_edges)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(rc={'figure.figsize':(15,5)})\n", + "f, axis = plt.subplots(1, 2, figsize=(15,5))\n", + "\n", + "# grid lines\n", + "sns.set_style(\"darkgrid\")\n", + "\n", + "bx = sns.barplot(x=\"Node Types\", y=\"Count\", hue='Network', data=node_df, palette=['#EE442F','#63ACBE'], ax=axis[0]) \n", + "bx = sns.barplot(x=\"Edge Types\", y=\"Count\", hue='Network', data=edges_df, palette=['#EE442F','#63ACBE'], ax=axis[1]) \n", + "\n", + "# title\n", + "axis[0].set_title(\n", + " 'Distribution of Node Types', \n", + " fontsize=15, \n", + " fontweight=\"bold\"\n", + ")\n", + "axis[1].set_title(\n", + " 'Distribution of Relationships', \n", + " fontsize=15, \n", + " fontweight=\"bold\"\n", + ")\n", + "\n", + "# axis labels\n", + "axis[0].set_xlabel(\"Node Type\", fontsize=14, fontweight=\"bold\")\n", + "axis[0].set_ylabel(\"Count\", fontsize=14, fontweight=\"bold\")\n", + "axis[1].set_xlabel(\"Edge Type\", fontsize=14, fontweight=\"bold\")\n", + "axis[1].set_ylabel(\"\") \n", + " \n", + "# axis ticks\n", + "axis[0].set_xticklabels(labels=['drug', 'protein', 'disease'])\n", + "axis[1].set_xticklabels(labels=['drug-protein','protein-protein','protein-disease'])\n", + "\n", + "# limit\n", + "axis[0].set_ylim(10, 15000)\n", + "axis[1].set_ylim(10, 80000)\n", + "\n", + "# Add counts above bars\n", + "for p in axis[0].patches:\n", + " _x = p.get_x() + p.get_width() / 2\n", + " _y = p.get_y() + p.get_height() + 50\n", + " value = int(p.get_height())\n", + " axis[0].text(_x, _y, value, ha=\"center\")\n", + " \n", + "\n", + "for p in axis[1].patches:\n", + " _x = p.get_x() + p.get_width() / 2\n", + " _y = p.get_y() + p.get_height() + 50\n", + " value = int(p.get_height())\n", + " axis[1].text(_x, _y, value, ha=\"center\") \n", + "\n", + "\n", + "plt.tight_layout()\n", + "axis[0].set_yscale(\"log\") #the log transformation\n", + "axis[1].set_yscale(\"log\") #the log transformation\n", + "\n", + "plt.savefig(\n", + " ('graph-permuted-stats.png'),\n", + " dpi=400,\n", + " bbox_inches='tight', \n", + ")\n", + "plt.show()\n", + " \n", + " " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/src/notebooks/1.2-dataset_statistics.ipynb b/src/notebooks/1.2-dataset_statistics.ipynb new file mode 100644 index 0000000..203e5f5 --- /dev/null +++ b/src/notebooks/1.2-dataset_statistics.ipynb @@ -0,0 +1,236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cultural-factory", + "metadata": {}, + "source": [ + "# Dataset statistics\n", + "\n", + "This notebook includes the statistics analysis for each dataset" + ] + }, + { + "cell_type": "markdown", + "id": "academic-cleaner", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "revolutionary-routine", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import pandas as pd\n", + "\n", + "from utils import DATA_DIR" + ] + }, + { + "cell_type": "markdown", + "id": "assisted-music", + "metadata": {}, + "source": [ + "# Load datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "banner-accounting", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'creed_harmonized_expression.json')) as f:\n", + " creed = json.load(f)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'geo_harmonized_expression.json')) as f:\n", + " geo = json.load(f)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'l1000_harmonized_expression.json')) as f:\n", + " l1000 = json.load(f)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'target_harmonized_expression.json')) as f:\n", + " target = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "sticky-essence", + "metadata": {}, + "outputs": [], + "source": [ + "MAP = {\n", + " 'creed': creed,\n", + " 'target': target,\n", + " 'geo': geo,\n", + " 'l1000': l1000\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "inclusive-compromise", + "metadata": {}, + "source": [ + "# Statistical analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "mobile-appendix", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(columns=[\n", + " 'dataset',\n", + " 'count', \n", + " 'mean_gene_count', \n", + " 'minimum_gene_count',\n", + " 'maximum_gene_count'\n", + "])\n", + "\n", + "for dataset_name, data in MAP.items():\n", + " \n", + " data_count = len(data)\n", + " \n", + " gene_list = []\n", + " \n", + " for el in data:\n", + " gene_list.append(len(data[el]))\n", + " \n", + " max_count = max(gene_list)\n", + " min_count = min(gene_list)\n", + " avg_count = sum(gene_list) / len(gene_list)\n", + " \n", + " tmp = pd.DataFrame(\n", + " {'dataset': dataset_name,\n", + " 'count': data_count, \n", + " 'mean_gene_count': avg_count, \n", + " 'minimum_gene_count': min_count,\n", + " 'maximum_gene_count': max_count},\n", + " index=[1,]\n", + " )\n", + " \n", + " df = pd.concat([df, tmp], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "expressed-optics", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " dataset count mean_gene_count minimum_gene_count maximum_gene_count\n", + "0 creed 39 556.974359 539 570\n", + "1 target 44 958.568182 1 3072\n", + "2 geo 18 6569.555556 26 18123\n", + "3 l1000 269 342.104089 3 2753" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/2.0-dataset_probabilities.ipynb b/src/notebooks/2.0-dataset_probabilities.ipynb new file mode 100644 index 0000000..686197a --- /dev/null +++ b/src/notebooks/2.0-dataset_probabilities.ipynb @@ -0,0 +1,449 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "historical-railway", + "metadata": {}, + "source": [ + "# Dataset probabilities\n", + "\n", + "This notebook involves anlaysing the clinical trial information for chemical-disease pair in for a given dataset to get the probability by chance." + ] + }, + { + "cell_type": "markdown", + "id": "satellite-cross", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "elect-repair", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import logging\n", + "import json\n", + "from collections import Counter\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "from itertools import product\n", + "\n", + "from networkx import DiGraph\n", + "from networkx.algorithms.shortest_paths.generic import has_path\n", + "\n", + "from utils import KG_DATA_PATH, DATA_DIR, create_graph_from_df" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dated-diversity", + "metadata": {}, + "outputs": [], + "source": [ + "logger = logging.getLogger(__name__)" + ] + }, + { + "cell_type": "markdown", + "id": "circular-ceremony", + "metadata": {}, + "source": [ + "# Load KG" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "standard-orange", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Report on the number of relations: {-1: 12477, 1: 29022}\n", + "Report on the number of relations: {1: 43578, -1: 8045}\n" + ] + } + ], + "source": [ + "openbiolinks_df = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'openbiolink_filtered_kg.tsv'),\n", + " sep='\\t'\n", + ")\n", + "\n", + "custom_df = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'custom_filtered_kg.tsv'),\n", + " sep='\\t'\n", + ")\n", + "\n", + "openbio_graph = create_graph_from_df(openbiolinks_df)\n", + "custom_graph = create_graph_from_df(custom_df)" + ] + }, + { + "cell_type": "markdown", + "id": "tested-emerald", + "metadata": {}, + "source": [ + "# Loading clinical trial pairs" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "sharp-japan", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(DATA_DIR, 'gold-standard', 'clinical-trial.json'), 'r') as f:\n", + " clinical_filtered = json.load(f)" + ] + }, + { + "cell_type": "markdown", + "id": "intermediate-cooling", + "metadata": {}, + "source": [ + "# GEO, LC1000, OpenTargets and Creed" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "regulation-charm", + "metadata": {}, + "outputs": [], + "source": [ + "# Load gene expression files\n", + "\n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'creed_harmonized_expression.json')) as file:\n", + " creed_chemicals = json.load(file).keys()\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'geo_harmonized_expression.json')) as file2:\n", + " geo_diseases = json.load(file2).keys()\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'l1000_harmonized_expression.json')) as file3:\n", + " lc1000_chemicals = json.load(file3).keys()\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'target_harmonized_expression.json')) as file4:\n", + " open_target_diseases = json.load(file4).keys()\n" + ] + }, + { + "cell_type": "markdown", + "id": "mineral-mercury", + "metadata": {}, + "source": [ + "# Get overlaps with subgraph" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "equivalent-snake", + "metadata": {}, + "outputs": [], + "source": [ + "MAP = {\n", + " 'creed' : creed_chemicals,\n", + " 'target': open_target_diseases,\n", + " 'geo': geo_diseases,\n", + " 'lc1000': lc1000_chemicals,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "premium-timothy", + "metadata": {}, + "outputs": [], + "source": [ + "def get_overlap_stats(graph_dict: dict):\n", + " \n", + " openbio_graph = graph_dict['openbio']\n", + " \n", + " openlink_kg_chemical = set()\n", + " openlink_kg_disease = set()\n", + "\n", + " for node in openbio_graph.nodes():\n", + " if 'pubchem' in node:\n", + " openlink_kg_chemical.add(node)\n", + "\n", + " elif 'mondo' in node:\n", + " openlink_kg_disease.add(node)\n", + "\n", + " custom_graph = graph_dict['custom']\n", + " \n", + " custom_kg_chemical = set()\n", + " custom_kg_disease = set()\n", + "\n", + " for node in custom_graph.nodes():\n", + " if 'pubchem' in node:\n", + " custom_kg_chemical.add(node)\n", + "\n", + " elif 'mondo' in node:\n", + " custom_kg_disease.add(node)\n", + " \n", + " # Get data present on in the KG \n", + " info_dict = {}\n", + "\n", + " for i in MAP:\n", + " curies = set(MAP[i])\n", + "\n", + " print(f'\\n#### {i} ####')\n", + " disease1 = openlink_kg_disease.intersection(curies)\n", + " disease2 = custom_kg_disease.intersection(curies)\n", + "\n", + " chemical1 = openlink_kg_chemical.intersection(curies)\n", + " chemical2 = custom_kg_chemical.intersection(curies)\n", + "\n", + " if i not in info_dict:\n", + " info_dict[i] = {}\n", + "\n", + " if len(disease1) > 0:\n", + " info_dict[i]['openbiolinks'] = disease1\n", + " info_dict[i]['custom'] = disease2\n", + " elif len(chemical1) > 0:\n", + " info_dict[i]['openbiolinks'] = chemical1\n", + " info_dict[i]['custom'] = chemical2\n", + " else:\n", + " print('SOMETHING IS WRONG!!!')\n", + "\n", + " print(f'Overlaps with OpenBioLink KG chemical', len(chemical1))\n", + " print(f'Overlaps with Custom KG chemical', len(chemical2))\n", + " print(f'Overlaps with OpenBioLink KG disease', len(disease1))\n", + " print(f'Overlaps with Custom KG disease', len(disease2))\n", + " return info_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "liable-policy", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "#### creed ####\n", + "Overlaps with OpenBioLink KG chemical 31\n", + "Overlaps with Custom KG chemical 30\n", + "Overlaps with OpenBioLink KG disease 0\n", + "Overlaps with Custom KG disease 0\n", + "\n", + "#### target ####\n", + "Overlaps with OpenBioLink KG chemical 0\n", + "Overlaps with Custom KG chemical 0\n", + "Overlaps with OpenBioLink KG disease 18\n", + "Overlaps with Custom KG disease 39\n", + "\n", + "#### geo ####\n", + "Overlaps with OpenBioLink KG chemical 0\n", + "Overlaps with Custom KG chemical 0\n", + "Overlaps with OpenBioLink KG disease 10\n", + "Overlaps with Custom KG disease 17\n", + "\n", + "#### lc1000 ####\n", + "Overlaps with OpenBioLink KG chemical 189\n", + "Overlaps with Custom KG chemical 198\n", + "Overlaps with OpenBioLink KG disease 0\n", + "Overlaps with Custom KG disease 0\n" + ] + } + ], + "source": [ + "info_graph = get_overlap_stats(graph_dict={'openbio': openbio_graph, 'custom': custom_graph})" + ] + }, + { + "cell_type": "markdown", + "id": "healthy-juvenile", + "metadata": {}, + "source": [ + "# Get clinical pair percentage based on dataset and gold standard data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "convinced-belfast", + "metadata": {}, + "outputs": [], + "source": [ + "def get_gold_standard_stats(\n", + " gold_standard_dict: dict, \n", + " graph_dict: dict, \n", + " info_dict: dict\n", + "):\n", + " \n", + " clinical_evidence = {'openbiolinks': {}, 'custom': {}}\n", + " count_dict = {'openbiolinks': {}, 'custom': {}}\n", + " total_count_dict = {'openbiolinks': {}, 'custom': {}}\n", + " \n", + " openbio_graph = graph_dict['openbio']\n", + " custom_graph = graph_dict['custom']\n", + "\n", + " for c, d in product(['creed', 'lc1000'], ['target', 'geo']):\n", + " c_set = info_dict[c]\n", + " d_set = info_dict[d]\n", + " \n", + " # For OpenBioLinks\n", + " tp_openbio = 0\n", + " \n", + " for chem_idx, disease_idx in product(c_set['openbiolinks'], d_set['openbiolinks']):\n", + " name = chem_idx + '_' + disease_idx\n", + " if name in gold_standard_dict:\n", + " clinical_evidence['openbiolinks'][name] = 'yes'\n", + " tp_openbio += 1\n", + "\n", + " key_name = c + '_' + d\n", + " \n", + " # No.of clinical trial pairs\n", + " count_dict['openbiolinks'][key_name] = tp_openbio \n", + " \n", + " # Total no.of pairs\n", + " total_count_dict['openbiolinks'][key_name] = len(c_set['openbiolinks']) * len(d_set['openbiolinks'])\n", + " \n", + " # For Custom Network\n", + " tp_custom = 0\n", + "\n", + " for chem_idx, disease_idx in product(c_set['custom'], d_set['custom']):\n", + " name = chem_idx + '_' + disease_idx\n", + " if name in gold_standard_dict:\n", + " clinical_evidence['custom'][name] = 'yes'\n", + " tp_custom += 1\n", + "\n", + " key_name = c + '_' + d\n", + " \n", + " # No.of clinical trial pairs\n", + " count_dict['custom'][key_name] = tp_custom\n", + " \n", + " # Total no.of pairs\n", + " total_count_dict['custom'][key_name] = len(c_set['custom']) * len(d_set['custom'])\n", + " \n", + " # Display the value by change df\n", + " data_df = pd.DataFrame(\n", + " columns=['creed_target', 'creed_geo', 'lc1000_target', 'lc1000_geo'],\n", + " index=['openbiolinks', 'custom']\n", + " ) \n", + " \n", + " for kg_name in total_count_dict:\n", + " for el in total_count_dict[kg_name]: \n", + " val = count_dict[kg_name][el] / total_count_dict[kg_name][el]\n", + " data_df.loc[kg_name, el] = val*100\n", + " \n", + " return data_df" + ] + }, + { + "cell_type": "markdown", + "id": "lightweight-mambo", + "metadata": {}, + "source": [ + "# Clinical data - Value by chance" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "excellent-magnitude", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " source target polarity\n", + "0 ncbigene:29101 ncbigene:36 -1\n", + "1 ncbigene:64108 ncbigene:201456 1\n", + "2 ncbigene:6907 ncbigene:79019 1\n", + "3 ncbigene:5905 ncbigene:55055 1\n", + "4 ncbigene:54821 ncbigene:3295 1\n", + "... ... ... ...\n", + "41494 ncbigene:2966 ncbigene:5519 1\n", + "41495 pubchem.compound:5280453 ncbigene:2194 1\n", + "41496 ncbigene:1027 ncbigene:1647 1\n", + "41497 pubchem.compound:24826799 ncbigene:219952 1\n", + "41498 ncbigene:2033 ncbigene:9306 1\n", + "\n", + "[41499 rows x 3 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "permuted_custom = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'shuffled_custom.tsv'), \n", + " sep='\\t'\n", + ")\n", + "permuted_custom.rename(columns={\"relation\": \"polarity\"}, inplace=True)\n", + "\n", + "\n", + "permuted_openbiolink = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'shuffled_openbiolink.tsv'), \n", + " sep='\\t'\n", + ")\n", + "permuted_openbiolink.rename(columns={\"relation\": \"polarity\"}, inplace=True)\n", + "permuted_openbiolink" + ] + }, + { + "cell_type": "markdown", + "id": "strange-career", + "metadata": {}, + "source": [ + "# Create graph" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "spoken-cambodia", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Report on the number of relations: {-1: 12477, 1: 29022}\n", + "Report on the number of relations: {1: 43578, -1: 8045}\n", + "Report on the number of relations: {-1: 12477, 1: 29022}\n", + "Report on the number of relations: {1: 43578, -1: 8045}\n" + ] + } + ], + "source": [ + "graph_openbio = create_graph_from_df(openbiolink)\n", + "graph_custom = create_graph_from_df(custom)\n", + "\n", + "graph_permuted_openbio = create_graph_from_df(permuted_openbiolink)\n", + "graph_permuted_custom = create_graph_from_df(permuted_custom)" + ] + }, + { + "cell_type": "markdown", + "id": "brown-sunglasses", + "metadata": {}, + "source": [ + "# Graph stats" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "unique-january", + "metadata": {}, + "outputs": [], + "source": [ + "data = {\n", + " 'raw': {},\n", + " 'permuted': {}\n", + "}\n", + "\n", + "for i in ['permuted', 'raw']:\n", + " if i == 'raw':\n", + " openbio_g = graph_openbio\n", + " custom_g = graph_custom\n", + " openbio_df = openbiolink\n", + " custom_df = custom\n", + " else:\n", + " openbio_g = graph_permuted_openbio\n", + " custom_g = graph_permuted_custom\n", + " openbio_df = permuted_openbiolink\n", + " custom_df = permuted_custom\n", + " \n", + " o_nodes = list(openbio_g.nodes())\n", + " \n", + " o_degree = list(openbio_g.degree(o_nodes))\n", + " o_degree = [el for node, el in o_degree]\n", + " \n", + " c_nodes = list(custom_g.nodes())\n", + " \n", + " c_degree = list(custom_g.degree(c_nodes))\n", + " c_degree = [el for node, el in c_degree]\n", + " \n", + " \n", + " data[i]['openbio_nodes'] = str(len(o_nodes))\n", + " data[i]['custom_nodes'] = str(len(c_nodes))\n", + " \n", + " data[i]['openbio_degree'] = str(round(sum(o_degree) / len(o_degree), 3))\n", + " data[i]['custom_degree'] = str(round(sum(c_degree) / len(c_degree), 3))\n", + " \n", + " o_edges = len(openbio_g.edges())\n", + " c_edges = len(custom_g.edges())\n", + " \n", + " data[i]['openbio_edges'] = str(o_edges)\n", + " data[i]['custom_edges'] = str(c_edges)\n", + " \n", + " data[i]['openbio_activatory'] = len(openbio_df.index[openbio_df['polarity'] == 1])\n", + " data[i]['custom_activatory'] = len(custom_df.index[custom_df['polarity'] == 1])\n", + " \n", + " data[i]['openbio_inhibitory'] = len(openbio_df.index[openbio_df['polarity'] == -1])\n", + " data[i]['custom_inhibitory'] = len(custom_df.index[custom_df['polarity'] == -1])\n", + " \n", + " data[i]['openbio_longest_shortest_path'] = get_longest_shortest_path(openbio_df, openbio_g)\n", + " data[i]['custom_longest_shortest_path'] = get_longest_shortest_path(custom_df, custom_g) " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "4077a595", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " raw permuted\n", + "openbio_nodes 4831 4862\n", + "custom_nodes 8489 8470\n", + "openbio_degree 17.17 17.07\n", + "custom_degree 12.161 12.187\n", + "openbio_edges 41474 41496\n", + "custom_edges 51617 51611\n", + "openbio_activatory 29022 29022\n", + "custom_activatory 43578 43578\n", + "openbio_inhibitory 12477 12477\n", + "custom_inhibitory 8045 8045\n", + "openbio_longest_shortest_path 11 8\n", + "custom_longest_shortest_path 11 9" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(data)\n", + "df" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/4.0-subgraph_full_analysis.ipynb b/src/notebooks/4.0-subgraph_full_analysis.ipynb new file mode 100644 index 0000000..caa82f6 --- /dev/null +++ b/src/notebooks/4.0-subgraph_full_analysis.ipynb @@ -0,0 +1,416 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "environmental-guarantee", + "metadata": {}, + "source": [ + "# Subgraph Full Analysis\n", + "\n", + "This notebook conatins selection of concordant paths based on both drug and disease transcription data of all chemical-disease pairs in our subgraphs" + ] + }, + { + "cell_type": "markdown", + "id": "terminal-enlargement", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "documentary-privacy", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import json\n", + "import logging\n", + "from tqdm import tqdm\n", + "from itertools import product\n", + "from networkx import DiGraph\n", + "\n", + "from utils import (get_paths, filter_dataset, get_validated_paths, create_graph_from_df,\n", + " get_path_count, DATA_DIR, KG_DATA_PATH)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "stuck-delicious", + "metadata": {}, + "outputs": [], + "source": [ + "logger = logging.getLogger(__name__)\n", + "logging.getLogger('drug2ways').setLevel(logging.CRITICAL)\n", + "logging.basicConfig(level=logging.CRITICAL)" + ] + }, + { + "cell_type": "markdown", + "id": "sonic-animal", + "metadata": {}, + "source": [ + "# Load dataset-generated network dataframe - Unfiltered" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "widespread-insured", + "metadata": {}, + "outputs": [], + "source": [ + "openbiolink_df = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'openbiolink_filtered_kg.tsv'),\n", + " sep='\\t'\n", + ")\n", + "\n", + "custom_df = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'custom_filtered_kg.tsv'),\n", + " sep='\\t'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "familiar-north", + "metadata": {}, + "source": [ + "# Load datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "verified-electric", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'creed_harmonized_expression.json')) as file:\n", + " creed_dict = json.load(file)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'geo_harmonized_expression.json')) as file2:\n", + " geo_dict = json.load(file2)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'l1000_harmonized_expression.json')) as file3:\n", + " l1000_dict = json.load(file3)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'transcriptomics', 'target_harmonized_expression.json')) as file4:\n", + " open_target_dict = json.load(file4)" + ] + }, + { + "cell_type": "markdown", + "id": "twelve-diesel", + "metadata": {}, + "source": [ + "# Filterting dataset based on network" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "conditional-object", + "metadata": {}, + "outputs": [], + "source": [ + "# CREED\n", + "creed_openbio = filter_dataset(dataset=creed_dict, graph_df=openbiolink_df)\n", + "creed_custom = filter_dataset(dataset=creed_dict, graph_df=custom_df)\n", + "\n", + "creed_dict = {'openbio': creed_openbio, 'custom': creed_custom}\n", + "\n", + "# GEO\n", + "geo_openbio = filter_dataset(dataset=geo_dict, graph_df=openbiolink_df)\n", + "geo_custom = filter_dataset(dataset=geo_dict, graph_df=custom_df)\n", + "\n", + "geo_dict = {'openbio': geo_openbio, 'custom': geo_custom}\n", + "\n", + "# OpenTarget\n", + "target_openbio = filter_dataset(dataset=open_target_dict, graph_df=openbiolink_df)\n", + "target_custom = filter_dataset(dataset=open_target_dict, graph_df=custom_df)\n", + "\n", + "open_target_dict = {'openbio': target_openbio, 'custom': target_custom}\n", + "\n", + "# L1000\n", + "l1000_openbio = filter_dataset(dataset=l1000_dict, graph_df=openbiolink_df)\n", + "l1000_custom = filter_dataset(dataset=l1000_dict, graph_df=custom_df)\n", + "\n", + "l1000_dict = {'openbio': l1000_openbio, 'custom': l1000_custom}\n" + ] + }, + { + "cell_type": "markdown", + "id": "broke-somalia", + "metadata": {}, + "source": [ + "# Load clinical and drug-indication data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "settled-sustainability", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(DATA_DIR, 'gold-standard', 'clinical-trial.json')) as file:\n", + " clinical_dict = json.load(file).keys()" + ] + }, + { + "cell_type": "markdown", + "id": "environmental-gross", + "metadata": {}, + "source": [ + "# Creating information dict for each chemical-disease pair" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "atmospheric-compiler", + "metadata": {}, + "outputs": [], + "source": [ + "MAP = {\n", + " 'creed' : creed_dict,\n", + " 'target': open_target_dict,\n", + " 'geo': geo_dict,\n", + " 'l1000': l1000_dict,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "precious-norway", + "metadata": {}, + "outputs": [], + "source": [ + "def create_graph(graph_df):\n", + " graph = DiGraph()\n", + "\n", + " for sub_name, obj_name, relation in graph_df.values:\n", + " # Store edge in the graph\n", + " graph.add_edge(\n", + " sub_name,\n", + " obj_name,\n", + " polarity=relation,\n", + " )\n", + " return graph" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "marked-tower", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creed_target\n", + "### creed-target ###\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Getting paths: 100%|██████████| 620/620 [01:15<00:00, 8.17it/s]\n", + "Calculating concordance: 100%|██████████| 5/5 [00:01<00:00, 2.90it/s]\n", + "Getting paths: 100%|██████████| 1170/1170 [04:43<00:00, 4.12it/s]\n", + "Calculating concordance: 100%|██████████| 5/5 [00:10<00:00, 2.12s/it]\n", + "Getting paths: 0%| | 0/310 [00:00 0:\n", + "\n", + " # Just get the nodes from the path without relations\n", + " tmp_paths = []\n", + "\n", + " for v, l in p['paths'].items():\n", + " pth = []\n", + " for k in l:\n", + " if k in ['-|', '->']:\n", + " continue\n", + " else:\n", + " pth.append(k)\n", + " tmp_paths.append(pth)\n", + "\n", + " chemical = p['source']\n", + " disease = p['target']\n", + "\n", + "\n", + " results = get_validated_paths(\n", + " directed_graph=kg,\n", + " source=chemical,\n", + " target=disease,\n", + " all_paths=tmp_paths,\n", + " drug_dict=MAP[c][gname][chemical],\n", + " disease_dict=MAP[d][gname][disease],\n", + " clinical_pair_dict=clinical_dict,\n", + " )\n", + "\n", + " if results['number_of_concordant_paths'] != 0:\n", + " new_results = {\n", + " 'source': results['source'],\n", + " 'target': results['target'],\n", + " 'number_of_paths': results['number_of_paths'],\n", + " 'number_of_concordant_paths': results['number_of_concordant_paths'],\n", + " 'in_clinical_trial': results['in_clinical_trial'],\n", + " 'number_of_concordant_activatory_paths': results['number_of_concordant_activatory_paths'],\n", + " 'number_of_concordant_inhibitory_paths': results['number_of_concordant_inhibitory_paths'],\n", + " 'subgraph_size': results['subgraph_size'],\n", + " 'number_of_unique_nodes': results['number_of_unique_nodes'],\n", + " 'lmax': lmax,\n", + " 'subgraph_name': gname,\n", + " }\n", + "\n", + " tmp_df = pd.DataFrame(new_results, index=[0])\n", + " df = pd.concat(\n", + " [df, tmp_df],\n", + " ignore_index=True\n", + " )\n", + "\n", + " n_file_path = os.path.join(DATA_DIR, 'concordant_paths', f'{graph_name}-results.tsv')\n", + " df.to_csv(n_file_path, sep='\\t', index=False) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "established-highway", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/4.1-analysis_of_pairs.ipynb b/src/notebooks/4.1-analysis_of_pairs.ipynb new file mode 100644 index 0000000..f804f8f --- /dev/null +++ b/src/notebooks/4.1-analysis_of_pairs.ipynb @@ -0,0 +1,511 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "mature-memory", + "metadata": {}, + "source": [ + "# Analysis of pairs\n", + "\n", + "This notebook summarizesz the statistical analysis of the clinical trial and KG data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "appointed-postcard", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import pandas as pd\n", + "\n", + "from collections import Counter, defaultdict\n", + "from networkx import DiGraph, connected_components\n", + "\n", + "from utils import DATA_DIR" + ] + }, + { + "cell_type": "markdown", + "id": "coordinated-provider", + "metadata": {}, + "source": [ + "# Filtering functions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "raising-arbor", + "metadata": {}, + "outputs": [], + "source": [ + "def filter_by_lmax(\n", + " df: pd.DataFrame,\n", + " by_lmax: list\n", + "):\n", + " \"\"\"Filter dataframe by speicifc lmax. \"\"\"\n", + " tmp_df = df.copy(deep=True)\n", + " \n", + " new_df = pd.DataFrame(columns=tmp_df.columns)\n", + " \n", + " for l in by_lmax:\n", + " fil_df = tmp_df.loc[tmp_df['lmax'] == f'lmax_{l}']\n", + " new_df = pd.concat([new_df, fil_df], ignore_index=True)\n", + " \n", + " return new_df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "attractive-excitement", + "metadata": {}, + "outputs": [], + "source": [ + "def filter_by_path_occurence(\n", + " df: pd.DataFrame,\n", + " filter_num: int\n", + "):\n", + " \"\"\"Filter dataframe based on number of times the pair appears in. \"\"\"\n", + " tmp_df = df.copy(deep=True)\n", + "\n", + " tmp_df = tmp_df[['pairs', 'subgraph_name', 'lmax']]\n", + "\n", + " # Collect data for each KG individually\n", + " custom_data = tmp_df.loc[tmp_df['subgraph_name'] == 'custom'].drop(\n", + " 'subgraph_name',\n", + " axis=1,\n", + " )\n", + "\n", + " custom_pairs = {}\n", + "\n", + " for pair, lmax in custom_data.values:\n", + " if pair not in custom_pairs:\n", + " custom_pairs[pair] = set()\n", + " custom_pairs[pair].add(lmax)\n", + "\n", + " opentarget_data = tmp_df.loc[tmp_df['subgraph_name'] == 'openbio'].drop(\n", + " 'subgraph_name',\n", + " axis=1\n", + " )\n", + "\n", + " open_pairs = {}\n", + "\n", + " for pair, lmax in opentarget_data.values:\n", + " if pair not in open_pairs:\n", + " open_pairs[pair] = set()\n", + " open_pairs[pair].add(lmax)\n", + "\n", + " # Filter data in DF\n", + " index_list = []\n", + "\n", + " for key, val in custom_pairs.items():\n", + " if len(val) > filter_num:\n", + " df1 = tmp_df.loc[(tmp_df['pairs'] == key) & (tmp_df['subgraph_name'] == 'custom')]\n", + " index_list.extend(df1.index.tolist())\n", + "\n", + " for key, val in open_pairs.items():\n", + " if len(val) > filter_num:\n", + " df1 = tmp_df.loc[(tmp_df['pairs'] == key) & (tmp_df['subgraph_name'] == 'openbio')]\n", + " index_list.extend(df1.index.tolist())\n", + "\n", + " index_to_keep = sorted(list(set(index_list)))\n", + "\n", + " new_df = df[df.index.isin(index_to_keep)]\n", + "\n", + " print(f'Reduced dataframe from {df.shape} to {new_df.shape}')\n", + " return new_df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "uniform-contemporary", + "metadata": {}, + "outputs": [], + "source": [ + "def filter_by_node_count(\n", + " df: pd.DataFrame,\n", + " min_count: int,\n", + " max_count: int = 0\n", + "):\n", + " \"\"\"Filter dataframe according to the number of unique nodes between drug-disease pair. \"\"\"\n", + " tmp_df = df.copy(deep=True)\n", + " \n", + " if max_count == 0:\n", + " return tmp_df.loc[tmp_df['number_of_unique_nodes'] > min_count]\n", + " else:\n", + " return tmp_df.loc[tmp_df['number_of_unique_nodes'].between(min_count, max_count)]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "reasonable-hughes", + "metadata": {}, + "outputs": [], + "source": [ + "def filter_by_concordant_path(\n", + " df: pd.DataFrame,\n", + " min_count: int,\n", + " max_count: int = 0\n", + "):\n", + " \"\"\"Filtere dataframe based on number of concordant_paths between drug-disease pair. \"\"\"\n", + " tmp_df = df.copy(deep=True)\n", + "\n", + " if max_count == 0:\n", + " return tmp_df.loc[tmp_df['number_of_concordant_paths'] > min_count]\n", + " else:\n", + " return tmp_df.loc[tmp_df['number_of_concordant_paths'].between(min_count, max_count)]" + ] + }, + { + "cell_type": "markdown", + "id": "tropical-washington", + "metadata": {}, + "source": [ + "# Filter function main" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "unlimited-carter", + "metadata": {}, + "outputs": [], + "source": [ + "def filter_df(\n", + " df_list: list, \n", + " filter_num: int = 0, \n", + " min_node_count: int = 0,\n", + " max_node_count: int = 0,\n", + " max_path_count:int = 0,\n", + " min_path_count:int = 0,\n", + " lmax_list: list = []\n", + "): \n", + " final_df = []\n", + " \n", + " for df in df_list:\n", + " current_df = df\n", + " if len(lmax_list) > 0:\n", + " current_df = filter_by_lmax(current_df, by_lmax=lmax_list)\n", + " \n", + " if min_node_count != 0:\n", + " current_df = filter_by_node_count(\n", + " current_df,\n", + " min_count=min_node_count,\n", + " max_count=max_node_count\n", + " )\n", + " \n", + " if min_path_count != 0:\n", + " current_df = filter_by_concordant_path(\n", + " current_df, \n", + " max_count=max_path_count, \n", + " min_count=min_path_count\n", + " )\n", + " \n", + " if filter_num != 0:\n", + " current_df = filter_by_path_occurence(current_df, filter_num)\n", + " \n", + " final_df.append(current_df)\n", + " \n", + " return final_df" + ] + }, + { + "cell_type": "markdown", + "id": "featured-tuner", + "metadata": {}, + "source": [ + "# Value by chance for each gold standard data (From Notebook 5.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "loose-monroe", + "metadata": {}, + "outputs": [], + "source": [ + "clinical_dict = {\n", + " 'openbio': {\n", + " 'creed_target': 26.1649,\n", + " 'creed_geo': 36.7742,\n", + " 'l1000_target': 11.3169,\n", + " 'l1000_geo': 15.6614,\n", + " },\n", + " 'custom': {\n", + " 'creed_target': 24.359,\n", + " 'creed_geo': 33.5294,\n", + " 'l1000_target': 9.23336,\n", + " 'l1000_geo': 12.8342,\n", + " }\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "sound-account", + "metadata": {}, + "source": [ + "# Get precision values" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "coastal-thing", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "def cal_trial_info(df_list, trial_value_dict, data_col):\n", + " \n", + " info_df = pd.DataFrame(columns=[\n", + " 'dataset',\n", + " 'subgraph',\n", + " 'precision',\n", + " 'value by chance',\n", + " 'pair_count'\n", + " ])\n", + " \n", + " for df in df_list:\n", + " for gname in ['openbio', 'custom']:\n", + " if df.empty:\n", + " continue\n", + " \n", + " new_df = df.loc[df['subgraph_name'] == gname]\n", + " \n", + " if new_df.empty:\n", + " data_set = df['dataset'].unique().tolist()[0]\n", + " trial_dict = {True: set(), False: set()}\n", + " percent = ''\n", + " else:\n", + " data_set = new_df['dataset'].unique().tolist()[0]\n", + " \n", + " trial_dict = {True: set(), False: set()}\n", + " for bool_val, pair in new_df[[data_col, 'pairs']].values:\n", + " trial_dict[bool_val].add(pair)\n", + " \n", + " # Convert back to list\n", + " trial_dict[True] = list(trial_dict[True])\n", + " trial_dict[False] = list(trial_dict[False])\n", + " \n", + " percent = len(trial_dict[True])/(len(trial_dict[True]) + len(trial_dict[False])) * 100\n", + " \n", + " gdict = trial_value_dict[gname]\n", + " \n", + " # Get binomial p-value\n", + " true_positive = len(trial_dict[True])\n", + " all_positives = (len(trial_dict[True]) + len(trial_dict[False]))\n", + " trial_val = round(gdict[data_set], 3) / 100\n", + " \n", + " tmp = pd.DataFrame({\n", + " 'dataset': data_set,\n", + " 'subgraph': gname,\n", + " 'precision': round(percent, 3) if type(percent) == float else percent, \n", + " 'value by chance': round(gdict[data_set], 3),\n", + " 'pair_count': f'{true_positive}/{all_positives}'\n", + " }, index=[0])\n", + " info_df = pd.concat([info_df, tmp], ignore_index=True)\n", + " \n", + " return info_df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "recreational-bottle", + "metadata": {}, + "outputs": [], + "source": [ + "def main(file_ending: str, trial_dict: dict, col_name: str):\n", + " k = []\n", + " \n", + " for file in os.listdir(os.path.join(DATA_DIR, 'concordant_paths')):\n", + " if file.endswith(file_ending):\n", + " df = pd.read_csv(\n", + " os.path.join(DATA_DIR, 'concordant_paths', file),\n", + " sep='\\t'\n", + " )\n", + "\n", + " data_set = file.split('-')[0]\n", + " df['dataset'] = data_set\n", + "\n", + " df['pairs'] = df['source'] + '_' + df['target']\n", + "\n", + " k.append(df)\n", + " \n", + " filtered_df = filter_df(\n", + " df_list=k,\n", + "# node_count=3,\n", + "# max_node_count=400,\n", + "# lmax_list=[6, 7],\n", + "# max_path_count=486,\n", + "# min_path_count=3,\n", + " )\n", + " \n", + " m = cal_trial_info(\n", + " df_list=filtered_df, \n", + " trial_value_dict=trial_dict, \n", + " data_col=col_name\n", + " )\n", + " return m" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "faced-comedy", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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datasetsubgraphprecisionvalue by chancepair_count
0l1000_targetopenbio54.54511.3176/11
1l1000_targetcustom50.0009.2332/4
2creed_geoopenbio50.00036.7741/2
3creed_geocustom50.00033.5291/2
4creed_targetopenbio50.00026.1651/2
5creed_targetcustom0.00024.3590/1
6l1000_geoopenbio80.00015.6614/5
7l1000_geocustom66.66712.8342/3
\n", + "
" + ], + "text/plain": [ + " dataset subgraph precision value by chance pair_count\n", + "0 l1000_target openbio 54.545 11.317 6/11\n", + "1 l1000_target custom 50.000 9.233 2/4\n", + "2 creed_geo openbio 50.000 36.774 1/2\n", + "3 creed_geo custom 50.000 33.529 1/2\n", + "4 creed_target openbio 50.000 26.165 1/2\n", + "5 creed_target custom 0.000 24.359 0/1\n", + "6 l1000_geo openbio 80.000 15.661 4/5\n", + "7 l1000_geo custom 66.667 12.834 2/3" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "m = main(\n", + " file_ending='-results.tsv',\n", + " trial_dict=clinical_dict,\n", + " col_name='in_clinical_trial'\n", + ")\n", + "m" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/5.0-transcriptomics_specific_analysis.ipynb b/src/notebooks/5.0-transcriptomics_specific_analysis.ipynb new file mode 100644 index 0000000..0b42c46 --- /dev/null +++ b/src/notebooks/5.0-transcriptomics_specific_analysis.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "environmental-guarantee", + "metadata": {}, + "source": [ + "# Transcriptomics-specific Analysis\n", + "\n", + "This notebook contains creation of all chemical-disease pairs in our subgraphs based on specific-transcriptomic data" + ] + }, + { + "cell_type": "markdown", + "id": "living-sessions", + "metadata": {}, + "source": [ + "# Pre-requirements\n", + "\n", + "1. Installation of drug2ways\n", + "1. Running of earlier notebook (notebook 2,3, and 5) " + ] + }, + { + "cell_type": "markdown", + "id": "terminal-enlargement", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "documentary-privacy", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import os\n", + "import json\n", + "import logging\n", + "from tqdm import tqdm\n", + "from itertools import product\n", + "from networkx import DiGraph\n", + "\n", + "from utils import (get_paths, filter_dataset, \n", + " get_transcriptomic_paths, create_graph_from_df,\n", + " get_path_count, DATA_DIR)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "stuck-delicious", + "metadata": {}, + "outputs": [], + "source": [ + "logger = logging.getLogger(__name__)\n", + "logging.getLogger('drug2ways').setLevel(logging.CRITICAL)\n", + "logging.basicConfig(level=logging.CRITICAL)" + ] + }, + { + "cell_type": "markdown", + "id": "sonic-animal", + "metadata": {}, + "source": [ + "# Load dataset-generated network dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "widespread-insured", + "metadata": {}, + "outputs": [], + "source": [ + "openbiolink_path = os.path.join(DATA_DIR, 'kg', 'normalized', 'openbiolink_kg_normalized.tsv')\n", + "custom_path = os.path.join(DATA_DIR, 'kg', 'normalized', 'custom_kg_normalized.tsv')\n", + "\n", + "# Load DF\n", + "openbiolink_df = pd.read_csv(openbiolink_path, sep='\\t')\n", + "openbiolink_df.rename(columns={'relation': 'polarity'}, inplace=True)\n", + "\n", + "custom_df = pd.read_csv(custom_path, sep='\\t')\n", + "custom_df.rename(columns={'relation': 'polarity'}, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "id": "familiar-north", + "metadata": {}, + "source": [ + "# Load datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "verified-electric", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(DATA_DIR, 'creeds', 'normalized', 'harmonized_expression.json')) as file:\n", + " creed_dict = json.load(file)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'geo', 'normalized', 'harmonized_expression.json')) as file2:\n", + " geo_dict = json.load(file2)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'l1000', 'normalized', 'harmonized_expression.json')) as file3:\n", + " l1000_dict = json.load(file3)\n", + " \n", + "with open(os.path.join(DATA_DIR, 'open_targets', 'normalized', 'harmonized_expression.json')) as file4:\n", + " open_target_dict = json.load(file4)" + ] + }, + { + "cell_type": "markdown", + "id": "twelve-diesel", + "metadata": {}, + "source": [ + "# Filterting dataset based on network" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "conditional-object", + "metadata": {}, + "outputs": [], + "source": [ + "# CREED\n", + "creed_openbio = filter_dataset(dataset=creed_dict, graph_df=openbiolink_df)\n", + "creed_custom = filter_dataset(dataset=creed_dict, graph_df=custom_df)\n", + "\n", + "creed_dict = {'openbio': creed_openbio, 'custom': creed_custom}\n", + "\n", + "# GEO\n", + "geo_openbio = filter_dataset(dataset=geo_dict, graph_df=openbiolink_df)\n", + "geo_custom = filter_dataset(dataset=geo_dict, graph_df=custom_df)\n", + "\n", + "geo_dict = {'openbio': geo_openbio, 'custom': geo_custom}\n", + "\n", + "# OpenTarget\n", + "target_openbio = filter_dataset(dataset=open_target_dict, graph_df=openbiolink_df)\n", + "target_custom = filter_dataset(dataset=open_target_dict, graph_df=custom_df)\n", + "\n", + "open_target_dict = {'openbio': target_openbio, 'custom': target_custom}\n", + "\n", + "# L1000\n", + "l1000_openbio = filter_dataset(dataset=l1000_dict, graph_df=openbiolink_df)\n", + "l1000_custom = filter_dataset(dataset=l1000_dict, graph_df=custom_df)\n", + "\n", + "l1000_dict = {'openbio': l1000_openbio, 'custom': l1000_custom}\n" + ] + }, + { + "cell_type": "markdown", + "id": "broke-somalia", + "metadata": {}, + "source": [ + "# Load clinical and drug-indication data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "settled-sustainability", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(DATA_DIR, 'gold-standard', 'filtered-clinical-pairs.json')) as file:\n", + " clinical_pair_dict = json.load(file).keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "aggressive-italy", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(DATA_DIR, 'gold-standard', 'filtered-indications.json')) as file:\n", + " indication_pair_dict = json.load(file).keys()" + ] + }, + { + "cell_type": "markdown", + "id": "touched-grocery", + "metadata": {}, + "source": [ + "# Analysis path for " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "integral-prompt", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "environmental-gross", + "metadata": {}, + "source": [ + "# Creating information dict for each chemical-disease pair" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "atmospheric-compiler", + "metadata": {}, + "outputs": [], + "source": [ + "MAP = {\n", + " 'creed' : creed_dict,\n", + " 'target': open_target_dict,\n", + " 'geo': geo_dict,\n", + " 'l1000': l1000_dict,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "marked-tower", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading graph: 100%|██████████| 48878/48878 [00:00<00:00, 385178.96it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "creed_target\n", + "### creed-target ###\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████| 4512/4512 [06:53<00:00, 10.90it/s]\n", + "Calculating concordance: 100%|██████████| 5/5 [00:07<00:00, 1.57s/it]\n", + "Loading graph: 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l1000-target ###\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 37788/37788 [30:02<00:00, 20.96it/s] \n", + "Calculating concordance: 100%|██████████| 5/5 [00:22<00:00, 4.51s/it]\n", + "Loading graph: 100%|██████████| 52182/52182 [00:00<00:00, 358597.00it/s]\n", + "100%|██████████| 10220/10220 [49:03<00:00, 3.47it/s] \n", + "Calculating concordance: 100%|██████████| 5/5 [03:01<00:00, 36.26s/it]\n", + "Loading graph: 100%|██████████| 48878/48878 [00:00<00:00, 345137.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "l1000_geo\n", + "### l1000-geo ###\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 14472/14472 [13:36<00:00, 17.73it/s] \n", + "Calculating concordance: 100%|██████████| 5/5 [00:56<00:00, 11.31s/it]\n", + "Loading graph: 100%|██████████| 52182/52182 [00:00<00:00, 398025.71it/s]\n", + "100%|██████████| 4964/4964 [28:39<00:00, 2.89it/s] \n", + "Calculating concordance: 100%|██████████| 5/5 [05:00<00:00, 60.17s/it]\n" + ] + } + ], + "source": [ + "for c, d in product(['creed', 'l1000'], ['target', 'geo']):\n", + " c_set = MAP[c]\n", + " d_set = MAP[d]\n", + " graph_name = c + '_' + d\n", + " print(graph_name)\n", + " \n", + " print(f'### {c}-{d} ###')\n", + " \n", + " df = pd.DataFrame(columns=[\n", + " 'source',\n", + " 'target',\n", + " 'number_of_paths',\n", + " 'number_of_concordant_paths',\n", + " 'in_clinical_trial',\n", + " 'in_drug_indication',\n", + " 'number_of_concordant_activatory_paths',\n", + " 'number_of_concordant_inhibitory_paths',\n", + " 'subgraph_size',\n", + " 'number_of_unique_nodes',\n", + " 'lmax',\n", + " 'subgraph_name',\n", + " ])\n", + " \n", + " if not os.path.exists(os.path.join(DATA_DIR, 'concordant_paths')):\n", + " os.mkdir(os.path.join(DATA_DIR, 'concordant_paths'))\n", + " \n", + " NAME = f'{graph_name}-.tsv'\n", + " file_path = os.path.join(DATA_DIR, 'concordant_paths', NAME)\n", + " \n", + " if os.path.exists(file_path):\n", + " continue\n", + " \n", + " # Iterating different chemical-disease pair\n", + " final_data = {\n", + " 'drug': df,\n", + " 'disease': df\n", + " }\n", + " \n", + " for gname in ['openbio', 'custom']:\n", + " if gname == 'openbio':\n", + " flag = True\n", + " kg_df = openbiolink_df\n", + " else:\n", + " flag = False\n", + " kg_df = custom_df\n", + " \n", + " paths = get_paths(\n", + " graph_df=kg_df,\n", + " disease_dict=d_set[gname],\n", + " chemical_dict=c_set[gname],\n", + " graph_name=graph_name,\n", + " openbio=flag\n", + " )\n", + " \n", + " if paths is None:\n", + " continue\n", + " \n", + " graph_copy = create_graph_from_df(kg_df)\n", + " graph = graph_copy.copy()\n", + "\n", + " for lmax, p_dict in tqdm(paths.items(), desc='Calculating concordance'):\n", + " for p in p_dict:\n", + " if len(p['paths']) > 0:\n", + " # Just get the nodes from the path\n", + " tmp_paths = []\n", + " for v, l in p['paths'].items():\n", + " pth = []\n", + " for k in l:\n", + " if k in ['-|', '->']:\n", + " continue\n", + " else:\n", + " pth.append(k)\n", + " tmp_paths.append(pth)\n", + "\n", + " chemical = p['source']\n", + " disease = p['target']\n", + "\n", + " results = get_transcriptomic_paths(\n", + " directed_graph=graph,\n", + " source=chemical,\n", + " target=disease,\n", + " all_paths=tmp_paths,\n", + " drug_dict=c_set[gname][chemical],\n", + " disease_dict=d_set[gname][disease],\n", + " clinical_pair_dict=clinical_pair_dict,\n", + " drug_indication_dict=indication_pair_dict,\n", + " )\n", + " \n", + " # For drug data\n", + " for i in ['drug_paths', 'disease_paths']:\n", + " concordant_num = len(results[i])\n", + " if concordant_num != 0:\n", + " activated_paths, inhibited_paths = get_path_count(\n", + " directed_graph=graph,\n", + " filtered_paths=results[i]\n", + " )\n", + " \n", + " new_results = {\n", + " 'source': results['source'],\n", + " 'target': results['target'],\n", + " 'number_of_paths': results['number_of_paths'],\n", + " 'number_of_concordant_paths': concordant_num,\n", + " 'in_clinical_trial': results['in_clinical_trial'],\n", + " 'in_drug_indication': results['in_drug_indication'],\n", + " 'number_of_concordant_activatory_paths': activated_paths,\n", + " 'number_of_concordant_inhibitory_paths': inhibited_paths,\n", + " 'subgraph_size': results['subgraph_size'],\n", + " 'number_of_unique_nodes': results['number_of_unique_nodes'],\n", + " 'lmax': lmax,\n", + " 'subgraph_name': gname,\n", + " }\n", + "\n", + " tmp_df = pd.DataFrame(new_results, index=[0])\n", + " if i == 'drug_paths':\n", + " final_data['drug'] = pd.concat(\n", + " [final_data['drug'], tmp_df],\n", + " ignore_index=True\n", + " )\n", + " else:\n", + " final_data['disease'] = pd.concat(\n", + " [final_data['disease'], tmp_df],\n", + " ignore_index=True\n", + " )\n", + " \n", + " for i, val in final_data.items():\n", + " n_file_path = os.path.join(DATA_DIR, 'concordant_paths', f'{graph_name}-{i}.tsv')\n", + " val.to_csv(n_file_path, sep='\\t', index=False) " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/6.0-benchmark_analysis.ipynb b/src/notebooks/6.0-benchmark_analysis.ipynb new file mode 100644 index 0000000..f0e7c13 --- /dev/null +++ b/src/notebooks/6.0-benchmark_analysis.ipynb @@ -0,0 +1,728 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "willing-theology", + "metadata": {}, + "source": [ + "# Benchmark analysis\n", + "\n", + "This notebook compares the different benchmark according to the two KG." + ] + }, + { + "cell_type": "markdown", + "id": "b63e5181", + "metadata": {}, + "source": [ + "# Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ff7348e0", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import math\n", + "import logging\n", + "import networkx as nx\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from collections import Counter, defaultdict\n", + "from itertools import product\n", + "from typing import Mapping, List\n", + "from tqdm import tqdm\n", + "\n", + "from utils import DATA_DIR, KG_DATA_PATH, create_graph_from_df" + ] + }, + { + "cell_type": "markdown", + "id": "0d74ef14", + "metadata": {}, + "source": [ + "# Load Graph and Gold Standard" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a08b5c11", + "metadata": {}, + "outputs": [], + "source": [ + "openbiolink_df = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'openbiolink_filtered_kg.tsv'),\n", + " sep='\\t'\n", + ")\n", + "\n", + "custom_df = pd.read_csv(\n", + " os.path.join(KG_DATA_PATH, 'custom_filtered_kg.tsv'),\n", + " sep='\\t'\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "moved-kazakhstan", + "metadata": {}, + "source": [ + "# Load clinical data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "708dba64", + "metadata": {}, + "outputs": [], + "source": [ + "with open(os.path.join(DATA_DIR, 'gold-standard', 'clinical-trial.json')) as file:\n", + " clinical_pair_dict = json.load(file).keys()" + ] + }, + { + "cell_type": "markdown", + "id": "chief-lottery", + "metadata": {}, + "source": [ + "# Create KG " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5477bbd3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Report on the number of relations: {-1: 12477, 1: 29022}\n", + "Report on the number of relations: {1: 43578, -1: 8045}\n" + ] + } + ], + "source": [ + "graph_openbio = create_graph_from_df(openbiolink_df)\n", + "graph_custom = create_graph_from_df(custom_df)" + ] + }, + { + "cell_type": "markdown", + "id": "molecular-sheet", + "metadata": {}, + "source": [ + "# Create drug disease dicts specific to KG" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "daily-cedar", + "metadata": {}, + "outputs": [], + "source": [ + "disease_openbio, drug_openbio,disease_custom,drug_custom = set(), set(), set(), set()\n", + "\n", + "for u in graph_openbio.nodes():\n", + " if u.startswith('mondo'):\n", + " disease_openbio.add(u)\n", + " elif u.startswith('pubchem'):\n", + " drug_openbio.add(u)\n", + "\n", + "for u in graph_custom.nodes():\n", + " if u.startswith('mondo'):\n", + " disease_custom.add(u)\n", + " elif u.startswith('pubchem'):\n", + " drug_custom.add(u)" + ] + }, + { + "cell_type": "markdown", + "id": "70e43c44", + "metadata": {}, + "source": [ + "# Helper Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "baking-brush", + "metadata": {}, + "outputs": [], + "source": [ + "score_actual = {}\n", + "kg_dfs = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "subject-anchor", + "metadata": {}, + "outputs": [], + "source": [ + "def khop(\n", + " nodeA: str, \n", + " nodeB: str, \n", + " graph: nx.Graph, \n", + " total: bool\n", + ") -> tuple:\n", + " \n", + " \"\"\"Find nodes within the distance limit \"\"\"\n", + " \n", + " khop_A = {u for u in graph.neighbors(nodeA)}\n", + " khop_B = {u for u in graph.neighbors(nodeB)}\n", + " \n", + " if total:\n", + " return list(khop_A | khop_B), khop_A, khop_B\n", + " else:\n", + " return list(khop_A & khop_B), khop_A, khop_B" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "senior-quest", + "metadata": {}, + "outputs": [], + "source": [ + "def get_dict_df(\n", + " diseases, \n", + " drugs, \n", + " undirected_kg_graph, \n", + " di_kg_graph,\n", + " similarity_type\n", + "):\n", + " \n", + " df = pd.DataFrame(columns=[\n", + " 'source',\n", + " 'target',\n", + " sim_scores[similarity_type]\n", + " ])\n", + "\n", + " for disease in diseases:\n", + " # Skip drugs not part of largest component of KG\n", + " if disease not in undirected_kg_graph.nodes():\n", + " continue\n", + " \n", + " cn = []\n", + " \n", + " # for each disease, find the similarity score with for each drug and append to list\n", + " for drug in drugs:\n", + " \n", + " # Skip drugs not part of largest component of KG\n", + " if drug not in undirected_kg_graph.nodes():\n", + " continue\n", + " \n", + " shared_nodes, nodeA_neighbor, nodeB_neighbor = khop(\n", + " nodeA=drug,\n", + " nodeB=disease,\n", + " graph=undirected_kg_graph, \n", + " total=False,\n", + " )\n", + " \n", + " total_nodes, _, _ = khop(\n", + " nodeA=drug,\n", + " nodeB=disease,\n", + " graph=undirected_kg_graph, \n", + " total=True,\n", + " )\n", + " \n", + " if similarity_type == 'cn':\n", + " similarity = len(shared_nodes)\n", + " \n", + " elif similarity_type == 'cos':\n", + " similarity = len(shared_nodes) / (math.sqrt(len(nodeA_neighbor) * len(nodeB_neighbor)))\n", + " \n", + " elif similarity_type == 'ji':\n", + " similarity = len(shared_nodes) / len(total_nodes)\n", + " \n", + " elif similarity_type == 'si':\n", + " similarity = (2 * len(shared_nodes)) / (len(nodeA_neighbor) + len(nodeB_neighbor))\n", + " \n", + " elif similarity_type == 'hpi':\n", + " similarity = len(shared_nodes) / min(len(nodeA_neighbor), len(nodeB_neighbor)) \n", + " \n", + " elif similarity_type == 'hdi':\n", + " similarity = len(shared_nodes) / max(len(nodeA_neighbor), len(nodeB_neighbor)) \n", + " \n", + " elif similarity_type == 'lhn':\n", + " similarity = len(shared_nodes) / (len(nodeA_neighbor) * len(nodeB_neighbor)) \n", + " \n", + " elif similarity_type == 'pa':\n", + " similarity = len(nodeA_neighbor) * len(nodeB_neighbor)\n", + " \n", + " elif similarity_type == 'aa':\n", + " similarity = 0\n", + " \n", + " for n in shared_nodes:\n", + " neighbors_list = set(i for i in undirected_kg_graph.neighbors(n))\n", + " similarity += 1 / math.log10(len(neighbors_list))\n", + " \n", + " elif similarity_type == 'ra':\n", + " similarity = 0\n", + " \n", + " for n in shared_nodes:\n", + " neighbors_list = set(i for i in undirected_kg_graph.neighbors(n))\n", + " similarity += 1 / len(neighbors_list)\n", + " elif similarity_type == 'sp':\n", + " # try to see if path is between two nodes\n", + " try:\n", + " similarity = len(nx.shortest_path(di_kg_graph,source=drug, target=disease))\n", + " except:\n", + " continue\n", + " cn.append(similarity)\n", + " \n", + " if not similarity_type == 'sp':\n", + " index = np.where(cn == np.amax(cn))\n", + " else:\n", + " index = np.where(cn == np.amin(cn))\n", + " # if list is full of 0's (i.e sum == 0), then there are no shared neighbors \n", + " \n", + " if np.sum(cn) == 0 or len(np.array(cn)[index]) > 1:\n", + " continue \n", + " \n", + " for val in index:\n", + " for j in val:\n", + " df = df.append(\n", + " {\n", + " 'source': list(drugs)[j], \n", + " 'target': disease, \n", + " sim_scores[similarity_type]: cn[j]\n", + " }, \n", + " ignore_index=True\n", + " )\n", + "\n", + " return df" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "892dea1b", + "metadata": {}, + "outputs": [], + "source": [ + "def get_precision(\n", + " clinical_trial_dict: dict, \n", + " predicted: list\n", + ")-> tuple: \n", + " \n", + " total = len(predicted)\n", + " pos = 0\n", + " \n", + " for pair in predicted:\n", + " if pair in clinical_trial_dict:\n", + " pos += 1\n", + " \n", + " \n", + " return round(((pos/total) * 100), 3), pos, total\n" + ] + }, + { + "cell_type": "markdown", + "id": "nervous-valentine", + "metadata": {}, + "source": [ + "# Value by change for both KGs" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "falling-kernel", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.132" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "openbio_prob = 0\n", + "\n", + "for disease in disease_openbio:\n", + " for drug in drug_openbio:\n", + " trial = f'{drug}_{disease}'\n", + " if trial in clinical_pair_dict:\n", + " openbio_prob += 1\n", + "\n", + "total = len(drug_openbio) * len(disease_openbio)\n", + "prob = openbio_prob / total\n", + "open_val_by_chance = round(prob, 3)\n", + "open_val_by_chance" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fleet-today", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.108" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "custom_prob = 0\n", + "\n", + "for disease in disease_custom:\n", + " for drug in drug_custom:\n", + " trial = f'{drug}_{disease}'\n", + " if trial in clinical_pair_dict:\n", + " custom_prob += 1\n", + "\n", + "total = len(drug_custom) * len(disease_custom)\n", + "prob = custom_prob / total\n", + "custom_val_by_chance = round(prob, 3)\n", + "custom_val_by_chance" + ] + }, + { + "cell_type": "markdown", + "id": "perfect-yemen", + "metadata": {}, + "source": [ + "# Different benchmark methods" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "flying-buying", + "metadata": {}, + "outputs": [], + "source": [ + "sim_scores = {\n", + " 'cn': 'Common Neighbors',\n", + " 'cos': 'Cosine Similiarity',\n", + " 'ji': 'Jaccard index',\n", + " 'si': 'Sorensen index',\n", + " 'hpi': 'Hub Promoted Index',\n", + " 'hdi': 'Hub Depressed Index', \n", + " 'lhn': 'Leicht–Holme–Newman Index',\n", + " 'pa':'Preferential Attachment',\n", + " 'aa': 'Adamic-Adar', \n", + " 'ra': 'Resource Allocation Index',\n", + " 'sp': 'Shortest Path'\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "protecting-shaft", + "metadata": {}, + "outputs": [], + "source": [ + "undirected_openbio = graph_openbio.to_undirected()\n", + "undirected_custom = graph_custom.to_undirected()\n", + "check_df = None" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "widespread-dominant", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Calculating scores for algorithms: 100%|██████████| 11/11 [00:03<00:00, 3.19it/s]\n" + ] + } + ], + "source": [ + "for algo in tqdm(sim_scores, desc='Calculating scores for algorithms'):\n", + " \n", + " algo_name = sim_scores[algo]\n", + " \n", + " # OpenBioLink KG\n", + " full_df = get_dict_df(\n", + " diseases=disease_openbio,\n", + " drugs=drug_openbio, \n", + " undirected_kg_graph=undirected_openbio,\n", + " di_kg_graph=graph_openbio,\n", + " similarity_type=algo\n", + " )\n", + "\n", + " \n", + " full_df['pair'] = full_df['source'] + '_' + full_df['target']\n", + " \n", + " openbio_precision, openbio_pos, openbio_total = get_precision(\n", + " clinical_trial_dict=clinical_pair_dict, \n", + " predicted=list(full_df['pair'].unique())\n", + " )\n", + " \n", + " # Custom KG\n", + " df_custom = get_dict_df(\n", + " diseases=disease_custom, \n", + " drugs=drug_custom, \n", + " undirected_kg_graph=undirected_custom,\n", + " di_kg_graph=graph_custom,\n", + " similarity_type=algo\n", + " )\n", + " \n", + " df_custom['pair'] = df_custom['source'] + '_' + df_custom['target']\n", + " \n", + " custom_precision, custom_pos, custom_total = get_precision(\n", + " clinical_trial_dict=clinical_pair_dict, \n", + " predicted=list(df_custom['pair'].unique())\n", + " )\n", + " \n", + " score_actual[algo_name] = {\n", + " 'openbio_precision': openbio_precision,\n", + " 'openbio_val_by_chance': open_val_by_chance,\n", + " '# openbio_pairs': f'{openbio_pos}/{openbio_total}',\n", + " 'custom_precision': custom_precision,\n", + " 'custom_val_by_chance': custom_val_by_chance,\n", + " '# custom_pairs': f'{custom_pos}/{custom_total}',\n", + " } " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bf401167", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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openbio_precisionopenbio_val_by_chance# openbio_pairscustom_precisioncustom_val_by_chance# custom_pairs
Common Neighbors50.00.1322/444.4440.1084/9
Cosine Similiarity43.750.1327/1642.8570.1086/14
Jaccard index43.750.1327/1640.00.1086/15
Sorensen index43.750.1327/1640.00.1086/15
Hub Promoted Index60.00.1323/546.1540.1086/13
Hub Depressed Index43.750.1327/1641.6670.1085/12
Leicht–Holme–Newman Index43.750.1327/1646.6670.1087/15
Preferential Attachment5.00.1321/209.3020.1084/43
Adamic-Adar33.3330.1322/636.3640.1084/11
Resource Allocation Index33.3330.1322/636.3640.1084/11
Shortest Path0.00.1320/10.00.1080/10
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" + ], + "text/plain": [ + " openbio_precision openbio_val_by_chance \\\n", + "Common Neighbors 50.0 0.132 \n", + "Cosine Similiarity 43.75 0.132 \n", + "Jaccard index 43.75 0.132 \n", + "Sorensen index 43.75 0.132 \n", + "Hub Promoted Index 60.0 0.132 \n", + "Hub Depressed Index 43.75 0.132 \n", + "Leicht–Holme–Newman Index 43.75 0.132 \n", + "Preferential Attachment 5.0 0.132 \n", + "Adamic-Adar 33.333 0.132 \n", + "Resource Allocation Index 33.333 0.132 \n", + "Shortest Path 0.0 0.132 \n", + "\n", + " # openbio_pairs custom_precision \\\n", + "Common Neighbors 2/4 44.444 \n", + "Cosine Similiarity 7/16 42.857 \n", + "Jaccard index 7/16 40.0 \n", + "Sorensen index 7/16 40.0 \n", + "Hub Promoted Index 3/5 46.154 \n", + "Hub Depressed Index 7/16 41.667 \n", + "Leicht–Holme–Newman Index 7/16 46.667 \n", + "Preferential Attachment 1/20 9.302 \n", + "Adamic-Adar 2/6 36.364 \n", + "Resource Allocation Index 2/6 36.364 \n", + "Shortest Path 0/1 0.0 \n", + "\n", + " custom_val_by_chance # custom_pairs \n", + "Common Neighbors 0.108 4/9 \n", + "Cosine Similiarity 0.108 6/14 \n", + "Jaccard index 0.108 6/15 \n", + "Sorensen index 0.108 6/15 \n", + "Hub Promoted Index 0.108 6/13 \n", + "Hub Depressed Index 0.108 5/12 \n", + "Leicht–Holme–Newman Index 0.108 7/15 \n", + "Preferential Attachment 0.108 4/43 \n", + "Adamic-Adar 0.108 4/11 \n", + "Resource Allocation Index 0.108 4/11 \n", + "Shortest Path 0.108 0/10 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.DataFrame(score_actual).transpose()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2608089d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/notebooks/utils.py b/src/notebooks/utils.py new file mode 100644 index 0000000..6be0910 --- /dev/null +++ b/src/notebooks/utils.py @@ -0,0 +1,558 @@ +# -*- coding: utf-8 -*- + +""" + Util functions used in notebook +""" + +import os +import json +import logging +import ast +import pandas as pd +from collections import Counter, defaultdict +from tqdm import tqdm +from itertools import product +from typing import Mapping, List + +from venn import venn +from networkx import DiGraph, connected_components, has_path +import matplotlib.pyplot as plt +import plotly.graph_objects as go + +from drug2ways.alternative_graph_traversal import enumerate_paths +from drug2ways.pathway import _prepare_json +from drug2ways.wrapper import generate_reduced_graph +from drug2ways.rcr import (rcr_all_paths, validate_paths_with_disease_data, + disease_rcr_all_paths) +from drug2ways.cli_helper import _setup_logging + +# XREF paths +DATA_DIR = '../../data' +KG_DATA_PATH = os.path.join(DATA_DIR, 'kg') + +logger = logging.getLogger(__name__) + + +"""Notebook 1""" + + +def create_venn_diagram( + data_dict: dict, + plot_title: str, +) -> plt: + venn(data_dict) + plt.title(plot_title) + return plt + + +def get_stats( + network_1: dict, + network_2: dict, + disease_set: dict = None, + chemical_set: dict = None +) -> None: + """Get KG overlap information.""" + + diseases_1 = network_1['disease'] + diseases_2 = network_2['disease'] + + chemicals_1 = network_1['chemical'] + chemicals_2 = network_2['chemical'] + + if disease_set: + logger.info(f'No.of elements are: {len(disease_set)}') + + disease_1_intersection = diseases_1.intersection(disease_set) + logger.info( + f'No.of elements overlaping with OpenBio network are: ' + f'{len(disease_1_intersection)}' + ) + + disease_2_intersection = diseases_2.intersection(disease_set) + logger.info( + f'No.of elements overlaping with Custom network are: ' + f'{len(disease_2_intersection)}' + ) + + if chemical_set: + chem_1_intersection = chemicals_1.intersection(chemical_set) + logger.info( + f'No.of elements overlaping with OpenBio network are: ' + f'{len(chem_1_intersection)}' + ) + + chem_2_intersection = chemicals_2.intersection(chemical_set) + logger.info( + f'No.of elements overlaping with Custom network are: ' + f'{len(chem_2_intersection)}' + ) + + +"""Notebook 2""" + + +def create_graph_from_df( + graph_df +) -> DiGraph: + """Create fully connected graph from dataframe.""" + graph = DiGraph() + + for sub_name, obj_name, relation in graph_df.values: + # Store edge in the graph + graph.add_edge( + sub_name, + obj_name, + polarity=relation, + ) + + logger.warning( + f"Report on the number of relations: " + f"{dict(Counter(graph_df.polarity))}" + ) + + connected_components_subgraph = [ + component + for component in sorted( + connected_components( + graph.to_undirected() + ), + key=len, + reverse=True + ) + ] + + final_subgraph = graph.subgraph(connected_components_subgraph[0]) + + return final_subgraph + + +"""Notebook 3""" + + +def create_df_from_graph( + graph: DiGraph +) -> pd.DataFrame: + """Create dataframe from graph.""" + + df = pd.DataFrame(columns=['source', 'target', 'polarity']) + + for source, target, relation in tqdm(graph.edges(data=True), desc='Processing data'): + tmp_df = pd.DataFrame( + { + 'source': source, + 'target': target, + 'polarity': relation['polarity'] + }, + index=[1,] + ) + df = pd.concat([df, tmp_df], ignore_index=True) + + return df + + +"""Notebook 4""" + + +def _validation_paths( + reduced_graph: DiGraph, + paths: List[List[int]], + id2node: Mapping[int, str], +) -> [dict, set]: + """Get paths between nodes.""" + results = defaultdict(Counter) + + # Iter over all paths + for path in paths: + # Iterate through each node tracking its position in the path + for index, node in enumerate(path): + results[index][id2node[node]] += 1 + + polarity_dict = {1: '->', -1: '-|'} + + final_paths = set() + for path in paths: + + reconstructed_path = [] + + for index, node in enumerate(path): + + # Avoid crashing + if index + 1 == len(path): + continue + + polarity = polarity_dict[ + reduced_graph[node][path[index + 1]]['polarity'] + ] + + if index == 0: + reconstructed_path.append(node) + reconstructed_path.append(polarity) + reconstructed_path.append(path[index + 1]) + else: + reconstructed_path.append(polarity) + reconstructed_path.append(path[index + 1]) + + final_paths.add( + tuple( + id2node[cosa] if cosa in id2node else cosa + for cosa in reconstructed_path + ) + ) + + final_paths = _prepare_json(final_paths) + + return final_paths + + +def get_paths( + graph: DiGraph, + disease_dict: dict, + chemical_dict: dict, +) -> dict: + """Get paths in graph.""" + detailed_info = {} + + chemical_els = set( + node + for node in graph.nodes() + if 'pubchem' in node + ) + + disease_els = set( + node + for node in graph.nodes() + if 'mondo' in node + ) + + # Get the reduced version of the graph and the node2id mapping + target_nodes = list(disease_dict.keys()) + reduced_graph, node2id = generate_reduced_graph(graph, target_nodes) + + id2node = { + v: k + for k, v in node2id.items() + } + + # Iterating different chemical-disease pair + for chemical, disease in tqdm( + product(chemical_dict, disease_dict), + total=len(chemical_dict) * len(disease_dict), + desc='Getting paths' + ): + if chemical in chemical_els and disease in disease_els: + for lmax in range(3, 8): + dict_key = f'lmax_{lmax}' + + if dict_key not in detailed_info: + detailed_info[dict_key] = [] + + paths = enumerate_paths( + graph=reduced_graph, + source=node2id[chemical], + targets=[node2id[disease]], + lmax=lmax, + cycle_free=True, + ) + + # Skip if there are no paths + if not paths: + detailed_info[dict_key].append( + { + 'source': chemical, + 'target': disease, + 'paths': [] + } + ) + + else: + # Get summarized results for export + paths_summary = _validation_paths( + reduced_graph=reduced_graph, + paths=paths, + id2node=id2node, + ) + + detailed_info[dict_key].append( + { + 'source': chemical, + 'target': disease, + 'paths': paths_summary + } + ) + return detailed_info + + +def filter_dataset( + dataset: dict, + graph_df: pd.DataFrame, +) -> dict: + """Filter dataset based on data in KG""" + # TODO: Accept graph too. + chemical_nodes = set( + graph_df[ + graph_df['source'].str.contains('pubchem.compound') + ]['source'] + ) + disease_nodes = set( + graph_df[ + graph_df['target'].str.contains('mondo') + ]['target'] + ) + gene_1 = set( + graph_df[ + graph_df['source'].str.contains('ncbigene') + ]['source'] + ) + gene_2 = set( + graph_df[ + graph_df['target'].str.contains('ncbigene') + ]['target'] + ) + gene_nodes = gene_1.union(gene_2) + + new_dict = {} + + node_list = set() + + for i in dataset: + if i in chemical_nodes or i in disease_nodes: + gene_dict = {} + node_list.add(i) + + for gene in dataset[i]: + if gene in gene_nodes: + gene_dict[gene] = dataset[i][gene] + + new_dict[i] = gene_dict + + # Assert + for node in node_list: + assert node in chemical_nodes or node in disease_nodes + + logger.info(f'Reduced dataset from {len(dataset)} to {len(new_dict)}') + + return new_dict + + +ERRORS_ALLOWED = 1 + + +def get_validated_paths( + directed_graph: DiGraph, + source: str, + target: str, + all_paths: List[list], + drug_dict: dict, + disease_dict: dict, + clinical_pair_dict: dict, +) -> dict: + """Validate paths in KG""" + _setup_logging(False) + + nodes = set() + for path in all_paths: + path_set = set(path) + nodes.update(path_set) + + # Getting concordant paths with the drug_dict + filtered_paths = rcr_all_paths( + directed_graph, + all_paths, + drug_dict, + errors_allowed=ERRORS_ALLOWED + ) + + # Add genes if missing in disease data + for path in filtered_paths: + for gene in path[1:]: + if gene not in disease_dict: + disease_dict[gene] = 0 + + # Check the values for each node in both dictionaries are the opposite + validate_paths = validate_paths_with_disease_data( + paths=filtered_paths, + drug_dict=drug_dict, + disease_dict=disease_dict, + errors_allowed=ERRORS_ALLOWED + ) + + # Check if pair in clinical trials dicts + pair = source + '_' + target + + if pair in clinical_pair_dict: + trial_pair = True + else: + trial_pair = False + + # Count number of activatory and inhibitory paths + activatory_paths = 0 + for path in validate_paths: + relation_sign = 1 + for node_id in range(len(path) - 1): + s = path[node_id] + t = path[node_id + 1] + relation_sign *= directed_graph[s][t]['polarity'] + + if relation_sign == 1: + activatory_paths += 1 + + inhibitory_paths = len(validate_paths) - activatory_paths + + results = { + "source": source, + "target": target, + "number_of_paths": len(all_paths), + "number_of_concordant_paths": len(validate_paths), + "in_clinical_trial": trial_pair, + "number_of_concordant_activatory_paths": activatory_paths, + "number_of_concordant_inhibitory_paths": inhibitory_paths, + "subgraph_size": directed_graph.number_of_nodes(), + "number_of_unique_nodes": len(nodes), + } + return results + + +def get_transcriptomic_paths( + directed_graph: DiGraph, + source: str, + target: str, + all_paths: List[list], + drug_dict: dict, + disease_dict: dict, + clinical_pair_dict=dict, +) -> [dict, dict]: + """Validate paths in KG based on the transcriptomic data. """ + _setup_logging(False) + + nodes = set() + for path in all_paths: + path_set = set(path) + nodes.update(path_set) + + # Getting concordant paths with the transcriptomic data + drug_filtered_paths = rcr_all_paths( + directed_graph, + all_paths, + drug_dict, + errors_allowed=ERRORS_ALLOWED + ) + disease_filtered_paths = disease_rcr_all_paths( + directed_graph, + all_paths, + disease_dict, + errors_allowed=ERRORS_ALLOWED + ) + + # Check if pair in clinical trials + pair = source + '_' + target + if pair in clinical_pair_dict: + trial_pair = True + else: + trial_pair = False + + results = { + "source": source, + "target": target, + "number_of_paths": len(all_paths), + "in_clinical_trial": trial_pair, + "drug_paths": drug_filtered_paths, + "disease_paths": disease_filtered_paths, + "subgraph_size": directed_graph.number_of_nodes(), + "number_of_unique_nodes": len(nodes), + } + + return results + + +def get_path_count( + directed_graph: DiGraph, + filtered_paths: list +) -> (int, int): + """Count number of activatory and inhibitory paths. """ + activatory_paths = 0 + for path in filtered_paths: + relation_sign = 1 + for node_id in range(len(path) - 1): + s = path[node_id] + t = path[node_id + 1] + relation_sign *= directed_graph[s][t]['polarity'] + + if relation_sign == 1: + activatory_paths += 1 + + inhibitory_paths = len(filtered_paths) - activatory_paths + + return activatory_paths, inhibitory_paths + + +"""Notebook 6""" + + +def get_sankey_diagram( + path_tsv_dir: str, + source_node: str, + target_node: str, + graph_type: str +): + """ Get sankey graph for source and target. graph_type is either 'custom' + or 'openbio' """ + paths_df = pd.read_csv(path_tsv_dir, sep='\t') + query = '(source == "{0}") & (target == "{1}")'.format(source_node, target_node) + paths_query = paths_df.query(query) + index = paths_query.index[paths_query['subgraph_name'] == graph_type][0] + path, polarity = [], [] + + ini_list = paths_df.loc[index]['paths'] + ini_pol = paths_df.loc[index]['signs'] + + # transform string list into actual list + if type(ini_list) == str: + res = ast.literal_eval(ini_list) + res_pol = ast.literal_eval(ini_pol) + for n in res: + path.append(n) + for n in res_pol: + polarity.append(n) + + # get set of nodes to use for indexing + nodes = set() + for p in path: + for node in p: + nodes.add(node) + nodes = list(nodes) + # get source target value index corresponding to path connections + source, target, value = [], [], [] + for paths in path: + for n in range(len(paths)): + if n != (len(paths) - 1): + source.append(nodes.index(paths[n])) + target.append(nodes.index(paths[n + 1])) + value.append(1) + + # get colors of nodes + colors = [] + for n in nodes: + if n.startswith('mondo'): + colors.append('blue') + elif n.startswith('ncbigene'): + colors.append('green') + else: + colors.append('orange') + + fig = go.Figure(data=[go.Sankey( + node=dict( + pad=15, + thickness=10, + line=dict(color="black", width=0.3), + label=nodes, + color=colors + ), + link=dict( + source=source, + target=target, + value=value + ))]) + + fig.update_layout(title_text="Drug Disease Paths between {0} and {1}".format(source_node, target_node), + font_size=10) + fig.show()