From d9305d401579a6cef041f7823ecbb257392438a0 Mon Sep 17 00:00:00 2001 From: Tooba Abbassi-Daloii Date: Mon, 11 Mar 2024 08:30:34 +0100 Subject: [PATCH 01/10] Sample code for small analyses with networkx graph --- src/pyBiodatafuse/analyzer/analysis.py | 100 +++++++++++++++++++++++++ 1 file changed, 100 insertions(+) create mode 100644 src/pyBiodatafuse/analyzer/analysis.py diff --git a/src/pyBiodatafuse/analyzer/analysis.py b/src/pyBiodatafuse/analyzer/analysis.py new file mode 100644 index 00000000..60e39a8e --- /dev/null +++ b/src/pyBiodatafuse/analyzer/analysis.py @@ -0,0 +1,100 @@ +import networkx as nx +import pandas as pd +from generator import generate_networkx_graph + + +def main(): + df_test = pd.read_pickle("./combined_df.pkl") + print(df_test.head()) + g = generate_networkx_graph(df_test) + print("Number of nodes in graph: {}".format(len(g.nodes))) + print(len(g.edges)) + + max_out_degree = 0 # node associated to most diseases + node_max = None + for node in g.nodes: + if g.out_degree(node) > max_out_degree: + max_out_degree = g.out_degree(node) + node_max = node + + print( + "Node with most diseases associated: {} with {} disease associations known".format( + node_max, max_out_degree + ) + ) + + # print(list(g.edges(data=True))[:10]) + # print(g.get_edge_data("VAMP1", "CHRNE")) + # print(list(g.edges(data="label", default=1, keys=True))[0]) + + # fetch back all labels in the graph, this can help filter by edge type later + labels = set() + for u, v, k in g.edges(data=True): + labels.add(k["label"]) + + print("Labels: {}".format(labels)) + + # e.g. retrieving back gene - disease association links only: + gene_disease = ((u, v) for u, v, d in g.edges(data=True) if d["label"] == "associated_with") + + # how many edges are of type associated_with? + print(len(list(gene_disease))) + + # compute an overview of edge types + for label_type in labels: + subgraph = ((u, v) for u, v, d in g.edges(data=True) if d["label"] == label_type) + print("For label type {} there are {} edges.".format(label_type, len(list(subgraph)))) + + # extract interaction subgraph + ppi_edges = ((u, v) for u, v, d in g.edges(data=True) if d["label"] == "interacts_with") + + ppi_subgraph = nx.DiGraph() + nodes = set() + for u, v in ppi_edges: + ppi_subgraph.add_node(u) + ppi_subgraph.add_node(v) + nodes.add(u) + nodes.add(v) + ppi_subgraph.add_edge(u, v) + + communities_generator = nx.community.girvan_newman(ppi_subgraph) + top_level_communities = next(communities_generator) + print("Top level communities: {}".format(sorted(map(sorted, top_level_communities)))) + + gene_disease_edges = ( + (u, v) for u, v, d in g.edges(data=True) if d["label"] == "associated_with" + ) + + gene_disease_subgraph = nx.DiGraph() + nodes = set() + for u, v in gene_disease_edges: + gene_disease_subgraph.add_node(u) + gene_disease_subgraph.add_node(v) + nodes.add(u) + nodes.add(v) + gene_disease_subgraph.add_edge(u, v) + + print(len(nodes)) + communities_generator = nx.community.girvan_newman(gene_disease_subgraph.to_undirected()) + + top_level_communities = next(communities_generator) + print("Top level communities: {}".format(sorted(map(sorted, top_level_communities)))) + + # basic link prediction using Jaccard + predictions = list(nx.jaccard_coefficient(gene_disease_subgraph.to_undirected())) + non_zero_predictions = [ + (gene, disease, predicted_score) + for (gene, disease, predicted_score) in predictions + if predicted_score > 0 and predicted_score != 1 + ] + + # sort by prediction score descending + non_zero_predictions.sort(key=lambda x: x[2], reverse=True) + + # print top 10 predictions + # ... actually predicts disease - disease links ! + print("Predicted links: {}".format(non_zero_predictions[:10])) + + +if __name__ == "__main__": + main() From 365ee3db32958917baf6c06bbe226d5b4c90188b Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Fri, 11 Oct 2024 15:46:00 +0200 Subject: [PATCH 02/10] update and fix tox --- src/pyBiodatafuse/analyzer/analysis.py | 100 ---------- src/pyBiodatafuse/analyzer/summarize.py | 189 +++++++++++++++++++ src/pyBiodatafuse/annotators/minerva.py | 2 +- src/pyBiodatafuse/annotators/opentargets.py | 11 ++ src/pyBiodatafuse/annotators/wikipathways.py | 1 + src/pyBiodatafuse/graph/generator.py | 51 ++++- src/pyBiodatafuse/id_mapper.py | 31 ++- tests/annotators/test_disgenet.py | 2 +- tests/annotators/test_minerva.py | 12 +- tests/annotators/test_opentargets.py | 12 +- tests/annotators/test_wikipathways.py | 4 +- tox.ini | 1 + 12 files changed, 297 insertions(+), 119 deletions(-) delete mode 100644 src/pyBiodatafuse/analyzer/analysis.py create mode 100644 src/pyBiodatafuse/analyzer/summarize.py diff --git a/src/pyBiodatafuse/analyzer/analysis.py b/src/pyBiodatafuse/analyzer/analysis.py deleted file mode 100644 index 60e39a8e..00000000 --- a/src/pyBiodatafuse/analyzer/analysis.py +++ /dev/null @@ -1,100 +0,0 @@ -import networkx as nx -import pandas as pd -from generator import generate_networkx_graph - - -def main(): - df_test = pd.read_pickle("./combined_df.pkl") - print(df_test.head()) - g = generate_networkx_graph(df_test) - print("Number of nodes in graph: {}".format(len(g.nodes))) - print(len(g.edges)) - - max_out_degree = 0 # node associated to most diseases - node_max = None - for node in g.nodes: - if g.out_degree(node) > max_out_degree: - max_out_degree = g.out_degree(node) - node_max = node - - print( - "Node with most diseases associated: {} with {} disease associations known".format( - node_max, max_out_degree - ) - ) - - # print(list(g.edges(data=True))[:10]) - # print(g.get_edge_data("VAMP1", "CHRNE")) - # print(list(g.edges(data="label", default=1, keys=True))[0]) - - # fetch back all labels in the graph, this can help filter by edge type later - labels = set() - for u, v, k in g.edges(data=True): - labels.add(k["label"]) - - print("Labels: {}".format(labels)) - - # e.g. retrieving back gene - disease association links only: - gene_disease = ((u, v) for u, v, d in g.edges(data=True) if d["label"] == "associated_with") - - # how many edges are of type associated_with? - print(len(list(gene_disease))) - - # compute an overview of edge types - for label_type in labels: - subgraph = ((u, v) for u, v, d in g.edges(data=True) if d["label"] == label_type) - print("For label type {} there are {} edges.".format(label_type, len(list(subgraph)))) - - # extract interaction subgraph - ppi_edges = ((u, v) for u, v, d in g.edges(data=True) if d["label"] == "interacts_with") - - ppi_subgraph = nx.DiGraph() - nodes = set() - for u, v in ppi_edges: - ppi_subgraph.add_node(u) - ppi_subgraph.add_node(v) - nodes.add(u) - nodes.add(v) - ppi_subgraph.add_edge(u, v) - - communities_generator = nx.community.girvan_newman(ppi_subgraph) - top_level_communities = next(communities_generator) - print("Top level communities: {}".format(sorted(map(sorted, top_level_communities)))) - - gene_disease_edges = ( - (u, v) for u, v, d in g.edges(data=True) if d["label"] == "associated_with" - ) - - gene_disease_subgraph = nx.DiGraph() - nodes = set() - for u, v in gene_disease_edges: - gene_disease_subgraph.add_node(u) - gene_disease_subgraph.add_node(v) - nodes.add(u) - nodes.add(v) - gene_disease_subgraph.add_edge(u, v) - - print(len(nodes)) - communities_generator = nx.community.girvan_newman(gene_disease_subgraph.to_undirected()) - - top_level_communities = next(communities_generator) - print("Top level communities: {}".format(sorted(map(sorted, top_level_communities)))) - - # basic link prediction using Jaccard - predictions = list(nx.jaccard_coefficient(gene_disease_subgraph.to_undirected())) - non_zero_predictions = [ - (gene, disease, predicted_score) - for (gene, disease, predicted_score) in predictions - if predicted_score > 0 and predicted_score != 1 - ] - - # sort by prediction score descending - non_zero_predictions.sort(key=lambda x: x[2], reverse=True) - - # print top 10 predictions - # ... actually predicts disease - disease links ! - print("Predicted links: {}".format(non_zero_predictions[:10])) - - -if __name__ == "__main__": - main() diff --git a/src/pyBiodatafuse/analyzer/summarize.py b/src/pyBiodatafuse/analyzer/summarize.py new file mode 100644 index 00000000..9e36bd6d --- /dev/null +++ b/src/pyBiodatafuse/analyzer/summarize.py @@ -0,0 +1,189 @@ +"""Graph summary functions.""" + +from typing import Any, Dict, Optional + +import matplotlib.pyplot as plt +import networkx as nx +import pandas as pd +import plotly.express as px +import seaborn as sns +from tabulate import tabulate + +from pyBiodatafuse.graph.generator import build_networkx_graph, load_dataframe_from_pickle + + +class BioGraph(nx.MultiDiGraph): + """BioGraph class to analyze the graph.""" + + def __init__(self, graph=None, graph_path=None, graph_format="pickle"): + """Initialize the BioGraph class.""" + if graph: + self.graph = graph + elif graph_path: + if graph_format == "pickle": + self.graph = build_networkx_graph(load_dataframe_from_pickle(graph_path)) + elif graph_format == "gml": + self.graph = nx.read_gml(graph_path) + else: + raise ValueError("graph_format must be either 'pickle' or 'gml'") + + self.node_count = self.count_nodes_by_type() + self.edge_count = self.count_edge_by_type() + self.node_source_count = self.get_node_counts_by_source() + self.edge_source_count = self.get_edge_counts_by_source() + self.graph_summary = self.get_graph_summary() + + def get_graph_summary(self) -> str: + """Display graph summary.""" + stats = [ + ("Nodes", self.graph.number_of_nodes()), + ("Edges", self.graph.number_of_edges()), + ("Components", nx.number_weakly_connected_components(self.graph)), + ("Network Density", "{:.2E}".format(nx.density(self.graph))), + ] + return tabulate(stats, tablefmt="html") + + def _plot_type_count( + self, count_df: pd.DataFrame, interactive: bool = False, count_type: str = "Node" + ) -> None: + """Plot the type counts on barplot.""" + if count_type == "Node": + plot_title = "Node Type Count" + x_label = "Node Type" + x_col = "node_type" + elif count_type == "Edge": + plot_title = "Edge Type Count" + x_label = "Edge Type" + x_col = "edge_type" + else: + raise ValueError("count_type must be either 'Node' or 'Edge'") + + if interactive: + fig = px.bar(count_df, x=x_col, y="count") + fig.update_layout(title=plot_title) + fig.update_xaxes(title_text=x_label) + fig.update_yaxes(title_text="Count") + fig.show() + else: + plt.figure(figsize=(10, 6)) + sns.barplot(x=x_col, y="count", data=count_df) + # counts on top of bar + for i in range(count_df.shape[0]): + count = count_df.iloc[i]["count"] + plt.text(i, count, count, ha="center") + plt.title(plot_title) + plt.xlabel(x_label) + plt.ylabel("Count") + plt.tight_layout() + plt.show() + + def count_nodes_by_type( + self, plot: bool = False, interactive: bool = False + ) -> Optional[pd.DataFrame]: + """Count the differnent nodes type in the graph.""" + node_data = pd.DataFrame(self.graph.nodes(data=True), columns=["node", "data"]) + node_data["node_type"] = node_data["data"].apply(lambda x: x["labels"]) + node_count = node_data["node_type"].value_counts().reset_index() + node_count = node_count.sort_values(by="count", ascending=False) + + if plot: + self._plot_type_count(node_count, interactive, count_type="Node") + + return node_count + + def count_edge_by_type( + self, plot: bool = False, interactive: bool = False + ) -> Optional[pd.DataFrame]: + """Count the different edge types in the graph.""" + edge_data = pd.DataFrame(self.graph.edges(data=True), columns=["source", "target", "data"]) + edge_data["edge_type"] = edge_data["data"].apply(lambda x: x["label"]) + edge_count = edge_data["edge_type"].value_counts().reset_index() + edge_count = edge_count.sort_values(by="count", ascending=False) + + if plot: + self._plot_type_count(edge_count, interactive, count_type="Edge") + + return edge_count + + def _plot_source_count(self, source_count_df: pd.DataFrame, count_type: str = "Node") -> None: + """Plot count of nodes or edges by source.""" + if count_type == "Node": + x_col = "node_type" + x_color = "node_source" + elif count_type == "Edge": + x_col = "edge_type" + x_color = "edge_source" + + fig = px.bar(source_count_df, x=x_col, y="count", color=x_color) + fig.update_layout( + title=f"{count_type} count by source", + xaxis_title=f"{count_type} Type", + yaxis_title="Count", + ) + fig.show() + + def get_node_counts_by_source(self, plot: bool = False) -> Optional[pd.DataFrame]: + """Get the count of nodes by data source.""" + node_data = pd.DataFrame(self.graph.nodes(data=True), columns=["node", "data"]) + node_data["node_type"] = node_data["data"].apply(lambda x: x["labels"]) + node_data["node_source"] = node_data["data"].apply(lambda x: x["source"]) + node_source_count = ( + node_data.groupby(["node_type", "node_source"]).size().reset_index(name="count") + ) + + if plot: + self._plot_source_count(node_source_count, count_type="Node") + + return node_source_count + + def get_edge_counts_by_source(self, plot: bool = False) -> Optional[pd.DataFrame]: + """Get the count of edges by data source.""" + edge_data = pd.DataFrame(self.graph.edges(data=True), columns=["source", "target", "data"]) + edge_data["edge_type"] = edge_data["data"].apply(lambda x: x["label"]) + edge_data["edge_source"] = edge_data["data"].apply(lambda x: x["source"]) + edge_source_count = ( + edge_data.groupby(["edge_type", "edge_source"]).size().reset_index(name="count") + ) + edge_source_count = edge_source_count.sort_values(by="count", ascending=False) + + if plot: + self._plot_source_count(edge_source_count, count_type="Edge") + + return edge_source_count + + def get_subgraph(self): + """Get the subgraph of the graph.""" + pass + + def get_all_nodes_by_label(self) -> Dict[str, Any]: + """Get all nodes with their labels.""" + label_dict = {} # type: Dict[str, Any] + for node, data in self.graph.nodes(data=True): + node_type = data["labels"] + if node_type not in label_dict: + label_dict[node_type] = [] + label_dict[node_type].append((node, data)) + + return label_dict + + def get_nodes_by_label(self, label: str) -> list: + """Get all nodes by specific label.""" + label_dict = self.get_all_nodes_by_label() + return label_dict[label] + + def node_in_graph(self, node_type: str, node_namespace: str, node_name: str): + """Check if the node is in the graph.""" + possible_node_type = self.node_count["node_type"].to_list() + + assert node_type in possible_node_type, f"Node type {node_type} not in {possible_node_type}" + + pass + + def get_source_interactions(self, source_type, source_name, interaction_type, datasource): + """Get interactions of a source.""" + pass + + def get_chemical_metatdata(self, chemical_name): + """Get metadata of a chemical.""" + # """Adverse effects, Clinical trials,""" + pass diff --git a/src/pyBiodatafuse/annotators/minerva.py b/src/pyBiodatafuse/annotators/minerva.py index 72372bda..cb6dedd7 100644 --- a/src/pyBiodatafuse/annotators/minerva.py +++ b/src/pyBiodatafuse/annotators/minerva.py @@ -256,7 +256,7 @@ def get_gene_minerva_pathways( data["symbol"] = symbol data["pathway_label"] = pathway_name data["pathway_gene_count"] = len(symbol) - symbol.count(None) - data["pathway_id"] = models[idx]["idObject"] + data["pathway_id"] = "MINERVA:" + str(models[idx]["idObject"]) data["refs"] = refs data["ensembl"] = ensembl data["type"] = entity_type diff --git a/src/pyBiodatafuse/annotators/opentargets.py b/src/pyBiodatafuse/annotators/opentargets.py index b81523ba..5eca26ee 100644 --- a/src/pyBiodatafuse/annotators/opentargets.py +++ b/src/pyBiodatafuse/annotators/opentargets.py @@ -317,6 +317,17 @@ def get_gene_reactome_pathways( check_values_in=["pathway_id"], ) + # Fixing the pathway_id + new_ids = [] + for idx in intermediate_df["pathway_id"]: + if idx.startswith("R-"): + new_ids.append(f"Reactome:{idx}") + elif idx.startswuth("WP"): + new_ids.append(f"WP:{idx}") + else: + new_ids.append(idx) + intermediate_df["pathway_id"] = new_ids + # Merge the two DataFrames on the target column merged_df = collapse_data_sources( data_df=data_df, diff --git a/src/pyBiodatafuse/annotators/wikipathways.py b/src/pyBiodatafuse/annotators/wikipathways.py index 6a604797..6eb06d4d 100644 --- a/src/pyBiodatafuse/annotators/wikipathways.py +++ b/src/pyBiodatafuse/annotators/wikipathways.py @@ -165,6 +165,7 @@ def get_gene_wikipathways(bridgedb_df: pd.DataFrame): intermediate_df.rename(columns={"gene_id": "target"}, inplace=True) intermediate_df["pathway_gene_count"] = pd.to_numeric(intermediate_df["pathway_gene_count"]) intermediate_df = intermediate_df.drop_duplicates() + intermediate_df["pathway_id"] = intermediate_df["pathway_id"].apply(lambda x: f"WP:{x}") # Check if all keys in df match the keys in OUTPUT_DICT check_columns_against_constants( diff --git a/src/pyBiodatafuse/graph/generator.py b/src/pyBiodatafuse/graph/generator.py index c332b15c..ece72aa9 100644 --- a/src/pyBiodatafuse/graph/generator.py +++ b/src/pyBiodatafuse/graph/generator.py @@ -3,7 +3,10 @@ """Python module to construct a NetworkX graph from the annotated data frame.""" import json +import os import pickle +from logging import Logger +from typing import Any import networkx as nx import pandas as pd @@ -64,6 +67,8 @@ WIKIPATHWAYS, ) +logger = Logger(__name__) + def load_dataframe_from_pickle(pickle_path: str) -> pd.DataFrame: """Load a previously annotated DataFrame from a pickle file. @@ -163,7 +168,7 @@ def add_disgenet_gene_disease_subgraph(g, gene_node_label, annot_list): annot_node_label = annot[DISEASE_NODE_MAIN_LABEL] annot_node_attrs = DISGENET_DISEASE_NODE_ATTRS.copy() annot_node_attrs["name"] = annot["disease_name"] - annot_node_attrs["id"] = annot["UMLS"] + annot_node_attrs["id"] = f"UMLS:{annot['UMLS'].split('_')[1]}" if not pd.isna(annot["HPO"]): annot_node_attrs["HPO"] = annot["HPO"] @@ -780,7 +785,7 @@ def add_gene_node(g, row, dea_columns): gene_node_label = row["identifier"] gene_node_attrs = { "source": BRIDGEDB, - "name": row["identifier"], + "name": f"{row['identifier.source']}:{row['identifier']}", "id": row["target"], "labels": GENE_NODE_LABELS, row["target.source"]: row["target"], @@ -871,7 +876,10 @@ def normalize_edge_attributes(g): del g[u][v][k]["attr_dict"] -def networkx_graph(combined_df: pd.DataFrame, disease_compound=None): +def build_networkx_graph( + combined_df: pd.DataFrame, + disease_compound=None, +): """Construct a NetWorkX graph from a Pandas DataFrame of genes and their multi-source annotations. :param combined_df: the input DataFrame to be converted into a graph. @@ -909,3 +917,40 @@ def networkx_graph(combined_df: pd.DataFrame, disease_compound=None): normalize_edge_attributes(g) return g + + +def save_graph( + combined_df: pd.DataFrame, + combined_metadata: dict[Any, Any], + disease_compound: pd.DataFrame = None, + graph_name: str = "combined", + graph_dir: str = "examples/usecases/", +): + """Save the graph to a file. + + :param combined_df: the input DataFrame to be converted into a graph. + :param combined_metadata: the metadata of the graph. + :param disease_compound: the input DataFrame containing disease-compound relationships. + :param graph_name: the name of the graph. + :param graph_dir: the directory to save the graph. + """ + graph_path = f"{graph_dir}/{graph_name}/" + os.makedirs(graph_path, exist_ok=True) + logger.info(f"Graph will be saved in {graph_path} folder") + + df_path = f"{graph_path}{graph_name}_df.pkl" + metadata_path = f"{graph_path}/{graph_name}_metadata.pkl" + graph_path_pickle = f"{graph_path}/{graph_name}_graph.pkl" + graph_path_gml = f"{graph_path}/{graph_name}_graph.gml" + + # Save the combined DataFrame + combined_df.to_pickle(df_path) + + # Save the metadata + with open(metadata_path, "wb") as file: + pickle.dump(combined_metadata, file) + + # Save the graph + g = build_networkx_graph(combined_df, disease_compound) + nx.write_gpickle(g, graph_path_pickle) + nx.write_gml(g, graph_path_gml) diff --git a/src/pyBiodatafuse/id_mapper.py b/src/pyBiodatafuse/id_mapper.py index 821e8b97..724093cb 100644 --- a/src/pyBiodatafuse/id_mapper.py +++ b/src/pyBiodatafuse/id_mapper.py @@ -233,7 +233,7 @@ def check_smiles(smile: Optional[str]) -> Optional[str]: def get_cid_from_data(idx: Optional[str], idx_type: str) -> Optional[str]: - """Get PubChem ID from any query. + """Get PubChem ID from any query using PubChempy. :param idx: identifier to query :param idx_type: type of identifier to query. Potential curies include : smiles, inchikey, inchi, name @@ -256,6 +256,31 @@ def get_cid_from_data(idx: Optional[str], idx_type: str) -> Optional[str]: return None +def get_cid_from_pugrest(idx: Optional[str], idx_type: str) -> Optional[str]: + """Get PubChem ID from any query throung Pubchem PUGREST. + + :param idx: identifier to query + :param idx_type: type of identifier to query. Potential curies include : smiles, inchikey, inchi, name + :returns: PubChem ID + """ + if idx_type.lower() == "smiles": + idx = check_smiles(idx) + + if not idx: + return None + + cid_data = requests.get( + f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/{idx_type}/{idx}/property/Title/JSON" + ).json() + + if "Fault" in cid_data: + logger.info(f"Issue with {idx}") + return None + + cidx = cid_data["PropertyTable"]["Properties"][0]["CID"] + return cidx + + def pubchem_xref(identifiers: list, identifier_type: str = "name") -> Tuple[pd.DataFrame, dict]: """Map chemical names or smiles or inchikeys to PubChem identifier. @@ -273,12 +298,12 @@ def pubchem_xref(identifiers: list, identifier_type: str = "name") -> Tuple[pd.D # Getting the response to the query cid_data = [] for idx in identifiers: - cid = get_cid_from_data(idx, identifier_type) + cid = get_cid_from_pugrest(idx, identifier_type) cid_data.append( { "identifier": idx, "identifier.source": identifier_type, - "target": str(cid).split(".")[0] if cid else None, + "target": f"pubchem.compounds:{str(cid).split('.')[0]}" if cid else None, "target.source": "PubChem Compound", } ) diff --git a/tests/annotators/test_disgenet.py b/tests/annotators/test_disgenet.py index 3108466e..b6af48cc 100644 --- a/tests/annotators/test_disgenet.py +++ b/tests/annotators/test_disgenet.py @@ -21,7 +21,7 @@ class TestDisgenet(unittest.TestCase): """Test the DISGENET class.""" - @patch("pyBiodatafuse.annotators.disgenet.requests.post") + @patch.object(Session, "get") def test_get_version_disgenet(self, mock_sparql_version): """Test that the API endpoint returns the expected get_version_bgee results.""" mock_sparql_version.side_effect = { diff --git a/tests/annotators/test_minerva.py b/tests/annotators/test_minerva.py index 4af2c4e8..f5710a32 100644 --- a/tests/annotators/test_minerva.py +++ b/tests/annotators/test_minerva.py @@ -64,17 +64,23 @@ def test_get_gene_minerva_pathways(self): # Define the expected DataFrame expected_df = pd.Series( [ - [{"pathway_id": 952, "pathway_label": "HMOX1 pathway", "pathway_gene_count": 113}], [ { - "pathway_id": 953, + "pathway_id": "MINERVA:952", + "pathway_label": "HMOX1 pathway", + "pathway_gene_count": 113, + } + ], + [ + { + "pathway_id": "MINERVA:953", "pathway_label": "Kynurenine synthesis pathway", "pathway_gene_count": 45, } ], [ { - "pathway_id": 942, + "pathway_id": "MINERVA:942", "pathway_label": "Nsp14 and metabolism", "pathway_gene_count": 96, } diff --git a/tests/annotators/test_opentargets.py b/tests/annotators/test_opentargets.py index 5acd5d3c..94779fb8 100644 --- a/tests/annotators/test_opentargets.py +++ b/tests/annotators/test_opentargets.py @@ -327,31 +327,31 @@ def test_get_gene_reactome_pathways(self, mock_post_reactome): [ { "pathway_label": "Biosynthesis of the N-glycan precursor (dolichol lipid-linked oligosaccharide, LLO) and transfer to a nascent protein", - "pathway_id": "R-HSA-446193", + "pathway_id": "Reactome:R-HSA-446193", }, { "pathway_label": "Defective ALG14 causes ALG14-CMS", - "pathway_id": "R-HSA-5633231", + "pathway_id": "Reactome:R-HSA-5633231", }, ], [ { "pathway_label": "Biosynthesis of the N-glycan precursor (dolichol lipid-linked oligosaccharide, LLO) and transfer to a nascent protein", - "pathway_id": "R-HSA-446193", + "pathway_id": "Reactome:R-HSA-446193", }, { "pathway_label": "Defective ALG2 causes CDG-1i", - "pathway_id": "R-HSA-4549349", + "pathway_id": "Reactome:R-HSA-4549349", }, ], [ { "pathway_label": "Highly calcium permeable nicotinic acetylcholine receptors", - "pathway_id": "R-HSA-629597", + "pathway_id": "Reactome:R-HSA-629597", }, { "pathway_label": "Highly calcium permeable postsynaptic nicotinic acetylcholine receptors", - "pathway_id": "R-HSA-629594", + "pathway_id": "Reactome:R-HSA-629594", }, ], ] diff --git a/tests/annotators/test_wikipathways.py b/tests/annotators/test_wikipathways.py index f4be6458..898def1c 100644 --- a/tests/annotators/test_wikipathways.py +++ b/tests/annotators/test_wikipathways.py @@ -105,14 +105,14 @@ def test_get_gene_wikipathways(self, mock_sparql_request): [ [ { - "pathway_id": "WP5153", + "pathway_id": "WP:WP5153", "pathway_label": "N-glycan biosynthesis", "pathway_gene_count": 57.0, } ], [ { - "pathway_id": "WP5153", + "pathway_id": "WP:WP5153", "pathway_label": "N-glycan biosynthesis", "pathway_gene_count": 57.0, } diff --git a/tox.ini b/tox.ini index 25e8ca70..e7f18448 100644 --- a/tox.ini +++ b/tox.ini @@ -113,6 +113,7 @@ description = Run the pyroma tool to check the package friendliness of the proje [testenv:mypy] deps = mypy skip_install = true +implicit_optional = True commands = mypy --install-types --non-interactive --ignore-missing-imports src/ description = Run the mypy tool to check static typing on the project. From 346b5687decca9038b3113e87f94e7f8b7374e6c Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Fri, 11 Oct 2024 16:39:57 +0200 Subject: [PATCH 03/10] fix tox --- src/pyBiodatafuse/graph/generator.py | 4 ++-- tests/annotators/test_disgenet.py | 24 +++++++++++++----------- 2 files changed, 15 insertions(+), 13 deletions(-) diff --git a/src/pyBiodatafuse/graph/generator.py b/src/pyBiodatafuse/graph/generator.py index ece72aa9..a9618856 100644 --- a/src/pyBiodatafuse/graph/generator.py +++ b/src/pyBiodatafuse/graph/generator.py @@ -6,7 +6,7 @@ import os import pickle from logging import Logger -from typing import Any +from typing import Any, Dict import networkx as nx import pandas as pd @@ -921,7 +921,7 @@ def build_networkx_graph( def save_graph( combined_df: pd.DataFrame, - combined_metadata: dict[Any, Any], + combined_metadata: Dict[Any, Any], disease_compound: pd.DataFrame = None, graph_name: str = "combined", graph_dir: str = "examples/usecases/", diff --git a/tests/annotators/test_disgenet.py b/tests/annotators/test_disgenet.py index b6af48cc..c278da04 100644 --- a/tests/annotators/test_disgenet.py +++ b/tests/annotators/test_disgenet.py @@ -9,10 +9,9 @@ from unittest.mock import Mock, patch import pandas as pd -from requests import Session +from requests import Response, Session from pyBiodatafuse.annotators import disgenet -from pyBiodatafuse.annotators.disgenet import get_gene_disease, get_version_disgenet from pyBiodatafuse.constants import DISGENET_DISEASE_COL data_file_folder = os.path.join(os.path.dirname(__file__), "data") @@ -22,14 +21,18 @@ class TestDisgenet(unittest.TestCase): """Test the DISGENET class.""" @patch.object(Session, "get") - def test_get_version_disgenet(self, mock_sparql_version): - """Test that the API endpoint returns the expected get_version_bgee results.""" - mock_sparql_version.side_effect = { + def test_get_version_disgenet(self, requests_get): + """Test that the API endpoint returns the expected get_version_disgenet results.""" + successful_response = Mock(Response) + successful_response.status_code = 406 + successful_response.json.return_value = { "status": "OK", "payload": {"lastUpdate": "10 Jul 2024", "version": "DISGENET v24.2"}, + "httpStatus": 200, } + requests_get.return_value = successful_response - obtained_version = get_version_disgenet(api_key="test") + obtained_version = disgenet.get_version_disgenet(api_key="test") expected_version = {"lastUpdate": "10 Jul 2024", "version": "DISGENET v24.2"} @@ -39,10 +42,7 @@ def test_get_version_disgenet(self, mock_sparql_version): def test_get_gene_disease(self, mock_post_gene_disease): """Test the get_gene_disease function.""" disgenet.get_version_disgenet = Mock( - return_value={ - "status": "OK", - "payload": {"lastUpdate": "10 Jul 2024", "version": "DISGENET v24.2"}, - } + return_value={"lastUpdate": "10 Jul 2024", "version": "DISGENET v24.2"} ) # Mock the version call disgenet.check_endpoint_disgenet = Mock(return_value=True) @@ -58,7 +58,9 @@ def test_get_gene_disease(self, mock_post_gene_disease): mock_post_gene_disease.return_value.ok = True mock_post_gene_disease.return_value.text = '{"status":"OK","paging":{"pageSize":100,"totalElements":6,"totalElementsInPage":6,"currentPageNumber":0},"warnings":["gene_ensembl_id > The parameter value is empty / unspecified.","gene_symbol > The parameter value is empty / unspecified.","disease > The parameter value is empty / unspecified.","chemical > The parameter value is empty / unspecified.","dis_type > The parameter value is empty / unspecified.","dis_class_list > The parameter value is empty / unspecified.","min_score > The parameter value is unspecified or not a double number.","max_score > The parameter value is unspecified or not a double number.","min_ei > The parameter value is unspecified or not a double number.","max_ei > The parameter value is unspecified or not a double number.","min_dsi > The parameter value is unspecified or not a double number.","max_dsi > The parameter value is unspecified or not a double number.","min_dpi > The parameter value is unspecified or not a double number.","max_dpi > The parameter value is unspecified or not a double number.","min_pli > The parameter value is unspecified or not a double number.","max_pli > The parameter value is unspecified or not a double number."],"requestpar":{"gene_ncbi_id":"199857","source":"CURATED","page_number":0},"userinfo":{"profile":"ACADEMIC","profileDescription":"Your DISGENET profile is ACADEMIC; you can access DISGENET REST endpoints with restrictions."},"payload":[{"assocID":15912171,"symbolOfGene":"ALG14","geneNcbiID":199857,"geneEnsemblIDs":["ENSG00000172339"],"geneNcbiType":"protein-coding","geneDSI":0.622,"geneDPI":0.542,"genepLI":6.9786E-5,"geneProteinStrIDs":["Q96F25"],"geneProteinClassIDs":[],"geneProteinClassNames":[],"diseaseVocabularies":["MESH_D018981","NCI_C84615","ORDO_137","DO_5212","MONDO_0015286","UMLS_C0282577"],"diseaseName":"Congenital Disorders of Glycosylation","diseaseType":"disease","diseaseUMLSCUI":"C0282577","diseaseClasses_MSH":["Congenital, Hereditary, and Neonatal Diseases and Abnormalities (C16)","Nutritional and Metabolic Diseases (C18)"],"diseaseClasses_UMLS_ST":["Disease or Syndrome (T047)"],"diseaseClasses_DO":["physical disorder (0080015)","genetic disease (630)","disease of metabolism (0014667)"],"diseaseClasses_HPO":[],"chemicalsIncludedInEvidence":null,"numberPmidsWithChemsIncludedInEvidenceBySource":[],"score":0.6000000000000001,"yearInitial":2005,"yearFinal":2022,"el":"Limited","ei":1.0},{"assocID":20783606,"symbolOfGene":"ALG14","geneNcbiID":199857,"geneEnsemblIDs":["ENSG00000172339"],"geneNcbiType":"protein-coding","geneDSI":0.622,"geneDPI":0.542,"genepLI":6.9786E-5,"geneProteinStrIDs":["Q96F25"],"geneProteinClassIDs":[],"geneProteinClassNames":[],"diseaseVocabularies":["MESH_D020294","NCI_C84647","ORDO_590","DO_3635","MONDO_0018940","UMLS_C0751882"],"diseaseName":"Myasthenic Syndromes, Congenital","diseaseType":"disease","diseaseUMLSCUI":"C0751882","diseaseClasses_MSH":["Congenital, Hereditary, and Neonatal Diseases and Abnormalities (C16)","Nervous System Diseases (C10)"],"diseaseClasses_UMLS_ST":["Disease or Syndrome (T047)"],"diseaseClasses_DO":["physical disorder (0080015)","disease of anatomical entity (7)"],"diseaseClasses_HPO":[],"chemicalsIncludedInEvidence":null,"numberPmidsWithChemsIncludedInEvidenceBySource":[],"score":0.6000000000000001,"yearInitial":null,"yearFinal":null,"el":null,"ei":1.0},{"assocID":25777482,"symbolOfGene":"ALG14","geneNcbiID":199857,"geneEnsemblIDs":["ENSG00000172339"],"geneNcbiType":"protein-coding","geneDSI":0.622,"geneDPI":0.542,"genepLI":6.9786E-5,"geneProteinStrIDs":["Q96F25"],"geneProteinClassIDs":[],"geneProteinClassNames":[],"diseaseVocabularies":["OMIM_612866","OMIM_616227","DO_0110658","MONDO_0014542","UMLS_C4015596"],"diseaseName":"MYASTHENIC SYNDROME, CONGENITAL, 15","diseaseType":"disease","diseaseUMLSCUI":"C4015596","diseaseClasses_MSH":[],"diseaseClasses_UMLS_ST":["Disease or Syndrome (T047)"],"diseaseClasses_DO":["genetic disease (630)","physical disorder (0080015)","disease of anatomical entity (7)"],"diseaseClasses_HPO":[],"chemicalsIncludedInEvidence":null,"numberPmidsWithChemsIncludedInEvidenceBySource":[],"score":0.6,"yearInitial":2013,"yearFinal":2021,"el":null,"ei":1.0},{"assocID":26279064,"symbolOfGene":"ALG14","geneNcbiID":199857,"geneEnsemblIDs":["ENSG00000172339"],"geneNcbiType":"protein-coding","geneDSI":0.622,"geneDPI":0.542,"genepLI":6.9786E-5,"geneProteinStrIDs":["Q96F25"],"geneProteinClassIDs":[],"geneProteinClassNames":[],"diseaseVocabularies":["OMIM_612866","OMIM_619036","MONDO_0033619","UMLS_C5436652"],"diseaseName":"MYOPATHY, EPILEPSY, AND PROGRESSIVE CEREBRAL ATROPHY","diseaseType":"disease","diseaseUMLSCUI":"C5436652","diseaseClasses_MSH":[],"diseaseClasses_UMLS_ST":["Disease or Syndrome (T047)"],"diseaseClasses_DO":[],"diseaseClasses_HPO":[],"chemicalsIncludedInEvidence":null,"numberPmidsWithChemsIncludedInEvidenceBySource":[],"score":0.5,"yearInitial":2017,"yearFinal":2017,"el":null,"ei":1.0},{"assocID":26354111,"symbolOfGene":"ALG14","geneNcbiID":199857,"geneEnsemblIDs":["ENSG00000172339"],"geneNcbiType":"protein-coding","geneDSI":0.622,"geneDPI":0.542,"genepLI":6.9786E-5,"geneProteinStrIDs":["Q96F25"],"geneProteinClassIDs":[],"geneProteinClassNames":[],"diseaseVocabularies":["ORDO_353327","EFO_0700079","UMLS_C5680989"],"diseaseName":"Congenital myasthenic syndromes with glycosylation defect","diseaseType":"disease","diseaseUMLSCUI":"C5680989","diseaseClasses_MSH":["Congenital, Hereditary, and Neonatal Diseases and Abnormalities (C16)","Nervous System Diseases (C10)"],"diseaseClasses_UMLS_ST":["Disease or Syndrome (T047)"],"diseaseClasses_DO":[],"diseaseClasses_HPO":[],"chemicalsIncludedInEvidence":null,"numberPmidsWithChemsIncludedInEvidenceBySource":[],"score":0.4,"yearInitial":2013,"yearFinal":2013,"el":null,"ei":1.0},{"assocID":26279043,"symbolOfGene":"ALG14","geneNcbiID":199857,"geneEnsemblIDs":["ENSG00000172339"],"geneNcbiType":"protein-coding","geneDSI":0.622,"geneDPI":0.542,"genepLI":6.9786E-5,"geneProteinStrIDs":["Q96F25"],"geneProteinClassIDs":[],"geneProteinClassNames":[],"diseaseVocabularies":["OMIM_612866","OMIM_619031","MONDO_0033572","UMLS_C5436646"],"diseaseName":"INTELLECTUAL DEVELOPMENTAL DISORDER WITH EPILEPSY, BEHAVIORAL ABNORMALITIES, AND COARSE FACIES","diseaseType":"disease","diseaseUMLSCUI":"C5436646","diseaseClasses_MSH":[],"diseaseClasses_UMLS_ST":["Disease or Syndrome (T047)"],"diseaseClasses_DO":[],"diseaseClasses_HPO":[],"chemicalsIncludedInEvidence":null,"numberPmidsWithChemsIncludedInEvidenceBySource":[],"score":0.4,"yearInitial":2018,"yearFinal":2018,"el":null,"ei":1.0}]}' - obtained_data, metadata = get_gene_disease(api_key="test", bridgedb_df=bridgedb_dataframe) + obtained_data, metadata = disgenet.get_gene_disease( + api_key="test", bridgedb_df=bridgedb_dataframe + ) expected_data = pd.Series( [ From ef290f68d285be25d86fb506066bd227f1646dea Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Mon, 14 Oct 2024 07:28:39 +0200 Subject: [PATCH 04/10] update gititnore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 993b622d..b4c238df 100644 --- a/.gitignore +++ b/.gitignore @@ -907,3 +907,4 @@ scratch/ *.pkl /pyBiodatafuse-0.0.4.dev0 *.graphml +*.json \ No newline at end of file From 3b188aa39fbcb01c2cf8315eddf05250e9225395 Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Mon, 14 Oct 2024 11:24:03 +0200 Subject: [PATCH 05/10] fix code scripts --- .gitignore | 3 +- examples/usecases/PCS/PCS_gene_list.csv | 2024 ------------------ src/pyBiodatafuse/annotators/opentargets.py | 17 +- src/pyBiodatafuse/annotators/wikipathways.py | 14 +- src/pyBiodatafuse/graph/generator.py | 16 +- src/pyBiodatafuse/id_mapper.py | 37 +- 6 files changed, 67 insertions(+), 2044 deletions(-) delete mode 100644 examples/usecases/PCS/PCS_gene_list.csv diff --git a/.gitignore b/.gitignore index b4c238df..bb4557fb 100644 --- a/.gitignore +++ b/.gitignore @@ -907,4 +907,5 @@ scratch/ *.pkl /pyBiodatafuse-0.0.4.dev0 *.graphml -*.json \ No newline at end of file +*.json +*.gml \ No newline at end of file diff --git a/examples/usecases/PCS/PCS_gene_list.csv b/examples/usecases/PCS/PCS_gene_list.csv deleted file mode 100644 index 23df4a84..00000000 --- a/examples/usecases/PCS/PCS_gene_list.csv +++ /dev/null @@ -1,2024 +0,0 @@ -identifier -LOC729609 -LOC105374060 -DMP1 -PNLIP -OR4N3P -SLC6A14 -LOC101927239 -DEFB105A -DEFB105B -GSTTP1 -NEUROD1 -RND1 -VN1R10P -LOC440446 -LOC152225 -LOC101929341 -PGLYRP3 -LINC01533 -LINC01090 -SPEM1 -C16orf82 -MIR4432HG -LINC01169 -FAM71A -RNASE10 -KLF17 -C9 -ARC -MYL10 -GCM1 -AIPL1 -HSPA6 -LOC101929124 -C7orf65 -SLC2A14 -PNLIPRP2 -NPAS4 -LOC101060498 -PROP1 -ELAVL3 -LOC105747689 -TNF -ADAMTS4 -PCDH10 -LOC101927274 -NR4A2 -LOC102724612 -CEACAM22P -SNAI1 -SLC2A3 -DLX3 -ID2 -LOC151475 -ATF3 -NKAIN4 -ASAP1-IT2 -NOXRED1 -DNM1P41 -SLC7A11 -C10orf82 -ULBP2 -TPTE2P6 -NR4A3 -LOC399715 -CNTN3 -GEM -HSPA7 -NCMAP -PNP -PLK2 -ATP2C2 -TNFRSF10D -ULBP3 -HSPA5 -EFHB -HSD17B13 -WNK3 -LINC01535 -ELL2 -RND3 -DUSP5 -NRXN3 -IPCEF1 -ZNF492 -SDR16C5 -CENPL -SOX11 -MAFF -PRG4 -PCDH17 -CDKN1A -PELI1 -TMEM169 -TMEM236 -EFNA5 -GCH1 -ANGPTL4 -MAP1LC3C -CHL1 -MPZ -SERPINE1 -SLC2A1 -LRRC16A -FRZB -GLIS3 -TIAM1 -SRGAP1 -SH2D4A -MYEF2 -NT5E -VGLL3 -PRTG -DPP4 -KLF11 -TAF13 -STRADB -POMP -LAMTOR5 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-TSSC1 -BOLA1 -PABPC1P2 -TMEM229A -ATP8B1 -LCNL1 -DCDC5 -SOD1 -PAG1 -CETN2 -NCR1 -TMEM100 -URI1 -TEKT4P2 -PCAT1 -SERTAD4 -LINC00550 -GLB1L -UNG -AGMAT -LOC101928540 -ZNF681 -LINC01456 -FCGR2C -ABCG2 -ANAPC11 -LOC102800447 -CYLC2 -C6orf226 -REM2 -BMPR1B -BECN1 -ADM -PDPR -KDM8 -HMBS -MYO1H -LINC00493 -FGF14 -EIF2AK1 -LOC101928489 -KCNK1 -CKS2 -LOC101928035 -LINC01221 -EREG -NDUFB11 -NARF -ZC3HC1 -ADGRE2 -UFC1 -HOMER1 -HDDC2 -HIST1H3A -TNNT3 -ZNF670-ZNF695 -GSR -NDRG4 -TERC -FANCB -FFAR4 -MGAM2 -LRRTM4 -INHBA -LOC403312 -KLLN -DZANK1 -RGS9BP -RIIAD1 -ARL2-SNX15 -PLAU -SPDYE8P -SLC25A19 -BMS1P6 -ZFYVE19 -CTAGE1 -MTIF3 -SPACA4 -SIPA1L1 -SLC2A10 -PGK1 -GIF -MYH8 -LOC101928098 -FRMD4A -LINC01397 -LIPE -TRIM49D2 -PGM1 -HRH4 -LOC646241 -LOC101927587 -CTD-2201I18.1 -RAPGEF4 -RUNX1 -C5 -TRIM49D1 -LOC100508046 -LOC101928885 -UCHL1 -R3HDM4 -MAP9 -MIF4GD -LOC100190986 -COQ2 -KNTC1 -SAXO1 -LOC105369860 -FPR1 -GP6 -EIF2S2 -LINC00461 -HIST1H2AH -DHRS7 -CHST8 -HAGH -C4orf3 -NMUR2 -AKR1C3 -LRRC70 -REXO2 -PRH1-TAS2R14 -SLC9A1 -MNAT1 -SLC37A4 -MGC34796 -HSPB9 -CADM3 -MYEOV2 -KRTAP6-3 -ARNTL2 -ENPP2 -CUBN -LOC339059 -GSDMA -BTG3 -STBD1 -NAV3 -ALDH1L2 -ZBTB21 -SPATA5 -MRPL57 -CWC15 -NOMO3 -UBTD1 -IFI30 -FMNL2 -PRMT3 -LOC101927692 -NTPCR -DHRS7B -TBCB -C3orf58 -KRT222 -WRB-SH3BGR -LOC101928580 -RWDD1 -NKIRAS1 -ABCA1 -CASC20 -RTN4IP1 -SPATA6L -LUZP1 -CARS2 -C2orf61 -LOC102467226 -MIR3945HG -FGF9 -VRTN -PCDH18 -POLR3K -LINC00566 -AOX1 -PDLIM7 -LOC102577426 -USE1 -GINS2 -RAPGEF2 -LINC01492 -TMEM70 -COX17 -SRRM4 -LOC101928295 -ISCA1 -IL18R1 -APOC4-APOC2 -MT1M -LMO2 -SCN4B -RDH12 -FEZF2 -TMEM150B -CPS1 -SLC35G2 -TPM3 -REG1A -LINC01133 -AFAP1L2 -PSENEN -FAM72A -LINC00467 -HELLS -LINC00367 -PLXNA4 -C11orf73 -KLF7 -YBEY -OIT3 -LOC101929681 -PTPRD -LOC100422737 -LINC01411 -TSPAN17 -UGT1A10 -IFT22 -RPS10P7 -DBIL5P2 -IFI44 -BTK -MDP1 -LOC284080 -CYP2C18 -FBXW12 -CORO7-PAM16 -TMEM14B -POLQ -AFF4 -LHFPL4 -ABTB2 -NOMO1 -FHDC1 -TRIM38 -CTSV -GATA3 -LINCR-0002 -CFAP20 -NDUFB6 -RASA4 -LOC100288798 -CFAP206 -ROR1 -ACOT13 -LOC285626 -BANF1 -DCAF4L2 -SH3BGR -OTOA -CD226 -SLC29A4 -RPL18 -PRDX3 -FGB -TEX14 -FBN1 -EPHA3 \ No newline at end of file diff --git a/src/pyBiodatafuse/annotators/opentargets.py b/src/pyBiodatafuse/annotators/opentargets.py index 5eca26ee..8e333487 100644 --- a/src/pyBiodatafuse/annotators/opentargets.py +++ b/src/pyBiodatafuse/annotators/opentargets.py @@ -9,6 +9,7 @@ import numpy as np import pandas as pd import requests +from tqdm import tqdm from pyBiodatafuse import id_mapper from pyBiodatafuse.constants import ( @@ -172,7 +173,7 @@ def get_gene_go_process( # Generate the OpenTargets DataFrame intermediate_df = pd.DataFrame() - for gene in r["data"]["targets"]: + for gene in tqdm(r["data"]["targets"], desc="Processing gene annotation"): terms = [i["term"] for i in gene["geneOntology"]] types = [i["aspect"] for i in gene["geneOntology"]] path_df = pd.DataFrame(terms) @@ -293,7 +294,7 @@ def get_gene_reactome_pathways( # Generate the OpenTargets DataFrame intermediate_df = pd.DataFrame() - for gene in r["data"]["targets"]: + for gene in tqdm(r["data"]["targets"], desc="Processing gene-pathway interactions"): path_df = pd.DataFrame(gene["pathways"]) path_df = path_df.drop_duplicates() path_df["target"] = gene["id"] @@ -516,7 +517,7 @@ def get_gene_compound_interactions( # Generate the OpenTargets DataFrame intermediate_df = pd.DataFrame() - for gene in r["data"]["targets"]: + for gene in tqdm(r["data"]["targets"], desc="Processing gene-drug interactions"): if not gene["knownDrugs"]: continue @@ -930,10 +931,12 @@ def get_gene_compound_interactions( def get_disease_compound_interactions( bridgedb_df: pd.DataFrame, + cache_pubchem_cid: bool = False, ) -> Tuple[pd.DataFrame, dict]: """Get information about drugs associated with diseases of interest. :param bridgedb_df: BridgeDb output for creating the list of gene ids to query. + :param cache_pubchem_cid: If True, the PubChem CID will be cached for future use. :raises ValueError: if failed to retrieve data :returns: a DataFrame containing the OpenTargets output and dictionary of the query metadata. """ @@ -1025,7 +1028,9 @@ def get_disease_compound_interactions( ) return pd.DataFrame(), opentargets_version - for disease in response_data["data"]["diseases"]: + for disease in tqdm( + response_data["data"]["diseases"], desc="Processing diseases-drug interactions" + ): if not disease["knownDrugs"]: continue @@ -1082,7 +1087,9 @@ def get_disease_compound_interactions( # Fixing chembl_id to pubchem_id mapped_df, _ = id_mapper.pubchem_xref( - identifiers=intermediate_df["chembl_id"], identifier_type="name" + identifiers=intermediate_df["chembl_id"], + identifier_type="name", + cache_res=cache_pubchem_cid, ) intermediate_df["compound_cid"] = mapped_df["target"] diff --git a/src/pyBiodatafuse/annotators/wikipathways.py b/src/pyBiodatafuse/annotators/wikipathways.py index 6eb06d4d..3a0f7f55 100644 --- a/src/pyBiodatafuse/annotators/wikipathways.py +++ b/src/pyBiodatafuse/annotators/wikipathways.py @@ -4,13 +4,14 @@ """Python file for queriying Wikipathways SPARQL endpoint ().""" import datetime -import logging import os +import time import warnings from string import Template from typing import Any, Dict import pandas as pd +from tqdm import tqdm from SPARQLWrapper import JSON, SPARQLWrapper from SPARQLWrapper.SPARQLExceptions import SPARQLWrapperException @@ -26,8 +27,6 @@ get_identifier_of_interest, ) -logger = logging.getLogger("wikipathways") - def check_endpoint_wikipathways() -> bool: """Check the availability of the WikiPathways SPARQL endpoint. @@ -74,7 +73,7 @@ def get_gene_wikipathways(bridgedb_df: pd.DataFrame): :param bridgedb_df: BridgeDb output for creating the list of gene ids to query :returns: a DataFrame containing the WikiPathways output and dictionary of the WikiPathways metadata. """ - # Check if the DisGeNET API is available + # Check if the endpoint is available api_available = check_endpoint_wikipathways() if not api_available: @@ -113,8 +112,11 @@ def get_gene_wikipathways(bridgedb_df: pd.DataFrame): intermediate_df = pd.DataFrame() - for gene_list_str in query_gene_lists: - query_count += 1 + for gene_list_str in tqdm(query_gene_lists, desc="Querying WikiPathways"): + if query_count > 10: + print("Sleeping for 5 seconds to avoid overloading the server.") + time.sleep(5) + query_count = 0 sparql_query_template = Template(sparql_query) substit_dict = dict(gene_list=gene_list_str) diff --git a/src/pyBiodatafuse/graph/generator.py b/src/pyBiodatafuse/graph/generator.py index a9618856..1c8d5751 100644 --- a/src/pyBiodatafuse/graph/generator.py +++ b/src/pyBiodatafuse/graph/generator.py @@ -8,6 +8,7 @@ from logging import Logger from typing import Any, Dict +from tqdm import tqdm import networkx as nx import pandas as pd @@ -903,7 +904,7 @@ def build_networkx_graph( PUBCHEM_COMPOUND_ASSAYS_COL: add_pubchem_assay_subgraph, } - for _i, row in combined_df.iterrows(): + for _i, row in tqdm(combined_df.iterrows(), total=combined_df.shape[0], desc="Building graph"): if pd.notna(row["identifier"]) and pd.notna(row["target"]): gene_node_label = add_gene_node(g, row, dea_columns) process_annotations(g, gene_node_label, row, func_dict) @@ -934,23 +935,28 @@ def save_graph( :param graph_name: the name of the graph. :param graph_dir: the directory to save the graph. """ - graph_path = f"{graph_dir}/{graph_name}/" + graph_path = f"{graph_dir}/{graph_name}" os.makedirs(graph_path, exist_ok=True) - logger.info(f"Graph will be saved in {graph_path} folder") - df_path = f"{graph_path}{graph_name}_df.pkl" + df_path = f"{graph_path}/{graph_name}_df.pkl" metadata_path = f"{graph_path}/{graph_name}_metadata.pkl" graph_path_pickle = f"{graph_path}/{graph_name}_graph.pkl" graph_path_gml = f"{graph_path}/{graph_name}_graph.gml" # Save the combined DataFrame combined_df.to_pickle(df_path) + logger.warning(f"Combined DataFrame saved in {df_path}") # Save the metadata with open(metadata_path, "wb") as file: pickle.dump(combined_metadata, file) + logger.warning(f"Metadata saved in {metadata_path}") # Save the graph g = build_networkx_graph(combined_df, disease_compound) - nx.write_gpickle(g, graph_path_pickle) + logger.warning(f"Graph is built successfully") + + with open(graph_path_pickle, "wb") as f: + pickle.dump(g, f) nx.write_gml(g, graph_path_gml) + logger.warning(f"Graph saved in {graph_path_pickle} and {graph_path_gml}") diff --git a/src/pyBiodatafuse/id_mapper.py b/src/pyBiodatafuse/id_mapper.py index 724093cb..94c0cb39 100644 --- a/src/pyBiodatafuse/id_mapper.py +++ b/src/pyBiodatafuse/id_mapper.py @@ -2,11 +2,14 @@ """Python file for mapping identifiers using BridgeDb.""" +import os +import json import csv import datetime import logging import time from importlib import resources +from tqdm import tqdm from typing import List, Optional, Tuple import pandas as pd @@ -281,11 +284,14 @@ def get_cid_from_pugrest(idx: Optional[str], idx_type: str) -> Optional[str]: return cidx -def pubchem_xref(identifiers: list, identifier_type: str = "name") -> Tuple[pd.DataFrame, dict]: +def pubchem_xref( + identifiers: list, identifier_type: str = "name", cache_res: bool = False +) -> Tuple[pd.DataFrame, dict]: """Map chemical names or smiles or inchikeys to PubChem identifier. :param identifiers: a list of identifiers to query :param identifier_type: type of identifier to query. Potential curies include : smiles, inchikey, inchi, name + :param cache_res: whether to cache the results :raises ValueError: if the input_datasource is not provided or if the request fails :returns: a DataFrame containing the mapped identifiers and dictionary of the data resource metadata. """ @@ -297,8 +303,33 @@ def pubchem_xref(identifiers: list, identifier_type: str = "name") -> Tuple[pd.D # Getting the response to the query cid_data = [] - for idx in identifiers: - cid = get_cid_from_pugrest(idx, identifier_type) + c = 0 + + if cache_res: + if os.path.exists("pubchem_cache_results.json"): + with open("pubchem_cache_results.json", "r") as f: + cache_results = json.load(f) + else: + cache_results = {} + else: + cache_results = {} + + c = 0 + for idx in tqdm(identifiers, desc="Mapping PubChem"): + if idx in cache_results: + cid = cache_results[idx] + else: + c += 1 + if c == 100: + if cache_res: + with open("pubchem_cache_results.json", "w") as f: + json.dump(cache_results, f) + time.sleep(5) + c = 0 + + cid = get_cid_from_pugrest(idx, identifier_type) + cache_results[idx] = cid + cid_data.append( { "identifier": idx, From ddded6b8eb173e546f3e45aa001f0f2f3ba8d89f Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Mon, 14 Oct 2024 11:48:35 +0200 Subject: [PATCH 06/10] update usecase --- examples/usecases/PCS/PCS_usecase.ipynb | 3360 ------------------ examples/usecases/PCS/data/PCS_gene_list.csv | 2355 ++++++++++++ examples/usecases/PCS/graph_algorithm.ipynb | 98 + examples/usecases/PCS/graph_generation.ipynb | 1838 ++++++++++ examples/usecases/PCS/graph_summary.ipynb | 2176 ++++++++++++ 5 files changed, 6467 insertions(+), 3360 deletions(-) delete mode 100644 examples/usecases/PCS/PCS_usecase.ipynb create mode 100644 examples/usecases/PCS/data/PCS_gene_list.csv create mode 100644 examples/usecases/PCS/graph_algorithm.ipynb create mode 100644 examples/usecases/PCS/graph_generation.ipynb create mode 100644 examples/usecases/PCS/graph_summary.ipynb diff --git a/examples/usecases/PCS/PCS_usecase.ipynb b/examples/usecases/PCS/PCS_usecase.ipynb deleted file mode 100644 index 73994dc8..00000000 --- a/examples/usecases/PCS/PCS_usecase.ipynb +++ /dev/null @@ -1,3360 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Example: PCS use case\n", - "\n", - "This notebook shows all the steps to generate PCS KG and the downstream analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Set up the environment" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "new_path = \"E:\\BioDataFuse\\pyBiodatafuse\"\n", - "\n", - "import os\n", - "\n", - "os.chdir(new_path)\n", - "\n", - "# Set the current working directory\n", - "current_dir = os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Import modules\n", - "import pickle\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import networkx as nx\n", - "import numpy as np\n", - "import pandas as pd\n", - "from dotenv import load_dotenv\n", - "\n", - "from pyBiodatafuse import id_mapper\n", - "from pyBiodatafuse.annotators import disgenet, minerva, opentargets, stringdb, wikipathways\n", - "from pyBiodatafuse.constants import (\n", - " DISGENET_DISEASE_COL,\n", - " MINERVA,\n", - " OPENTARGETS_DISEASE_COMPOUND_COL,\n", - " OPENTARGETS_GENE_COMPOUND_COL,\n", - " OPENTARGETS_GO_COL,\n", - " OPENTARGETS_REACTOME_COL,\n", - " STRING_PPI_COL,\n", - " WIKIPATHWAYS,\n", - ")\n", - "from pyBiodatafuse.graph import generator\n", - "from pyBiodatafuse.utils import (\n", - " combine_sources,\n", - " create_harmonized_input_file,\n", - " create_or_append_to_metadata,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load the input list and convert it to a dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total number of genes: 2023\n" - ] - }, - { - "data": { - "text/html": [ - "
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identifier
0LOC729609
1LOC105374060
2DMP1
3PNLIP
4OR4N3P
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" - ], - "text/plain": [ - " identifier\n", - "0 LOC729609\n", - "1 LOC105374060\n", - "2 DMP1\n", - "3 PNLIP\n", - "4 OR4N3P" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_input = pd.read_csv(os.path.join(os.getcwd(), r\"examples\\usecases\\PCS\\PCS_gene_list.csv\"))\n", - "print(\"Total number of genes:\", len(data_input.drop_duplicates()))\n", - "data_input.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Entity resolution using BridgeDB" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of genes with mapping in BridgeDb: 1667\n" - ] - }, - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.source
0DMP1HGNCQ13316Uniprot-TrEMBL
1DMP1HGNCHGNC:2932HGNC Accession Number
2DMP1HGNCDMP1HGNC
3DMP1HGNCENSG00000152592Ensembl
4DMP1HGNC1758NCBI Gene
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" - ], - "text/plain": [ - " identifier identifier.source target target.source\n", - "0 DMP1 HGNC Q13316 Uniprot-TrEMBL\n", - "1 DMP1 HGNC HGNC:2932 HGNC Accession Number\n", - "2 DMP1 HGNC DMP1 HGNC\n", - "3 DMP1 HGNC ENSG00000152592 Ensembl\n", - "4 DMP1 HGNC 1758 NCBI Gene" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# bridgedb_df, bridgedb_metadata = id_mapper.bridgedb_xref(\n", - "# identifiers=data_input,\n", - "# input_species=\"Human\",\n", - "# input_datasource=\"HGNC\",\n", - "# output_datasource=\"All\",\n", - "# )\n", - "# bridgedb_df.to_pickle(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"bridgedb_df.pkl\"))\n", - "# with open(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"bridgedb_metadata.pkl\"), \"wb\") as file:\n", - "# pickle.dump(bridgedb_metadata, file)\n", - "with open(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"bridgedb_df.pkl\"), \"rb\"\n", - ") as file:\n", - " bridgedb_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"bridgedb_metadata.pkl\"), \"rb\"\n", - ") as file:\n", - " bridgedb_metadata = pickle.load(file)\n", - "\n", - "print(\"Number of genes with mapping in BridgeDb:\", len(bridgedb_df[\"identifier\"].unique()))\n", - "bridgedb_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gene to Disease annotatation from DisGeNet\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**ADD your DISGENET API KEY in the main folder**\n", - "\n", - " **1)** Create a ``.env`` file and add DISGENET_API_KEY to it:\n", - "\n", - " DISGENET_API_KEY=\"your-API-key-value\"\n", - "\n", - " **2)** Install *python-dotenv*:\n", - " \n", - " ```\n", - " pip install python-dotenv\n", - " ```" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# Read the .env File\n", - "load_dotenv()\n", - "# Retrieve the key from the environment variable\n", - "disgenet_api_key = os.getenv(\"DISGENET_API_KEY\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceDISGENET_diseases
0A2ML1HGNC144568NCBI Gene[{'disease_name': 'Noonan Syndrome', 'HPO': ''...
1AAMDCHGNC28971NCBI Gene[{'disease_name': nan, 'HPO': nan, 'NCI': nan,...
2ABCA1HGNC19NCBI Gene[{'disease_name': 'Tangier Disease', 'HPO': ''...
3ABCB1HGNC5243NCBI Gene[{'disease_name': 'Epilepsy', 'HPO': 'HPO_HP:0...
4ABCC6P1HGNC653190NCBI Gene[{'disease_name': nan, 'HPO': nan, 'NCI': nan,...
\n", - "
" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 A2ML1 HGNC 144568 NCBI Gene \n", - "1 AAMDC HGNC 28971 NCBI Gene \n", - "2 ABCA1 HGNC 19 NCBI Gene \n", - "3 ABCB1 HGNC 5243 NCBI Gene \n", - "4 ABCC6P1 HGNC 653190 NCBI Gene \n", - "\n", - " DISGENET_diseases \n", - "0 [{'disease_name': 'Noonan Syndrome', 'HPO': ''... \n", - "1 [{'disease_name': nan, 'HPO': nan, 'NCI': nan,... \n", - "2 [{'disease_name': 'Tangier Disease', 'HPO': ''... \n", - "3 [{'disease_name': 'Epilepsy', 'HPO': 'HPO_HP:0... \n", - "4 [{'disease_name': nan, 'HPO': nan, 'NCI': nan,... " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# disgenet_df, disgenet_metadata = disgenet.get_gene_disease(\n", - "# api_key=disgenet_api_key, bridgedb_df=bridgedb_df\n", - "# )\n", - "# disgenet_df.to_pickle(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"disgenet_df.pkl\"))\n", - "# with open(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"disgenet_metadata.pkl\"), \"wb\") as file:\n", - "# pickle.dump(disgenet_metadata, file)\n", - "with open(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"disgenet_df.pkl\"), \"rb\"\n", - ") as file:\n", - " disgenet_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"disgenet_metadata.pkl\"\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " disgenet_metadata = pickle.load(file)\n", - "\n", - "disgenet_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'disease_name': 'Noonan Syndrome',\n", - " 'HPO': '',\n", - " 'NCI': 'NCI_C34854',\n", - " 'OMIM': 'OMIM_163950, OMIM_176876',\n", - " 'MONDO': 'MONDO_0018997',\n", - " 'ORDO': 'ORDO_648',\n", - " 'EFO': '',\n", - " 'DO': 'DO_0060254, DO_11983, DO_11725, DO_2962, DO_14681, DO_3490, DO_14796, DO_6683',\n", - " 'MESH': 'MESH_D009634',\n", - " 'UMLS': 'UMLS_C0028326',\n", - " 'disease_type': 'disease',\n", - " 'disease_umlscui': 'C0028326',\n", - " 'score': 0.7,\n", - " 'ei': 0.8333333333333334,\n", - " 'el': 'Disputed'},\n", - " {'disease_name': 'Otitis Media',\n", - " 'HPO': 'HPO_HP:0000388',\n", - " 'NCI': 'NCI_C34885',\n", - " 'OMIM': '',\n", - " 'MONDO': 'MONDO_0005441',\n", - " 'ORDO': '',\n", - " 'EFO': 'EFO_0004992',\n", - " 'DO': 'DO_10754',\n", - " 'MESH': 'MESH_D010033',\n", - " 'UMLS': 'UMLS_C0029882',\n", - " 'disease_type': 'disease',\n", - " 'disease_umlscui': 'C0029882',\n", - " 'score': 0.65,\n", - " 'ei': 1.0,\n", - " 'el': None},\n", - " {'disease_name': 'Noonan Syndrome 1',\n", - " 'HPO': '',\n", - " 'NCI': 'NCI_C75459',\n", - " 'OMIM': 'OMIM_176876, OMIM_163950',\n", - " 'MONDO': 'MONDO_0008104, MONDO_0018997',\n", - " 'ORDO': 'ORDO_648',\n", - " 'EFO': '',\n", - " 'DO': 'DO_0060578, DO_3490',\n", - " 'MESH': 'MESH_D009634',\n", - " 'UMLS': 'UMLS_C4551602',\n", - " 'disease_type': 'disease',\n", - " 'disease_umlscui': 'C4551602',\n", - " 'score': 0.4,\n", - " 'ei': 1.0,\n", - " 'el': None},\n", - " {'disease_name': 'OTITIS MEDIA, SUSCEPTIBILITY TO',\n", - " 'HPO': '',\n", - " 'NCI': '',\n", - " 'OMIM': 'OMIM_166760, OMIM_610627',\n", - " 'MONDO': 'MONDO_0008162',\n", - " 'ORDO': '',\n", - " 'EFO': '',\n", - " 'DO': '',\n", - " 'MESH': '',\n", - " 'UMLS': 'UMLS_C1833692',\n", - " 'disease_type': 'phenotype',\n", - " 'disease_umlscui': 'C1833692',\n", - " 'score': 0.4,\n", - " 'ei': 1.0,\n", - " 'el': None}]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "disgenet_df[DISGENET_DISEASE_COL][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Add literature-based data\n", - "Genes found to be associated with Post-COVID-19" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Gene
0CTLA4
1PTPN22
2KIT
3KRAS
4NF1
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" - ], - "text/plain": [ - " Gene\n", - "0 CTLA4\n", - "1 PTPN22\n", - "2 KIT\n", - "3 KRAS\n", - "4 NF1" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pcs_associated_genes = pd.read_excel(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"pcs_associated_genes.xlsx\")\n", - ")\n", - "pcs_associated_genes.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Define the literature based info" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceDISGENET_diseasesliterature_based_info
0A2ML1HGNC144568NCBI Gene[{'disease_name': 'Noonan Syndrome', 'HPO': ''...[{'disease_name': nan, 'id': nan, 'source': nan}]
1AAMDCHGNC28971NCBI Gene[{'disease_name': nan, 'HPO': nan, 'NCI': nan,...[{'disease_name': nan, 'id': nan, 'source': nan}]
2ABCA1HGNC19NCBI Gene[{'disease_name': 'Tangier Disease', 'HPO': ''...[{'disease_name': nan, 'id': nan, 'source': nan}]
3ABCB1HGNC5243NCBI Gene[{'disease_name': 'Epilepsy', 'HPO': 'HPO_HP:0...[{'disease_name': nan, 'id': nan, 'source': nan}]
4ABCC6P1HGNC653190NCBI Gene[{'disease_name': nan, 'HPO': nan, 'NCI': nan,...[{'disease_name': nan, 'id': nan, 'source': nan}]
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" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 A2ML1 HGNC 144568 NCBI Gene \n", - "1 AAMDC HGNC 28971 NCBI Gene \n", - "2 ABCA1 HGNC 19 NCBI Gene \n", - "3 ABCB1 HGNC 5243 NCBI Gene \n", - "4 ABCC6P1 HGNC 653190 NCBI Gene \n", - "\n", - " DISGENET_diseases \\\n", - "0 [{'disease_name': 'Noonan Syndrome', 'HPO': ''... \n", - "1 [{'disease_name': nan, 'HPO': nan, 'NCI': nan,... \n", - "2 [{'disease_name': 'Tangier Disease', 'HPO': ''... \n", - "3 [{'disease_name': 'Epilepsy', 'HPO': 'HPO_HP:0... \n", - "4 [{'disease_name': nan, 'HPO': nan, 'NCI': nan,... \n", - "\n", - " literature_based_info \n", - "0 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "1 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "2 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "3 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "4 [{'disease_name': nan, 'id': nan, 'source': nan}] " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pyBiodatafuse.constants import LITERATURE_DISEASE_COL, LITERATURE_DISEASE_OUTPUT_DICT\n", - "\n", - "literature_disease_attrs = LITERATURE_DISEASE_OUTPUT_DICT.copy()\n", - "literature_disease_attrs[\"disease_name\"] = \"Post-COVID-19\"\n", - "literature_disease_attrs[\"id\"] = \"C00000\"\n", - "literature_disease_attrs[\"source\"] = \"PMID: 37675861\"\n", - "\n", - "\n", - "def get_literature_based_info(gene):\n", - " if gene in pcs_associated_genes[\"Gene\"].values:\n", - " return [literature_disease_attrs]\n", - " else:\n", - " return [{\"disease_name\": np.nan, \"id\": np.nan, \"source\": np.nan}]\n", - "\n", - "\n", - "disgenet_df[LITERATURE_DISEASE_COL] = disgenet_df[\"identifier\"].apply(get_literature_based_info)\n", - "\n", - "disgenet_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "362 [{'disease_name': 'Post-COVID-19', 'id': 'C000...\n", - "Name: literature_based_info, dtype: object" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "disgenet_df[disgenet_df[\"identifier\"] == \"DMP1\"][LITERATURE_DISEASE_COL]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "29\n" - ] - } - ], - "source": [ - "print(pcs_associated_genes[\"Gene\"].isin(disgenet_df[\"identifier\"]).sum())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Disease to Compound annotation from OpenTargets" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Prepare the input to use DISGENET output as seed for OpenTargets\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.source
0UMLS_C0029882UMLSEFO_0004992EFO
1UMLS_C0004153UMLSEFO_0003914EFO
2UMLS_C0004153UMLSEFO_1000819EFO
3UMLS_C0342898UMLSEFO_0700136EFO
4UMLS_C0010054UMLSEFO_0001645EFO
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" - ], - "text/plain": [ - " identifier identifier.source target target.source\n", - "0 UMLS_C0029882 UMLS EFO_0004992 EFO\n", - "1 UMLS_C0004153 UMLS EFO_0003914 EFO\n", - "2 UMLS_C0004153 UMLS EFO_1000819 EFO\n", - "3 UMLS_C0342898 UMLS EFO_0700136 EFO\n", - "4 UMLS_C0010054 UMLS EFO_0001645 EFO" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "disease_mapping_df = create_harmonized_input_file(disgenet_df, DISGENET_DISEASE_COL, \"EFO\", \"UMLS\")\n", - "disease_mapping_df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Disease to Compound annotation\n", - "\n", - "TODO: to run again." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceOpenTargets_disease_compounds
0UMLS_C0000786UMLSEFO_1001255EFO[{'chembl_id': 'CHEMBL1276308', 'drugbank_id':...
1UMLS_C0000889UMLSEFO_1000660EFO[{'chembl_id': 'CHEMBL1431', 'drugbank_id': 'D...
2UMLS_C0001125UMLSEFO_1000036EFO[{'chembl_id': 'CHEMBL306823', 'drugbank_id': ...
3UMLS_C0001175UMLSEFO_0000765EFO[{'chembl_id': 'CHEMBL704', 'drugbank_id': 'DB...
4UMLS_C0001306UMLSEFO_1001345EFO[{'chembl_id': 'CHEMBL628', 'drugbank_id': 'DB...
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" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 UMLS_C0000786 UMLS EFO_1001255 EFO \n", - "1 UMLS_C0000889 UMLS EFO_1000660 EFO \n", - "2 UMLS_C0001125 UMLS EFO_1000036 EFO \n", - "3 UMLS_C0001175 UMLS EFO_0000765 EFO \n", - "4 UMLS_C0001306 UMLS EFO_1001345 EFO \n", - "\n", - " OpenTargets_disease_compounds \n", - "0 [{'chembl_id': 'CHEMBL1276308', 'drugbank_id':... \n", - "1 [{'chembl_id': 'CHEMBL1431', 'drugbank_id': 'D... \n", - "2 [{'chembl_id': 'CHEMBL306823', 'drugbank_id': ... \n", - "3 [{'chembl_id': 'CHEMBL704', 'drugbank_id': 'DB... \n", - "4 [{'chembl_id': 'CHEMBL628', 'drugbank_id': 'DB... " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# (\n", - "# opentargets_disease_compound_df,\n", - "# opentargets_disease_compound_metadata,\n", - "# ) = opentargets.get_disease_compound_interactions(disease_mapping_df)\n", - "\n", - "# opentargets_disease_compound_df.to_pickle(\n", - "# os.path.join(\n", - "# os.getcwd(),\n", - "# \"examples\",\n", - "# \"usecases\",\n", - "# \"PCS\",\n", - "# \"datasources\",\n", - "# \"opentargets_disease_compound_df.pkl\",\n", - "# )\n", - "# )\n", - "# with open(\n", - "# os.path.join(\n", - "# os.getcwd(),\n", - "# \"examples\",\n", - "# \"usecases\",\n", - "# \"PCS\",\n", - "# \"datasources\",\n", - "# \"opentargets_disease_compound_metadata.pkl\",\n", - "# ),\n", - "# \"wb\",\n", - "# ) as file:\n", - "# pickle.dump(opentargets_disease_compound_metadata, file)\n", - "\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(),\n", - " \"examples\",\n", - " \"usecases\",\n", - " \"PCS\",\n", - " \"datasources\",\n", - " \"opentargets_disease_compound_df.pkl\",\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " opentargets_disease_compound_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(),\n", - " \"examples\",\n", - " \"usecases\",\n", - " \"PCS\",\n", - " \"datasources\",\n", - " \"opentargets_disease_compound_metadata.pkl\",\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " opentargets_disease_compound_metadata = pickle.load(file)\n", - "opentargets_disease_compound_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'chembl_id': 'CHEMBL1276308',\n", - " 'drugbank_id': 'DB00834',\n", - " 'compound_cid': nan,\n", - " 'compound_name': 'MIFEPRISTONE',\n", - " 'clincal_trial_phase': 4.0,\n", - " 'is_approved': True,\n", - " 'relation': 'treats',\n", - " 'adverse_effect_count': 35.0,\n", - " 'adverse_effect': [{'name': 'abortion incomplete'},\n", - " {'name': 'haemorrhage'},\n", - " {'name': 'pregnancy'},\n", - " {'name': 'endometritis'},\n", - " {'name': 'induced abortion failed'},\n", - " {'name': 'vaginal haemorrhage'},\n", - " {'name': 'anaemia'},\n", - " {'name': 'muscle spasms'},\n", - " {'name': 'metrorrhagia'},\n", - " {'name': 'abortion induced incomplete'},\n", - " {'name': 'menorrhagia'},\n", - " {'name': 'pain'},\n", - " {'name': 'uterine haemorrhage'},\n", - " {'name': 'post abortion infection'},\n", - " {'name': 'uterine rupture'},\n", - " {'name': 'ectopic pregnancy'},\n", - " {'name': 'blood potassium decreased'},\n", - " {'name': 'syncope'},\n", - " {'name': 'endometritis bacterial'},\n", - " {'name': 'pelvic inflammatory disease'},\n", - " {'name': 'uterine dilation and curettage'},\n", - " {'name': 'haemorrhagic anaemia'},\n", - " {'name': 'retained products of conception'},\n", - " {'name': 'endometrial thickening'},\n", - " {'name': 'ruptured ectopic pregnancy'}]},\n", - " {'chembl_id': 'CHEMBL606',\n", - " 'drugbank_id': 'DB00929',\n", - " 'compound_cid': '5282381',\n", - " 'compound_name': 'MISOPROSTOL',\n", - " 'clincal_trial_phase': 4.0,\n", - " 'is_approved': True,\n", - " 'relation': 'treats',\n", - " 'adverse_effect_count': 34.0,\n", - " 'adverse_effect': [{'name': 'abortion incomplete'},\n", - " {'name': 'haemorrhage'},\n", - " {'name': 'pregnancy'},\n", - " {'name': 'endometritis'},\n", - " {'name': 'menorrhagia'},\n", - " {'name': 'anaemia'},\n", - " {'name': 'muscle spasms'},\n", - " {'name': 'induced abortion failed'},\n", - " {'name': 'international normalised ratio fluctuation'},\n", - " {'name': 'drug hypersensitivity'},\n", - " {'name': 'pain'},\n", - " {'name': 'rheumatoid arthritis'},\n", - " {'name': 'bloody discharge'},\n", - " {'name': 'infection'},\n", - " {'name': 'uterine rupture'},\n", - " {'name': 'ectopic pregnancy'},\n", - " {'name': 'pelvic inflammatory disease'},\n", - " {'name': 'vaginal haemorrhage'},\n", - " {'name': 'metrorrhagia'},\n", - " {'name': 'abdominal pain'},\n", - " {'name': 'retained products of conception'},\n", - " {'name': 'foetal distress syndrome'},\n", - " {'name': 'syncope'},\n", - " {'name': 'post abortion infection'},\n", - " {'name': 'abortion induced incomplete'}]},\n", - " {'chembl_id': 'CHEMBL940',\n", - " 'drugbank_id': 'DB00996',\n", - " 'compound_cid': nan,\n", - " 'compound_name': 'GABAPENTIN',\n", - " 'clincal_trial_phase': 4.0,\n", - " 'is_approved': True,\n", - " 'relation': 'treats',\n", - " 'adverse_effect_count': 13.0,\n", - " 'adverse_effect': [{'name': 'drug hypersensitivity'},\n", - " {'name': 'somnolence'},\n", - " {'name': 'myoclonus'},\n", - " {'name': 'drug abuse'},\n", - " {'name': 'confusional state'},\n", - " {'name': 'sedation'},\n", - " {'name': 'dizziness'},\n", - " {'name': 'drug titration error'},\n", - " {'name': 'toxicity to various agents'},\n", - " {'name': 'suicidal ideation'},\n", - " {'name': 'presbyacusis'},\n", - " {'name': 'feeling abnormal'},\n", - " {'name': 'drug withdrawal syndrome neonatal'}]}]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "opentargets_disease_compound_df[OPENTARGETS_DISEASE_COMPOUND_COL][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gene to Compound annotation from OpenTarget" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceOpenTargets_gene_compounds
0A2ML1HGNCENSG00000166535Ensembl[{'chembl_id': nan, 'drugbank_id': nan, 'compo...
1AAMDCHGNCENSG00000087884Ensembl[{'chembl_id': nan, 'drugbank_id': nan, 'compo...
2ABCA1HGNCENSG00000165029Ensembl[{'chembl_id': 'CHEMBL608', 'drugbank_id': 'DB...
3ABCB1HGNCENSG00000085563Ensembl[{'chembl_id': 'CHEMBL1086218', 'drugbank_id':...
4ABCC13HGNCENSG00000243064Ensembl[{'chembl_id': nan, 'drugbank_id': nan, 'compo...
\n", - "
" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 A2ML1 HGNC ENSG00000166535 Ensembl \n", - "1 AAMDC HGNC ENSG00000087884 Ensembl \n", - "2 ABCA1 HGNC ENSG00000165029 Ensembl \n", - "3 ABCB1 HGNC ENSG00000085563 Ensembl \n", - "4 ABCC13 HGNC ENSG00000243064 Ensembl \n", - "\n", - " OpenTargets_gene_compounds \n", - "0 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", - "1 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", - "2 [{'chembl_id': 'CHEMBL608', 'drugbank_id': 'DB... \n", - "3 [{'chembl_id': 'CHEMBL1086218', 'drugbank_id':... \n", - "4 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# opentargets_compound_df, opentargets_compound_metadata = opentargets.get_gene_compound_interactions(\n", - "# bridgedb_df=bridgedb_df\n", - "# )\n", - "\n", - "# opentargets_compound_df.to_pickle(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_compound_df.pkl\"))\n", - "# with open(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_compound_metadata.pkl\"), \"wb\") as file:\n", - "# pickle.dump(opentargets_compound_metadata, file)\n", - "\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_compound_df.pkl\"\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " opentargets_compound_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(),\n", - " \"examples\",\n", - " \"usecases\",\n", - " \"PCS\",\n", - " \"datasources\",\n", - " \"opentargets_compound_metadata.pkl\",\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " opentargets_compound_metadata = pickle.load(file)\n", - "\n", - "opentargets_compound_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'chembl_id': 'CHEMBL1086218',\n", - " 'drugbank_id': 'DB11869',\n", - " 'compound_cid': '5281884',\n", - " 'compound_name': 'VALSPODAR',\n", - " 'clincal_trial_phase': 3.0,\n", - " 'is_approved': False,\n", - " 'relation': 'activates',\n", - " 'adverse_effect_count': nan,\n", - " 'adverse_effect': None},\n", - " {'chembl_id': 'CHEMBL444172',\n", - " 'drugbank_id': 'DB06191',\n", - " 'compound_cid': '3036703',\n", - " 'compound_name': 'ZOSUQUIDAR',\n", - " 'clincal_trial_phase': 3.0,\n", - " 'is_approved': False,\n", - " 'relation': 'activates',\n", - " 'adverse_effect_count': nan,\n", - " 'adverse_effect': None},\n", - " {'chembl_id': 'CHEMBL348475',\n", - " 'drugbank_id': 'DB06240',\n", - " 'compound_cid': '148201',\n", - " 'compound_name': 'TARIQUIDAR',\n", - " 'clincal_trial_phase': 3.0,\n", - " 'is_approved': False,\n", - " 'relation': 'activates',\n", - " 'adverse_effect_count': 2.0,\n", - " 'adverse_effect': [{'name': 'breast cancer female'},\n", - " {'name': 'malignant neoplasm progression'}]},\n", - " {'chembl_id': 'CHEMBL4594298',\n", - " 'drugbank_id': None,\n", - " 'compound_cid': '11399764',\n", - " 'compound_name': 'ENCEQUIDAR',\n", - " 'clincal_trial_phase': 3.0,\n", - " 'is_approved': False,\n", - " 'relation': 'activates',\n", - " 'adverse_effect_count': nan,\n", - " 'adverse_effect': None}]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "opentargets_compound_df[OPENTARGETS_GENE_COMPOUND_COL][3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gene to Pathway annotation from MINERVA" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceMINERVA
0A2ML1HGNCENSG00000166535Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
1AAMDCHGNCENSG00000087884Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
2ABCA1HGNCENSG00000165029Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
3ABCB1HGNCENSG00000085563Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
4ABCC13HGNCENSG00000243064Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
\n", - "
" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 A2ML1 HGNC ENSG00000166535 Ensembl \n", - "1 AAMDC HGNC ENSG00000087884 Ensembl \n", - "2 ABCA1 HGNC ENSG00000165029 Ensembl \n", - "3 ABCB1 HGNC ENSG00000085563 Ensembl \n", - "4 ABCC13 HGNC ENSG00000243064 Ensembl \n", - "\n", - " MINERVA \n", - "0 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "1 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "2 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "3 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "4 [{'pathway_id': nan, 'pathway_label': nan, 'pa... " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# minerva_df, minerva_metadata = minerva.get_gene_minerva_pathways(\n", - "# bridgedb_df, map_name=\"COVID19 Disease Map\"\n", - "# )\n", - "# minerva_df.to_pickle(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"minerva_df.pkl\"))\n", - "# with open(\n", - "# os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"minerva_metadata.pkl\"), \"wb\"\n", - "# ) as file:\n", - "# pickle.dump(minerva_metadata, file)\n", - "\n", - "with open(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"minerva_df.pkl\"), \"rb\"\n", - ") as file:\n", - " minerva_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"minerva_metadata.pkl\"),\n", - " \"rb\",\n", - ") as file:\n", - " minerva_metadata = pickle.load(file)\n", - "minerva_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'pathway_id': 953.0,\n", - " 'pathway_label': 'Kynurenine synthesis pathway',\n", - " 'pathway_gene_count': 45.0}]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "minerva_df[MINERVA][33]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gene to Pathway annotation from WikiPathways" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceWikiPathways
0A2ML1HGNC144568NCBI Gene[{'pathway_id': nan, 'pathway_label': nan, 'pa...
1AAMDCHGNC28971NCBI Gene[{'pathway_id': nan, 'pathway_label': nan, 'pa...
2ABCA1HGNC19NCBI Gene[{'pathway_id': 'WP4718', 'pathway_label': 'Ch...
3ABCB1HGNC5243NCBI Gene[{'pathway_id': 'WP3672', 'pathway_label': 'ln...
4ABCC6P1HGNC653190NCBI Gene[{'pathway_id': nan, 'pathway_label': nan, 'pa...
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" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 A2ML1 HGNC 144568 NCBI Gene \n", - "1 AAMDC HGNC 28971 NCBI Gene \n", - "2 ABCA1 HGNC 19 NCBI Gene \n", - "3 ABCB1 HGNC 5243 NCBI Gene \n", - "4 ABCC6P1 HGNC 653190 NCBI Gene \n", - "\n", - " WikiPathways \n", - "0 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "1 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "2 [{'pathway_id': 'WP4718', 'pathway_label': 'Ch... \n", - "3 [{'pathway_id': 'WP3672', 'pathway_label': 'ln... \n", - "4 [{'pathway_id': nan, 'pathway_label': nan, 'pa... " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# wikipathways_df, wikipathways_metadata = wikipathways.get_gene_wikipathways(bridgedb_df=bridgedb_df)\n", - "# wikipathways_df.to_pickle(\n", - "# os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"wikipathways_df.pkl\")\n", - "# )\n", - "# with open(\n", - "# os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"wikipathways_metadata.pkl\"), \"wb\"\n", - "# ) as file:\n", - "# pickle.dump(wikipathways_metadata, file)\n", - "\n", - "with open(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"wikipathways_df.pkl\"),\n", - " \"rb\",\n", - ") as file:\n", - " wikipathways_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"wikipathways_metadata.pkl\"\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " wikipathways_metadata = pickle.load(file)\n", - "wikipathways_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'pathway_id': 'WP3672',\n", - " 'pathway_label': 'lncRNA-mediated mechanisms of therapeutic resistance',\n", - " 'pathway_gene_count': 7.0},\n", - " {'pathway_id': 'WP2876',\n", - " 'pathway_label': 'Pregnane X receptor pathway',\n", - " 'pathway_gene_count': 33.0},\n", - " {'pathway_id': 'WP4917',\n", - " 'pathway_label': 'Proximal tubule transport',\n", - " 'pathway_gene_count': 57.0},\n", - " {'pathway_id': 'WP4673',\n", - " 'pathway_label': 'Male infertility',\n", - " 'pathway_gene_count': 145.0},\n", - " {'pathway_id': 'WP2328',\n", - " 'pathway_label': 'Allograft rejection',\n", - " 'pathway_gene_count': 102.0},\n", - " {'pathway_id': 'WP3640',\n", - " 'pathway_label': 'Imatinib and chronic myeloid leukemia',\n", - " 'pathway_gene_count': 20.0},\n", - " {'pathway_id': 'WP2882',\n", - " 'pathway_label': 'Nuclear receptors meta-pathway',\n", - " 'pathway_gene_count': 318.0},\n", - " {'pathway_id': 'WP1604',\n", - " 'pathway_label': 'Codeine and morphine metabolism',\n", - " 'pathway_gene_count': 17.0},\n", - " {'pathway_id': 'WP2289',\n", - " 'pathway_label': 'Drug induction of bile acid pathway',\n", - " 'pathway_gene_count': 17.0},\n", - " {'pathway_id': 'WP2877',\n", - " 'pathway_label': 'Vitamin D receptor pathway',\n", - " 'pathway_gene_count': 186.0},\n", - " {'pathway_id': 'WP2875',\n", - " 'pathway_label': 'Constitutive androstane receptor pathway',\n", - " 'pathway_gene_count': 32.0},\n", - " {'pathway_id': 'WP299',\n", - " 'pathway_label': 'Nuclear receptors in lipid metabolism and toxicity',\n", - " 'pathway_gene_count': 35.0}]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wikipathways_df[WIKIPATHWAYS][3]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gene to Reactome Pathway from OpenTargets" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceOpenTargets_reactome
0A2ML1HGNCENSG00000166535Ensembl[{'pathway_label': nan, 'pathway_id': nan}]
1AAMDCHGNCENSG00000087884Ensembl[{'pathway_label': nan, 'pathway_id': nan}]
2ABCA1HGNCENSG00000165029Ensembl[{'pathway_label': 'PPARA activates gene expre...
3ABCB1HGNCENSG00000085563Ensembl[{'pathway_label': 'Abacavir transmembrane tra...
4ABCC13HGNCENSG00000243064Ensembl[{'pathway_label': nan, 'pathway_id': nan}]
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" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 A2ML1 HGNC ENSG00000166535 Ensembl \n", - "1 AAMDC HGNC ENSG00000087884 Ensembl \n", - "2 ABCA1 HGNC ENSG00000165029 Ensembl \n", - "3 ABCB1 HGNC ENSG00000085563 Ensembl \n", - "4 ABCC13 HGNC ENSG00000243064 Ensembl \n", - "\n", - " OpenTargets_reactome \n", - "0 [{'pathway_label': nan, 'pathway_id': nan}] \n", - "1 [{'pathway_label': nan, 'pathway_id': nan}] \n", - "2 [{'pathway_label': 'PPARA activates gene expre... \n", - "3 [{'pathway_label': 'Abacavir transmembrane tra... \n", - "4 [{'pathway_label': nan, 'pathway_id': nan}] " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# opentargets_reactome_df, opentargets_reactome_metadata = opentargets.get_gene_reactome_pathways(\n", - "# bridgedb_df=bridgedb_df\n", - "# )\n", - "# opentargets_reactome_df.to_pickle(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_reactome_df.pkl\"))\n", - "# with open(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_reactome_metadata.pkl\"), \"wb\") as file:\n", - "# pickle.dump(opentargets_reactome_metadata, file)\n", - "\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_reactome_df.pkl\"\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " opentargets_reactome_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(),\n", - " \"examples\",\n", - " \"usecases\",\n", - " \"PCS\",\n", - " \"datasources\",\n", - " \"opentargets_reactome_metadata.pkl\",\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " opentargets_reactome_metadata = pickle.load(file)\n", - "\n", - "opentargets_reactome_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'pathway_label': 'PPARA activates gene expression',\n", - " 'pathway_id': 'R-HSA-1989781'},\n", - " {'pathway_label': 'Defective ABCA1 causes TGD',\n", - " 'pathway_id': 'R-HSA-5682113'},\n", - " {'pathway_label': 'NR1H3 & NR1H2 regulate gene expression linked to cholesterol transport and efflux',\n", - " 'pathway_id': 'R-HSA-9029569'},\n", - " {'pathway_label': 'HDL assembly', 'pathway_id': 'R-HSA-8963896'}]" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "opentargets_reactome_df[OPENTARGETS_REACTOME_COL][2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Gene Ontology annotation from OpenTargets" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceOpenTargets_go
0A2ML1HGNCENSG00000166535Ensembl[{'go_id': 'GO:0052548', 'go_name': 'regulatio...
1AAMDCHGNCENSG00000087884Ensembl[{'go_id': 'GO:0005737', 'go_name': 'cytoplasm...
2ABCA1HGNCENSG00000165029Ensembl[{'go_id': 'GO:0005524', 'go_name': 'ATP bindi...
3ABCB1HGNCENSG00000085563Ensembl[{'go_id': 'GO:0008559', 'go_name': 'ABC-type ...
4ABCC13HGNCENSG00000243064Ensembl[{'go_id': nan, 'go_name': nan, 'go_type': nan}]
\n", - "
" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 A2ML1 HGNC ENSG00000166535 Ensembl \n", - "1 AAMDC HGNC ENSG00000087884 Ensembl \n", - "2 ABCA1 HGNC ENSG00000165029 Ensembl \n", - "3 ABCB1 HGNC ENSG00000085563 Ensembl \n", - "4 ABCC13 HGNC ENSG00000243064 Ensembl \n", - "\n", - " OpenTargets_go \n", - "0 [{'go_id': 'GO:0052548', 'go_name': 'regulatio... \n", - "1 [{'go_id': 'GO:0005737', 'go_name': 'cytoplasm... \n", - "2 [{'go_id': 'GO:0005524', 'go_name': 'ATP bindi... \n", - "3 [{'go_id': 'GO:0008559', 'go_name': 'ABC-type ... \n", - "4 [{'go_id': nan, 'go_name': nan, 'go_type': nan}] " - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# opentargets_go_df, opentargets_go_metadata = opentargets.get_gene_go_process(bridgedb_df=bridgedb_df)\n", - "# opentargets_go_df.to_pickle(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_go_df.pkl\"))\n", - "# with open(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_go_metadata.pkl\"), \"wb\") as file:\n", - "# pickle.dump(opentargets_go_metadata, file)\n", - "\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_go_df.pkl\"\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " opentargets_go_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"opentargets_go_metadata.pkl\"\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " opentargets_go_metadata = pickle.load(file)\n", - "opentargets_go_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'go_id': 'GO:0052548',\n", - " 'go_name': 'regulation of endopeptidase activity',\n", - " 'go_type': 'P'},\n", - " {'go_id': 'GO:0070062', 'go_name': 'extracellular exosome', 'go_type': 'C'},\n", - " {'go_id': 'GO:0030414',\n", - " 'go_name': 'peptidase inhibitor activity',\n", - " 'go_type': 'F'},\n", - " {'go_id': 'GO:0005615', 'go_name': 'extracellular space', 'go_type': 'C'},\n", - " {'go_id': 'GO:0004867',\n", - " 'go_name': 'serine-type endopeptidase inhibitor activity',\n", - " 'go_type': 'F'}]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "opentargets_go_df[OPENTARGETS_GO_COL][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Protein-Protein interaction from STRING" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceStringDB_ppi
0DMP1HGNCENSG00000152592Ensembl[{'stringdb_link_to': 'TNFRSF11B', 'Ensembl': ...
1PNLIPHGNCENSG00000175535Ensembl[{'stringdb_link_to': 'LIPE', 'Ensembl': 'ENSP...
2OR4N3PHGNCENSG00000259435Ensembl[{'stringdb_link_to': nan, 'Ensembl': nan, 'sc...
3SLC6A14HGNCENSG00000268104Ensembl[{'stringdb_link_to': 'SLC7A11', 'Ensembl': 'E...
4DEFB105AHGNCENSG00000186562Ensembl[{'stringdb_link_to': 'DEFB118', 'Ensembl': 'E...
\n", - "
" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 DMP1 HGNC ENSG00000152592 Ensembl \n", - "1 PNLIP HGNC ENSG00000175535 Ensembl \n", - "2 OR4N3P HGNC ENSG00000259435 Ensembl \n", - "3 SLC6A14 HGNC ENSG00000268104 Ensembl \n", - "4 DEFB105A HGNC ENSG00000186562 Ensembl \n", - "\n", - " StringDB_ppi \n", - "0 [{'stringdb_link_to': 'TNFRSF11B', 'Ensembl': ... \n", - "1 [{'stringdb_link_to': 'LIPE', 'Ensembl': 'ENSP... \n", - "2 [{'stringdb_link_to': nan, 'Ensembl': nan, 'sc... \n", - "3 [{'stringdb_link_to': 'SLC7A11', 'Ensembl': 'E... \n", - "4 [{'stringdb_link_to': 'DEFB118', 'Ensembl': 'E... " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# string_ppi_df, string_ppi_metadata = stringdb.get_ppi(bridgedb_df=bridgedb_df)\n", - "# string_ppi_df.to_pickle(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"string_ppi_df.pkl\"))\n", - "# with open(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"string_ppi_metadata.pkl\"), \"wb\") as file:\n", - "# pickle.dump(string_ppi_metadata, file)\n", - "\n", - "with open(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"string_ppi_df.pkl\"),\n", - " \"rb\",\n", - ") as file:\n", - " string_ppi_df = pickle.load(file)\n", - "with open(\n", - " os.path.join(\n", - " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"datasources\", \"string_ppi_metadata.pkl\"\n", - " ),\n", - " \"rb\",\n", - ") as file:\n", - " string_ppi_metadata = pickle.load(file)\n", - "string_ppi_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'stringdb_link_to': 'TNFRSF11B',\n", - " 'Ensembl': 'ENSP00000297350',\n", - " 'score': 0.409},\n", - " {'stringdb_link_to': 'HSPA5', 'Ensembl': 'ENSP00000324173', 'score': 0.504},\n", - " {'stringdb_link_to': 'GAPDH', 'Ensembl': 'ENSP00000380070', 'score': 0.449},\n", - " {'stringdb_link_to': 'CD44', 'Ensembl': 'ENSP00000398632', 'score': 0.601},\n", - " {'stringdb_link_to': 'ENPP1', 'Ensembl': 'ENSP00000498074', 'score': 0.625},\n", - " {'stringdb_link_to': 'RUNX2', 'Ensembl': 'ENSP00000360493', 'score': 0.713}]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "string_ppi_df[STRING_PPI_COL][0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Combing all the results into single dataframe" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "combined_df = combine_sources(\n", - " bridgedb_df,\n", - " [\n", - " disgenet_df,\n", - " opentargets_compound_df,\n", - " minerva_df,\n", - " wikipathways_df,\n", - " opentargets_reactome_df,\n", - " opentargets_go_df,\n", - " string_ppi_df,\n", - " ],\n", - ")\n", - "combined_metadata = create_or_append_to_metadata(\n", - " bridgedb_metadata,\n", - " [\n", - " disgenet_metadata,\n", - " opentargets_disease_compound_metadata,\n", - " opentargets_compound_metadata,\n", - " minerva_metadata,\n", - " wikipathways_metadata,\n", - " opentargets_reactome_metadata,\n", - " opentargets_go_metadata,\n", - " string_ppi_metadata,\n", - " ],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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identifieridentifier.sourcetargettarget.sourceDISGENET_diseasesliterature_based_infoOpenTargets_gene_compoundsMINERVAWikiPathwaysOpenTargets_reactomeOpenTargets_goStringDB_ppi
0DMP1HGNCENSG00000152592Ensembl[{'disease_name': 'Hypophosphatemic Rickets', ...[{'disease_name': 'Post-COVID-19', 'id': 'C000...[{'chembl_id': nan, 'drugbank_id': nan, 'compo...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_id': 'WP3971', 'pathway_label': 'OS...[{'pathway_label': 'ECM proteoglycans', 'pathw...[{'go_id': 'GO:0005788', 'go_name': 'endoplasm...[{'stringdb_link_to': 'TNFRSF11B', 'Ensembl': ...
1PNLIPHGNCENSG00000175535Ensembl[{'disease_name': 'Pancreatic Lipase Deficienc...[{'disease_name': nan, 'id': nan, 'source': nan}][{'chembl_id': 'CHEMBL175247', 'drugbank_id': ...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_label': 'Retinoid metabolism and tr...[{'go_id': 'GO:0004806', 'go_name': 'triglycer...[{'stringdb_link_to': 'LIPE', 'Ensembl': 'ENSP...
2OR4N3PHGNCENSG00000259435Ensembl[{'disease_name': nan, 'HPO': nan, 'NCI': nan,...[{'disease_name': nan, 'id': nan, 'source': nan}][{'chembl_id': nan, 'drugbank_id': nan, 'compo...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_label': nan, 'pathway_id': nan}][{'go_id': nan, 'go_name': nan, 'go_type': nan}][{'stringdb_link_to': nan, 'Ensembl': nan, 'sc...
3SLC6A14HGNCENSG00000268104Ensembl[{'disease_name': 'Cystic Fibrosis', 'HPO': ''...[{'disease_name': nan, 'id': nan, 'source': nan}][{'chembl_id': nan, 'drugbank_id': nan, 'compo...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_id': 'WP2882', 'pathway_label': 'Nu...[{'pathway_label': 'Amino acid transport acros...[{'go_id': 'GO:0015657', 'go_name': 'branched-...[{'stringdb_link_to': 'SLC7A11', 'Ensembl': 'E...
\n", - "
" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "0 DMP1 HGNC ENSG00000152592 Ensembl \n", - "1 PNLIP HGNC ENSG00000175535 Ensembl \n", - "2 OR4N3P HGNC ENSG00000259435 Ensembl \n", - "3 SLC6A14 HGNC ENSG00000268104 Ensembl \n", - "\n", - " DISGENET_diseases \\\n", - "0 [{'disease_name': 'Hypophosphatemic Rickets', ... \n", - "1 [{'disease_name': 'Pancreatic Lipase Deficienc... \n", - "2 [{'disease_name': nan, 'HPO': nan, 'NCI': nan,... \n", - "3 [{'disease_name': 'Cystic Fibrosis', 'HPO': ''... \n", - "\n", - " literature_based_info \\\n", - "0 [{'disease_name': 'Post-COVID-19', 'id': 'C000... \n", - "1 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "2 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "3 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "\n", - " OpenTargets_gene_compounds \\\n", - "0 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", - "1 [{'chembl_id': 'CHEMBL175247', 'drugbank_id': ... \n", - "2 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", - "3 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", - "\n", - " MINERVA \\\n", - "0 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "1 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "2 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "3 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "\n", - " WikiPathways \\\n", - "0 [{'pathway_id': 'WP3971', 'pathway_label': 'OS... \n", - "1 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "2 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "3 [{'pathway_id': 'WP2882', 'pathway_label': 'Nu... \n", - "\n", - " OpenTargets_reactome \\\n", - "0 [{'pathway_label': 'ECM proteoglycans', 'pathw... \n", - "1 [{'pathway_label': 'Retinoid metabolism and tr... \n", - "2 [{'pathway_label': nan, 'pathway_id': nan}] \n", - "3 [{'pathway_label': 'Amino acid transport acros... \n", - "\n", - " OpenTargets_go \\\n", - "0 [{'go_id': 'GO:0005788', 'go_name': 'endoplasm... \n", - "1 [{'go_id': 'GO:0004806', 'go_name': 'triglycer... \n", - "2 [{'go_id': nan, 'go_name': nan, 'go_type': nan}] \n", - "3 [{'go_id': 'GO:0015657', 'go_name': 'branched-... \n", - "\n", - " StringDB_ppi \n", - "0 [{'stringdb_link_to': 'TNFRSF11B', 'Ensembl': ... \n", - "1 [{'stringdb_link_to': 'LIPE', 'Ensembl': 'ENSP... \n", - "2 [{'stringdb_link_to': nan, 'Ensembl': nan, 'sc... \n", - "3 [{'stringdb_link_to': 'SLC7A11', 'Ensembl': 'E... " - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_df.head(4)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'disease_name': 'Post-COVID-19', 'id': 'C00000', 'source': 'PMID: 37675861'}]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_df[LITERATURE_DISEASE_COL][0]" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'datasource': 'DISGENET',\n", - " 'metadata': {'lastUpdate': '10 Jul 2024', 'version': 'DISGENET v24.2'},\n", - " 'query': {'size': 1590,\n", - " 'input_type': 'NCBI Gene',\n", - " 'time': '0:31:18.977092',\n", - " 'date': '2024-09-11 14:58:51',\n", - " 'url': 'https://api.disgenet.com/api/v1/gda/summary',\n", - " 'number_of_added_nodes': 2913,\n", - " 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'SCHEMAVERSION: 3',\n", - " 'DATASOURCENAME: Wikidata',\n", - " 'BUILDDATE: 20230428',\n", - " 'SERIES: publications',\n", - " 'DATATYPE: Article',\n", - " 'DATASOURCEVERSION: 1.0.0',\n", - " 'BRIDGEDBVERSION: 3.0.22-SNAPSHOT',\n", - " 'SCHEMAVERSION: 3']},\n", - " 'query': {'size': 2023,\n", - " 'input_type': 'HGNC',\n", - " 'time': '0:00:00.888076',\n", - " 'date': '2024-09-11 14:26:48',\n", - " 'url': 'https://webservice.bridgedb.org/Human/xrefsBatch',\n", - " 'request_string': 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LINC00264\\tH\\nANKRD7\\tH\\nLOC102724601\\tH\\nSH2D6\\tH\\nFAM138F\\tH\\nFAM138A\\tH\\nGYPE\\tH\\nDDX4\\tH\\nIL5RA\\tH\\nTNFRSF9\\tH\\nLINC00368\\tH\\nLGSN\\tH\\nNEK5\\tH\\nLOC105374177\\tH\\nGLB1L3\\tH\\nLOC105379511\\tH\\nMT1A\\tH\\nFAM138E\\tH\\nTEKT3\\tH\\nSV2C\\tH\\nNR2E3\\tH\\nPLA2G10\\tH\\nLOC101927770\\tH\\nENO4\\tH\\nSBK2\\tH\\nA2ML1\\tH\\nLOC101927257\\tH\\nSPRY4-IT1\\tH\\nDNAH8\\tH\\nAK7\\tH\\nASXL3\\tH\\nTEX38\\tH\\nDNM1P35\\tH\\nCCL26\\tH\\nPPP3R2\\tH\\nCTSLP2\\tH\\nACBD7\\tH\\nSOX2-OT\\tH\\nSTC1\\tH\\nLOC284865\\tH\\nFDPSP2\\tH\\nMARVELD2\\tH\\nCDKL2\\tH\\nDCX\\tH\\nSHISA9\\tH\\nC4orf26\\tH\\nDNAH5\\tH\\nCD3G\\tH\\nTTC23L\\tH\\nPDE6A\\tH\\nAPOBEC3H\\tH\\nLINC00311\\tH\\nCXCL2\\tH\\nLINC00632\\tH\\nSALL4\\tH\\nLOC105372582\\tH\\nFAM106CP\\tH\\nRASD1\\tH\\nCACNA1F\\tH\\nELAVL2\\tH\\nKIAA0087\\tH\\nGIPR\\tH\\nCIDEA\\tH\\nBCL11B\\tH\\nTNFRSF11B\\tH\\nCA13\\tH\\nANKRD20A9P\\tH\\nFAM106B\\tH\\nSEMA3E\\tH\\nGPRC5A\\tH\\nLOC285819\\tH\\nLOC730101\\tH\\nIL1RL1\\tH\\nRGS2\\tH\\nRYBP\\tH\\nC3orf52\\tH\\nHOOK1\\tH\\nPCDH9\\tH\\nCDH19\\tH\\nPGA4\\tH\\nSTARD4\\tH\\nCYP2B7P\\tH\\nTFPI2\\tH\\nPDK4\\tH\\nPGA5\\tH\\nKCNAB3\\tH\\nLINC00641\\tH\\nLOC102724571\\tH\\nSEZ6L\\tH\\nTNFSF9\\tH\\nZNF483\\tH\\nM1AP\\tH\\nFAAP24\\tH\\nKLHL15\\tH\\nCHD1\\tH\\nAP1S3\\tH\\nCDS1\\tH\\nCRTAC1\\tH\\nGYG2\\tH\\nGRHL1\\tH\\nFSIP1\\tH\\nSYT1\\tH\\nPLCXD3\\tH\\nLOC101928371\\tH\\nPEG10\\tH\\nMPZL3\\tH\\nZNF331\\tH\\nKCNQ1OT1\\tH\\nLOC388436\\tH\\nLOC79999\\tH\\nFAM106A\\tH\\nRPS6KA6\\tH\\nBCL2L15\\tH\\nTBX5\\tH\\nEMP1\\tH\\nPPP2R2B\\tH\\nTACR1\\tH\\nSLC7A10\\tH\\nELOVL6\\tH\\nATP1B3\\tH\\nSEMA4A\\tH\\nCEP152\\tH\\nLINC01296\\tH\\nNRXN1\\tH\\nADGRG2\\tH\\nCLDN1\\tH\\nZSWIM6\\tH\\nWNT3\\tH\\nCCDC170\\tH\\nTHBS1\\tH\\nSLC35F2\\tH\\nZC3H12B\\tH\\nPLIN1\\tH\\nLOC401052\\tH\\nCATSPERG\\tH\\nIFRD1\\tH\\nGAS2L3\\tH\\nAPOBEC3D\\tH\\nPOU2F2\\tH\\nERRFI1\\tH\\nARSJ\\tH\\nFOXC1\\tH\\nPRDM1\\tH\\nRASGRP1\\tH\\nKIAA1683\\tH\\nPRELP\\tH\\nTIPARP\\tH\\nZC3H12A\\tH\\nSGIP1\\tH\\nPDE8B\\tH\\nGFPT2\\tH\\nCABP4\\tH\\nRAD51B\\tH\\nMICB\\tH\\nEIF4A3\\tH\\nFAM72C\\tH\\nC7\\tH\\nQPCT\\tH\\nMAP3K8\\tH\\nTUFT1\\tH\\nDUXAP10\\tH\\nSHROOM3\\tH\\nZC3HAV1\\tH\\nS1PR2\\tH\\nFAM122C\\tH\\nHRH1\\tH\\nUGCG\\tH\\nSOX9\\tH\\nLYVE1\\tH\\nBCL2L11\\tH\\nEIF2AK3\\tH\\nC11orf63\\tH\\nSERPINB8\\tH\\nLEPR\\tH\\nCACNB2\\tH\\nCACNA2D4\\tH\\nNR2F1\\tH\\nCLCF1\\tH\\nPSD3\\tH\\nADNP2\\tH\\nDYNC2H1\\tH\\nOR2A20P\\tH\\nSYT17\\tH\\nVASH2\\tH\\nTMEM2\\tH\\nOR2A9P\\tH\\nUSP32P2\\tH\\nEDIL3\\tH\\nLOX\\tH\\nMXD1\\tH\\nNHSL1\\tH\\nDLC1\\tH\\nCYBB\\tH\\nETV5\\tH\\nCEP126\\tH\\nPTPRF\\tH\\nCOCH\\tH\\nSCRN1\\tH\\nPPM1D\\tH\\nLILRB4\\tH\\nMFSD4A\\tH\\nCCDC144B\\tH\\nPXDNL\\tH\\nAHR\\tH\\nTRIM14\\tH\\nFRMD4B\\tH\\nCD84\\tH\\nTIAM2\\tH\\nADAMTS5\\tH\\nXYLT1\\tH\\nMYOF\\tH\\nSLC7A1\\tH\\nSMG1P3\\tH\\nUGDH\\tH\\nPMP22\\tH\\nAMPH\\tH\\nNPIPB5\\tH\\nNT5DC3\\tH\\nUBE2D2\\tH\\nPIGX\\tH\\nTTC1\\tH\\nSRP14\\tH\\nGKAP1\\tH\\nFIBP\\tH\\nMED11\\tH\\nVTI1B\\tH\\nATPAF1\\tH\\nDNAJC19\\tH\\nMRPL24\\tH\\nTRIM16L\\tH\\nPOLR2F\\tH\\nGCSH\\tH\\nTMEM147\\tH\\nLSM10\\tH\\nMRPL40\\tH\\nC11orf74\\tH\\nSERF2-C15ORF63\\tH\\nNDUFAF2\\tH\\nUBE3D\\tH\\nMALSU1\\tH\\nCOA4\\tH\\nELP6\\tH\\nMTX2\\tH\\nCMC4\\tH\\nMON1A\\tH\\nCABP7\\tH\\nMID1IP1\\tH\\nCOA6\\tH\\nKIF22\\tH\\nTSEN15\\tH\\nNDFIP2\\tH\\nHYPK\\tH\\nZCRB1\\tH\\nPARK7\\tH\\nCOX16\\tH\\nGTF3C6\\tH\\nMINOS1\\tH\\nMRPS15\\tH\\nSTOML2\\tH\\nKCNS3\\tH\\nCACNA2D3\\tH\\nCTNNBIP1\\tH\\nC7orf55\\tH\\nCOPS5\\tH\\nCHCHD5\\tH\\nYBX3P1\\tH\\nSPAG7\\tH\\nNDUFS3\\tH\\nTPI1\\tH\\nPET100\\tH\\nST3GAL2\\tH\\nMRPL21\\tH\\nTP53TG1\\tH\\nCDKN2AIPNL\\tH\\nOIP5\\tH\\nRPS20\\tH\\nATP5E\\tH\\nCBWD2\\tH\\nCDK5\\tH\\nTOMM5\\tH\\nPRR34\\tH\\nHINT1\\tH\\nBAD\\tH\\nATP5L\\tH\\nSFXN5\\tH\\nAAMDC\\tH\\nMRPL51\\tH\\nKIAA0930\\tH\\nVAMP5\\tH\\nSEPW1\\tH\\nNDUFA6\\tH\\nSLIRP\\tH\\nSHISA2\\tH\\nNUDT2\\tH\\nCOX5B\\tH\\nSNRPN\\tH\\nSNURF\\tH\\nAURKA\\tH\\nCBWD1\\tH\\nNDUFB2\\tH\\nNAA38\\tH\\nCKM\\tH\\nGPD1\\tH\\nRPS29\\tH\\nDHRS4L1\\tH\\nMRPL33\\tH\\nLOC100507291\\tH\\nATP23\\tH\\nUQCRQ\\tH\\nNDUFC2\\tH\\nBOLA3\\tH\\nTCEB2\\tH\\nCOX7A1\\tH\\nDHRS4\\tH\\nCOX6C\\tH\\nFHL2\\tH\\nSLN\\tH\\nNDUFA1\\tH\\nRPL21P28\\tH\\nRPL21\\tH\\nNDUFC2-KCTD14\\tH\\nATP5I\\tH\\nUQCC2\\tH\\nLOC101929231\\tH\\nDBNDD1\\tH\\nNDUFB9\\tH\\nLAMB3\\tH\\nCSF3R\\tH\\nUSMG5\\tH\\nDHRS4L2\\tH\\nSERPINA1\\tH\\nC1orf53\\tH\\nGLT1D1\\tH\\nGREM2\\tH\\nUQCRBP1\\tH\\nFAM24B\\tH\\nS100A8\\tH\\nCDH22\\tH\\nLEFTY1\\tH\\nC3orf14\\tH\\nLINC01291\\tH\\nTPI1P2\\tH\\nCHAF1B\\tH\\nCENPE\\tH\\nE2F2\\tH\\nOSMR\\tH\\nNDUFC1\\tH\\nGP9\\tH\\nCDON\\tH\\nPOU3F3\\tH\\nLINC01224\\tH\\nOR7G1\\tH\\nZNF735\\tH\\nRPL23AP53\\tH\\nSAMD12\\tH\\nPAMR1\\tH\\nHIST3H2A\\tH\\nLOC101927798\\tH\\nFMOD\\tH\\nOR8S1\\tH\\nKLHL11\\tH\\nLOC105375429\\tH\\nLINC01122\\tH\\nTMCO2\\tH\\nDNAH12\\tH\\nKLF4\\tH\\nCHRM4\\tH\\nLOC101928505\\tH\\nADAMTS1\\tH\\nBEX2\\tH\\nMCTP1\\tH\\nHSD3BP4\\tH\\nLINC01053\\tH\\nELK2AP\\tH\\nLOC105377458\\tH\\nFAM71E2\\tH\\nHAO1\\tH\\nCD68\\tH\\nLOC101928728\\tH\\nSYT15\\tH\\nBAGE\\tH\\nBPIFC\\tH\\nRAET1K\\tH\\nTMPRSS11BNL\\tH\\nTOMM7\\tH\\nHESX1\\tH\\nLRRC72\\tH\\nTUSC5\\tH\\nMUC13\\tH\\nLOC101929227\\tH\\nEDA2R\\tH\\nTM2D1\\tH\\nBCAT1\\tH\\nF13B\\tH\\nLINC00958\\tH\\nRFX4\\tH\\nBRD2\\tH\\nSCN3B\\tH\\nNANOS1\\tH\\nLINC01252\\tH\\nPHLDA2\\tH\\nSNAI3\\tH\\nLOC100506274\\tH\\nLINC01021\\tH\\nCHI3L1\\tH\\nTIMM10\\tH\\nKRTAP5-2\\tH\\nLY6G6C\\tH\\nLOC101927476\\tH\\nZNF169\\tH\\nTINCR\\tH\\nUBL5\\tH\\nLINC01551\\tH\\nFIRRE\\tH\\nRPS28\\tH\\nCYP2G1P\\tH\\nCASC21\\tH\\nWDR76\\tH\\nAGBL4-IT1\\tH\\nLINC01483\\tH\\nYEATS4\\tH\\nNUGGC\\tH\\nAPOBEC1\\tH\\nZAN\\tH\\nCNNM1\\tH\\nTMC1\\tH\\nAPOPT1\\tH\\nNT5M\\tH\\nLINC00877\\tH\\nLOC100133050\\tH\\nMRPL53\\tH\\nCBWD3\\tH\\nJMJD1C\\tH\\nNDUFA11\\tH\\nPLA2G2A\\tH\\nARRDC5\\tH\\nENPP1\\tH\\nNDUFB1\\tH\\nTSHZ2\\tH\\nCRIP3\\tH\\nSMIM4\\tH\\nNANOG\\tH\\nFBXO36\\tH\\nDGCR6L\\tH\\nFAM138D\\tH\\nARAP2\\tH\\nBMP6\\tH\\nMRPL20\\tH\\nMRPS18C\\tH\\nTGIF2-C20orf24\\tH\\nTPM1\\tH\\nSCML4\\tH\\nHRASLS\\tH\\nLOC105379450\\tH\\nNHS\\tH\\nLINC00888\\tH\\nLUADT1\\tH\\nTHBS2\\tH\\nSFTPB\\tH\\nSCN8A\\tH\\nCBWD6\\tH\\nSLC24A4\\tH\\nSRPX2\\tH\\nLCE3D\\tH\\nLCN12\\tH\\nGATA2\\tH\\nLINC00578\\tH\\nLOC101928449\\tH\\nGYPC\\tH\\nPDCL2\\tH\\nCHCHD3\\tH\\nGHET1\\tH\\nLOC101927284\\tH\\nC19orf35\\tH\\nPARP11\\tH\\nLOC100268168\\tH\\nANKRD45\\tH\\nCT45A3\\tH\\nAZGP1\\tH\\nARPC2\\tH\\nLINC01516\\tH\\nPTGER3\\tH\\nUROS\\tH\\nLOC101928887\\tH\\nFCGR1CP\\tH\\nLOC105375396\\tH\\nLOC727924\\tH\\nST20-MTHFS\\tH\\nTNIP3\\tH\\nTDGF1P3\\tH\\nCCL28\\tH\\nGALNT15\\tH\\nNME9\\tH\\nRSPH14\\tH\\nLINC00608\\tH\\nPCDH8\\tH\\nSHISA4\\tH\\nLVCAT5\\tH\\nDCUN1D3\\tH\\nLOC401463\\tH\\nLOC105375483\\tH\\nMRPL15\\tH\\nHS3ST2\\tH\\nC1orf194\\tH\\nRAB3B\\tH\\nTMEM251\\tH\\nLINC00152\\tH\\nLINC00102\\tH\\nCORO2B\\tH\\nBSPRY\\tH\\nCCR7\\tH\\nGLI3\\tH\\nAPOL4\\tH\\nKERA\\tH\\nGAMT\\tH\\nRBP4\\tH\\nLMO1\\tH\\nSNHG12\\tH\\nLINC01410\\tH\\nZNF280C\\tH\\nCCDC144A\\tH\\nSNRNP27\\tH\\nNDUFA3\\tH\\nSKIDA1\\tH\\nFZD5\\tH\\nRUNDC3B\\tH\\nSHFM1\\tH\\nZMAT5\\tH\\nGGT7\\tH\\nTXLNG\\tH\\nSMG1P1\\tH\\nMMADHC\\tH\\nKPNA2\\tH\\nPAM16\\tH\\nLOC101929697\\tH\\nCXCL13\\tH\\nIMPA2\\tH\\nPRKAG2\\tH\\nMEX3B\\tH\\nNCCRP1\\tH\\nMAFA\\tH\\nHIST1H3J\\tH\\nLDLR\\tH\\nKANK4\\tH\\nSHC4\\tH\\nMACROD1\\tH\\nTAC3\\tH\\nNKX2-5\\tH\\nCOX8A\\tH\\nCREB5\\tH\\nTIMM17B\\tH\\nCBWD5\\tH\\nMTFR2\\tH\\nGSTTP2\\tH\\nLINC01504\\tH\\nEMC4\\tH\\nLOC101928272\\tH\\nCWH43\\tH\\nAPOC4\\tH\\nCCND2\\tH\\nSDHAF4\\tH\\nC2orf91\\tH\\nMYCNOS\\tH\\nZNF80\\tH\\nSIK2\\tH\\nMRPL52\\tH\\nBAK1\\tH\\nEZH2\\tH\\nABCC6P1\\tH\\nHIST1H2BO\\tH\\nNRG1-IT1\\tH\\nWWC1\\tH\\nFAM183A\\tH\\nPABPC1L\\tH\\nTPTE\\tH\\nBRS3\\tH\\nPCDH19\\tH\\nAKR1D1\\tH\\nSLC4A8\\tH\\nLOC105377651\\tH\\nLDHA\\tH\\nRPGRIP1\\tH\\nPPP1R1B\\tH\\nATP5EP2\\tH\\nCACYBP\\tH\\nCHURC1-FNTB\\tH\\nBARX2\\tH\\nHELB\\tH\\nCTCFL\\tH\\nPTPN13\\tH\\nPGR\\tH\\nTMEM261\\tH\\nTRIM49B\\tH\\nMYLPF\\tH\\nLOC100131047\\tH\\nPAPPA\\tH\\nPGM2\\tH\\nMRC1\\tH\\nSNX29P2\\tH\\nLOC101929159\\tH\\nNAP1L3\\tH\\nHILPDA\\tH\\nEFNA2\\tH\\nTMEM35\\tH\\nLOC101243545\\tH\\nLOC101927829\\tH\\nHEPHL1\\tH\\nACER1\\tH\\nLYPD4\\tH\\nLOC101928510\\tH\\nLOC101929577\\tH\\nRELL1\\tH\\nSLC20A1\\tH\\nSSNA1\\tH\\nATP5G1\\tH\\nLRIT2\\tH\\nGDF6\\tH\\nNDUFA13\\tH\\nFAM227A\\tH\\nLOC101929431\\tH\\nGAPDH\\tH\\nSOAT1\\tH\\nPWRN2\\tH\\nLINC00173\\tH\\nFOXL2\\tH\\nUQCRHL\\tH\\nLINC00906\\tH\\nCA5A\\tH\\nAPOBEC2\\tH\\nCT45A1\\tH\\nPSMC3\\tH\\nPART1\\tH\\nLINC00305\\tH\\nLOC400655\\tH\\nSYT11\\tH\\nLINC01361\\tH\\nANGPTL7\\tH\\nMPC2\\tH\\nLGALS9B\\tH\\nLINC01276\\tH\\nRIPK2\\tH\\nHEPACAM\\tH\\nDKFZp779M0652\\tH\\nSOX4\\tH\\nSPATA21\\tH\\nEFCAB5\\tH\\nNDUFB5\\tH\\nTRAF3IP2\\tH\\nTRAPPC3\\tH\\nGADD45G\\tH\\nCXXC4\\tH\\nLINC00676\\tH\\nSOX1\\tH\\nC15orf61\\tH\\nHIST1H2BK\\tH\\nHIST1H2AC\\tH\\nLOC284950\\tH\\nTMEM266\\tH\\nMMP19\\tH\\nPLAUR\\tH\\nC20orf96\\tH\\nSLC9C2\\tH\\nLOC101060524\\tH\\nDRD5P2\\tH\\nMRPL11\\tH\\nAPOF\\tH\\nLRRC23\\tH\\nECT2L\\tH\\nNMNAT1\\tH\\nCCDC144CP\\tH\\nLOC101928539\\tH\\nRNLS\\tH\\nLOC105372179\\tH\\nMS4A10\\tH\\nTRAPPC2B\\tH\\nCHCHD2\\tH\\nLOC102724434\\tH\\nC7orf31\\tH\\nMIEN1\\tH\\nLOC100506444\\tH\\nPPP1R36\\tH\\nCCL2\\tH\\nSLC19A3\\tH\\nENDOU\\tH\\nLOC440028\\tH\\nPSMB10\\tH\\nFAM72D\\tH\\nGNG4\\tH\\nFOXO1\\tH\\nATP6V0A4\\tH\\nSKA1\\tH\\nPPP1R15B\\tH\\nTRPM5\\tH\\nANKRD33B\\tH\\nC1orf210\\tH\\nLOC101927058\\tH\\nMCF2\\tH\\nGALNT16\\tH\\nFRMD5\\tH\\nPCK1\\tH\\nPALM2\\tH\\nFIS1\\tH\\nKIAA0040\\tH\\nCIB2\\tH\\nNHEG1\\tH\\nCLDN11\\tH\\nPTGER4\\tH\\nCD83\\tH\\nNENF\\tH\\nLOC101928107\\tH\\nGLB1L2\\tH\\nLOC100505918\\tH\\nC2orf66\\tH\\nS100P\\tH\\nMBD3L3\\tH\\nLOC729970\\tH\\nREPS2\\tH\\nSNRPD2\\tH\\nCYP27A1\\tH\\nCDC20B\\tH\\nTAT\\tH\\nMDH1\\tH\\nCOX4I1\\tH\\nNHLH1\\tH\\nTMIGD1\\tH\\nTSACC\\tH\\nLOC101927596\\tH\\nWBSCR17\\tH\\nCYP1A2\\tH\\nPLK4\\tH\\nPSMD14\\tH\\nLOC105373782\\tH\\nMRPS28\\tH\\nARMC9\\tH\\nLINC01213\\tH\\nTGFBR3\\tH\\nARMCX4\\tH\\nLINC00243\\tH\\nDSC2\\tH\\nLOC105371335\\tH\\nLOC101927780\\tH\\nCXADR\\tH\\nDSG2\\tH\\nLPAR4\\tH\\nDAW1\\tH\\nBTG1\\tH\\nGLRX3\\tH\\nDUXAP8\\tH\\nMRPL34\\tH\\nSAT1\\tH\\nDHRS7C\\tH\\nOLR1\\tH\\nTM4SF1\\tH\\nSEMA3D\\tH\\nLOC101927650\\tH\\nLINC00668\\tH\\nRGS4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EP1B\\tH\\nNPIPB6\\tH\\nLOC101926940\\tH\\nLINC00959\\tH\\nLINC01180\\tH\\nDNAJC5G\\tH\\nFZD10\\tH\\nNDUFB8\\tH\\nERCC1\\tH\\nLOC389641\\tH\\nRPS14\\tH\\nARPC5L\\tH\\nDOCK10\\tH\\nLOC101928809\\tH\\nPLEKHA5\\tH\\nLINC00449\\tH\\nTFAP2B\\tH\\nMIR503HG\\tH\\nXG\\tH\\nCXCL3\\tH\\nCSTL1\\tH\\nLOC101928161\\tH\\nCOX6B1\\tH\\nCA8\\tH\\nIL1R1\\tH\\nLINC00619\\tH\\nGAGE1\\tH\\nNDUFA4\\tH\\nLINC01549\\tH\\nCCL16\\tH\\nERN2\\tH\\nALLC\\tH\\nCCDC43\\tH\\nFAM81B\\tH\\nMT2A\\tH\\nS100B\\tH\\nZSCAN12\\tH\\nCABP5\\tH\\nVAV3\\tH\\nIKZF3\\tH\\nDEFB118\\tH\\nDGCR6\\tH\\nLOC105371795\\tH\\nSLC28A3\\tH\\nLOC100129518\\tH\\nZNF503\\tH\\nJTB\\tH\\nLY9\\tH\\nMGC27345\\tH\\nMX2\\tH\\nLOC400002\\tH\\nUGGT2\\tH\\nNDUFA2\\tH\\nMFAP5\\tH\\nITGAM\\tH\\nXKR4\\tH\\nLINC01030\\tH\\nEBAG9\\tH\\nMAGEB5\\tH\\nTMEM150A\\tH\\nLOC101927653\\tH\\nEMC7\\tH\\nSIK1\\tH\\nEMB\\tH\\nDUXA\\tH\\nMIR3663HG\\tH\\nSPATA42\\tH\\nTNFRSF12A\\tH\\nLOC100507195\\tH\\nFAM78A\\tH\\nTENM2\\tH\\nLOC102724428\\tH\\nTRABD2A\\tH\\nTPTE2P3\\tH\\nRASAL1\\tH\\nITPRIP\\tH\\nADGRG6\\tH\\nVSIG4\\tH\\nADRBK2\\tH\\nTRIM49C\\tH\\nHOXC5\\tH\\nCMAHP\\tH\\nRPSAP58\\tH\\nOR7G3\\tH\\nLOC100288069\\tH\\nKRT9\\tH\\nARL6IP1\\tH\\nLINC00635\\tH\\nGPC3\\tH\\nSNX21\\tH\\nRIN2\\tH\\nMYHAS\\tH\\nPOTEE\\tH\\nCLEC2A\\tH\\nATP1A3\\tH\\nLOC105371267\\tH\\nLINC00696\\tH\\nBEND2\\tH\\nSPECC1\\tH\\nECM1\\tH\\nTSPAN1\\tH\\nFAM86JP\\tH\\nP2RX7\\tH\\nTMEM106A\\tH\\nPTPRH\\tH\\nEIF3K\\tH\\nSYK\\tH\\nAGR3\\tH\\nLINC00396\\tH\\nMR1\\tH\\nSLC9A2\\tH\\nGSTZ1\\tH\\nDEFB1\\tH\\nLOC101928370\\tH\\nCALD1\\tH\\nLINC01351\\tH\\nBICD1\\tH\\nFAM231D\\tH\\nSFRP5\\tH\\nEFNA1\\tH\\nLOC101929054\\tH\\nMETTL21A\\tH\\nHOXB5\\tH\\nRYR2\\tH\\nTCEA3\\tH\\nGOLGA8F\\tH\\nARL6IP6\\tH\\nLOC105369891\\tH\\nFAM185A\\tH\\nCCDC124\\tH\\nLOC100499194\\tH\\nKDM6A\\tH\\nLONRF1\\tH\\nADRA2A\\tH\\nFAM210B\\tH\\nTRIM31\\tH\\nRAB39B\\tH\\nKIAA0513\\tH\\nIQUB\\tH\\nTLL1\\tH\\nLRRC15\\tH\\nLOC284294\\tH\\nNQO1\\tH\\nRMST\\tH\\nC12orf57\\tH\\nSIRT1\\tH\\nPDGFC\\tH\\nPPIAL4C\\tH\\nPPIAL4A\\tH\\nC18orf61\\tH\\nLOC283194\\tH\\nRPS23\\tH\\nIFNLR1\\tH\\nGOLGA8G\\tH\\nLY6G6F\\tH\\nLINC00671\\tH\\nRPL23A\\tH\\nLOC101929726\\tH\\nOR10Q1\\tH\\nRNF7\\tH\\nSMCP\\tH\\nNCK2\\tH\\nRNF148\\tH\\nMIR17HG\\tH\\nLINC00479\\tH\\nLINC00551\\tH\\nSIRT4\\tH\\nHERC5\\tH\\nZNF738\\tH\\nLINC01209\\tH\\nTOB2P1\\tH\\nESPL1\\tH\\nLINC00116\\tH\\nHK1\\tH\\nLBP\\tH\\nLOC105369632\\tH\\nVIM\\tH\\nDSEL\\tH\\nPOTEJ\\tH\\nUSP44\\tH\\nLOC101927415\\tH\\nHSPH1\\tH\\nENPP7P13\\tH\\nTNFAIP3\\tH\\nBHLHE41\\tH\\nETV7\\tH\\nKCNQ4\\tH\\nLOC100287792\\tH\\nLOC101929511\\tH\\nMROH5\\tH\\nOAZ3\\tH\\nPPP1R15A\\tH\\nIDI2\\tH\\nCYB561A3\\tH\\nARMC4\\tH\\nBHMT2\\tH\\nNETO2\\tH\\nSUCNR1\\tH\\nSSU72\\tH\\nLOC399886\\tH\\nDISC1\\tH\\nSTAMBP\\tH\\nNLGN1\\tH\\nHAX1\\tH\\nTNRC18P1\\tH\\nAKR1B1\\tH\\nULK4P3\\tH\\nC1QTNF3\\tH\\nCT47A7\\tH\\nWBSCR22\\tH\\nHCAR1\\tH\\nRGL1\\tH\\nLINC01606\\tH\\nCLPS\\tH\\nDUPD1\\tH\\nSSX1\\tH\\nGSTK1\\tH\\nSPRY4\\tH\\nNUDCD2\\tH\\nRECK\\tH\\nNOL4L\\tH\\nPCBP4\\tH\\nCNTNAP2\\tH\\nKCNE1\\tH\\nLOC400541\\tH\\nLINC00261\\tH\\nC9orf173\\tH\\nMRPL48\\tH\\nPOM121L9P\\tH\\nMKRN2OS\\tH\\nRALY\\tH\\nESM1\\tH\\nEID1\\tH\\nNUDT6\\tH\\nHINT3\\tH\\nIPMK\\tH\\nC11orf98\\tH\\nCRLF1\\tH\\nCFL1P1\\tH\\nTMPRSS9\\tH\\nCHMP2A\\tH\\nOLFM1\\tH\\nZNF511\\tH\\nB3GNT7\\tH\\nSIK3\\tH\\nACER3\\tH\\nCIDEC\\tH\\nADGRD1\\tH\\nSPC25\\tH\\nLOC101926911\\tH\\nPELI3\\tH\\nEXT1\\tH\\nPCAT5\\tH\\nGDF15\\tH\\nMRPL47\\tH\\nPLSCR1\\tH\\nTOM1\\tH\\nC6\\tH\\nWDR87\\tH\\nFXYD5\\tH\\nCOBLL1\\tH\\nANGPT2\\tH\\nSRCIN1\\tH\\nSLC10A1\\tH\\nOAS1\\tH\\nMMP21\\tH\\nCOL19A1\\tH\\nGPR18\\tH\\nTMEM219\\tH\\nZNF296\\tH\\nUSP43\\tH\\nGOLGA2P9\\tH\\nRFX2\\tH\\nRAB27A\\tH\\nLOC102467217\\tH\\nMYH13\\tH\\nPHLPP2\\tH\\nLOC101928985\\tH\\nCDRT7\\tH\\nINTS6\\tH\\nHAS2\\tH\\nDZIP1\\tH\\nOR2V2\\tH\\nOR2H2\\tH\\nTSSC1\\tH\\nBOLA1\\tH\\nPABPC1P2\\tH\\nTMEM229A\\tH\\nATP8B1\\tH\\nLCNL1\\tH\\nDCDC5\\tH\\nSOD1\\tH\\nPAG1\\tH\\nCETN2\\tH\\nNCR1\\tH\\nTMEM100\\tH\\nURI1\\tH\\nTEKT4P2\\tH\\nPCAT1\\tH\\nSERTAD4\\tH\\nLINC00550\\tH\\nGLB1L\\tH\\nUNG\\tH\\nAGMAT\\tH\\nLOC101928540\\tH\\nZNF681\\tH\\nLINC01456\\tH\\nFCGR2C\\tH\\nABCG2\\tH\\nANAPC11\\tH\\nLOC102800447\\tH\\nCYLC2\\tH\\nC6orf226\\tH\\nREM2\\tH\\nBMPR1B\\tH\\nBECN1\\tH\\nADM\\tH\\nPDPR\\tH\\nKDM8\\tH\\nHMBS\\tH\\nMYO1H\\tH\\nLINC00493\\tH\\nFGF14\\tH\\nEIF2AK1\\tH\\nLOC101928489\\tH\\nKCNK1\\tH\\nCKS2\\tH\\nLOC101928035\\tH\\nLINC01221\\tH\\nEREG\\tH\\nNDUFB11\\tH\\nNARF\\tH\\nZC3HC1\\tH\\nADGRE2\\tH\\nUFC1\\tH\\nHOMER1\\tH\\nHDDC2\\tH\\nHIST1H3A\\tH\\nTNNT3\\tH\\nZNF670-ZNF695\\tH\\nGSR\\tH\\nNDRG4\\tH\\nTERC\\tH\\nFANCB\\tH\\nFFAR4\\tH\\nMGAM2\\tH\\nLRRTM4\\tH\\nINHBA\\tH\\nLOC403312\\tH\\nKLLN\\tH\\nDZANK1\\tH\\nRGS9BP\\tH\\nRIIAD1\\tH\\nARL2-SNX15\\tH\\nPLAU\\tH\\nSPDYE8P\\tH\\nSLC25A19\\tH\\nBMS1P6\\tH\\nZFYVE19\\tH\\nCTAGE1\\tH\\nMTIF3\\tH\\nSPACA4\\tH\\nSIPA1L1\\tH\\nSLC2A10\\tH\\nPGK1\\tH\\nGIF\\tH\\nMYH8\\tH\\nLOC101928098\\tH\\nFRMD4A\\tH\\nLINC01397\\tH\\nLIPE\\tH\\nTRIM49D2\\tH\\nPGM1\\tH\\nHRH4\\tH\\nLOC646241\\tH\\nLOC101927587\\tH\\nCTD-2201I18.1\\tH\\nRAPGEF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identifieridentifier.sourcetargettarget.sourceDISGENET_diseasesliterature_based_infoOpenTargets_gene_compoundsMINERVAWikiPathwaysOpenTargets_reactomeOpenTargets_goStringDB_ppi
2416PRDX3HGNCENSG00000165672Ensembl[{'disease_name': 'SPINOCEREBELLAR ATAXIA, AUT...[{'disease_name': nan, 'id': nan, 'source': nan}][{'chembl_id': nan, 'drugbank_id': nan, 'compo...[{'pathway_id': 933.0, 'pathway_label': 'Elect...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_label': 'Detoxification of Reactive...[{'go_id': 'GO:0005515', 'go_name': 'protein b...[{'stringdb_link_to': 'SIRT1', 'Ensembl': 'ENS...
2417FGBHGNCENSG00000171564Ensembl[{'disease_name': 'Cardiovascular Diseases', '...[{'disease_name': nan, 'id': nan, 'source': nan}][{'chembl_id': 'CHEMBL2109072', 'drugbank_id':...[{'pathway_id': 951.0, 'pathway_label': 'Coagu...[{'pathway_id': 'WP5115', 'pathway_label': 'Ne...[{'pathway_label': 'p130Cas linkage to MAPK si...[{'go_id': 'GO:0005576', 'go_name': 'extracell...[{'stringdb_link_to': 'LBP', 'Ensembl': 'ENSP0...
2418TEX14HGNCENSG00000121101Ensembl[{'disease_name': 'Non-obstructive azoospermia...[{'disease_name': nan, 'id': nan, 'source': nan}][{'chembl_id': nan, 'drugbank_id': nan, 'compo...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_label': nan, 'pathway_id': nan}][{'go_id': 'GO:0032466', 'go_name': 'negative ...[{'stringdb_link_to': nan, 'Ensembl': nan, 'sc...
2419FBN1HGNCENSG00000166147Ensembl[{'disease_name': 'Marfan Syndrome', 'HPO': ''...[{'disease_name': nan, 'id': nan, 'source': nan}][{'chembl_id': nan, 'drugbank_id': nan, 'compo...[{'pathway_id': 945.0, 'pathway_label': 'Nsp9 ...[{'pathway_id': 'WP3668', 'pathway_label': 'Hy...[{'pathway_label': 'TGF-beta receptor signalin...[{'go_id': 'GO:0005201', 'go_name': 'extracell...[{'stringdb_link_to': 'SERPINE1', 'Ensembl': '...
2420EPHA3HGNCENSG00000044524Ensembl[{'disease_name': 'Adenocarcinoma of lung (dis...[{'disease_name': nan, 'id': nan, 'source': nan}][{'chembl_id': 'CHEMBL24828', 'drugbank_id': '...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_id': 'WP2882', 'pathway_label': 'Nu...[{'pathway_label': 'EPH-Ephrin signaling', 'pa...[{'go_id': 'GO:0010717', 'go_name': 'regulatio...[{'stringdb_link_to': 'EFNA2', 'Ensembl': 'ENS...
\n", - "
" - ], - "text/plain": [ - " identifier identifier.source target target.source \\\n", - "2416 PRDX3 HGNC ENSG00000165672 Ensembl \n", - "2417 FGB HGNC ENSG00000171564 Ensembl \n", - "2418 TEX14 HGNC ENSG00000121101 Ensembl \n", - "2419 FBN1 HGNC ENSG00000166147 Ensembl \n", - "2420 EPHA3 HGNC ENSG00000044524 Ensembl \n", - "\n", - " DISGENET_diseases \\\n", - "2416 [{'disease_name': 'SPINOCEREBELLAR ATAXIA, AUT... \n", - "2417 [{'disease_name': 'Cardiovascular Diseases', '... \n", - "2418 [{'disease_name': 'Non-obstructive azoospermia... \n", - "2419 [{'disease_name': 'Marfan Syndrome', 'HPO': ''... \n", - "2420 [{'disease_name': 'Adenocarcinoma of lung (dis... \n", - "\n", - " literature_based_info \\\n", - "2416 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "2417 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "2418 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "2419 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "2420 [{'disease_name': nan, 'id': nan, 'source': nan}] \n", - "\n", - " OpenTargets_gene_compounds \\\n", - "2416 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", - "2417 [{'chembl_id': 'CHEMBL2109072', 'drugbank_id':... \n", - "2418 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", - "2419 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", - "2420 [{'chembl_id': 'CHEMBL24828', 'drugbank_id': '... \n", - "\n", - " MINERVA \\\n", - "2416 [{'pathway_id': 933.0, 'pathway_label': 'Elect... \n", - "2417 [{'pathway_id': 951.0, 'pathway_label': 'Coagu... \n", - "2418 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "2419 [{'pathway_id': 945.0, 'pathway_label': 'Nsp9 ... \n", - "2420 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "\n", - " WikiPathways \\\n", - "2416 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "2417 [{'pathway_id': 'WP5115', 'pathway_label': 'Ne... \n", - "2418 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", - "2419 [{'pathway_id': 'WP3668', 'pathway_label': 'Hy... \n", - "2420 [{'pathway_id': 'WP2882', 'pathway_label': 'Nu... \n", - "\n", - " OpenTargets_reactome \\\n", - "2416 [{'pathway_label': 'Detoxification of Reactive... \n", - "2417 [{'pathway_label': 'p130Cas linkage to MAPK si... \n", - "2418 [{'pathway_label': nan, 'pathway_id': nan}] \n", - "2419 [{'pathway_label': 'TGF-beta receptor signalin... \n", - "2420 [{'pathway_label': 'EPH-Ephrin signaling', 'pa... \n", - "\n", - " OpenTargets_go \\\n", - "2416 [{'go_id': 'GO:0005515', 'go_name': 'protein b... \n", - "2417 [{'go_id': 'GO:0005576', 'go_name': 'extracell... \n", - "2418 [{'go_id': 'GO:0032466', 'go_name': 'negative ... \n", - "2419 [{'go_id': 'GO:0005201', 'go_name': 'extracell... \n", - "2420 [{'go_id': 'GO:0010717', 'go_name': 'regulatio... \n", - "\n", - " StringDB_ppi \n", - "2416 [{'stringdb_link_to': 'SIRT1', 'Ensembl': 'ENS... \n", - "2417 [{'stringdb_link_to': 'LBP', 'Ensembl': 'ENSP0... \n", - "2418 [{'stringdb_link_to': nan, 'Ensembl': nan, 'sc... \n", - "2419 [{'stringdb_link_to': 'SERPINE1', 'Ensembl': '... \n", - "2420 [{'stringdb_link_to': 'EFNA2', 'Ensembl': 'ENS... " - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "combined_df.tail()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Exporting the combined data in pickle format" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# with open(os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"combined_df.pkl\"), \"wb\") as out:\n", - "# pickle.dump(combined_df, out)\n", - "# with open(\n", - "# os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"combined_metadata.pkl\"), \"wb\"\n", - "# ) as file:\n", - "# pickle.dump(combined_metadata, file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating a graph from the annotated data" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "pygraph = generator.networkx_graph(combined_df, opentargets_disease_compound_df)\n", - "with open(\n", - " os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"pcs_networkx_graph.pkl\"), \"wb\"\n", - ") as out:\n", - " pickle.dump(pygraph, out)\n", - "\n", - "# with open(\n", - "# os.path.join(os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"pcs_networkx_graph.pkl\"),\n", - "# \"rb\",\n", - "# ) as file:\n", - "# pygraph = pickle.load(file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize the graph" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# pos = nx.circular_layout(pygraph)\n", - "\n", - "# plt.figure(3, figsize=(30, 30))\n", - "# nx.draw(pygraph, pos)\n", - "# plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Cytosacpe" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyBiodatafuse.graph import cytoscape\n", - "\n", - "cytoscape.load_graph(pygraph, network_name=\"PCS network\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Neo4j" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "from pyBiodatafuse.graph import neo4j\n", - "\n", - "neo4j.save_graph_to_graphml(\n", - " pygraph,\n", - " output_path=os.path.join(\n", - " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"pcs_networkx_graph.graphml\"\n", - " ),\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Steps to load the graph in Neo4j" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- Add `.graphml` file in **import** subfolder of the DBMS folder\n", - "- Install apoc plugin\n", - "- Create `apoc.conf` file:\n", - " ```\n", - " apoc.trigger.enabled=true\n", - " apoc.import.file.enabled=true\n", - " apoc.export.file.enabled=true\n", - " apoc.import.file.use_neo4j_config=true\n", - " ```\n", - "- Add `apoc.conf` file to **conf** subfolder of the DBMS folder\n", - "- Open Neo4j Browser\n", - "- (Optionl, only run if you have imported a graph before) Remove all the nodes before importing `.graphml` file\n", - "\n", - " ```\n", - " MATCH (n) DETACH DELETE n\n", - " ```\n", - "\n", - "- Import `.graphml` file\n", - "\n", - " ```\n", - " call apoc.import.graphml('file:///pcs_networkx_graph.graphml',{readLabels:TRUE})\n", - " ```\n", - "\n", - "- Add indexes after importing the graph for improving the performance of queries\n", - "\n", - " ```\n", - " create index Gene for (n:Gene) on (n.node_type)\n", - " create index Pathway for (n:Pathway) on (n.node_type)\n", - " create index `Biological Process` for (n:`Biological Process`) on (n.node_type)\n", - " create index `Molecular Function` for (n:`Molecular Function`) on (n.node_type)\n", - " create index `Cellular Component` for (n:`Cellular Component`) on (n.node_type)\n", - " create index Disease for (n:Disease) on (n.node_type)\n", - " create index Compound for (n:Compound) on (n.node_type)\n", - " create index `Side Effect` for (n:`Side Effect`) on (n.node_type)\n", - " ```\n", - "\n", - "- Count the number of each node type\n", - " - total (```MATCH (n) RETURN count(n)```) = 19860\n", - " - Gene (```MATCH (n:Gene) RETURN count(n)```) = 1667\n", - " - Pathway (```MATCH (n:Pathway) RETURN count(n)```) = 1847\n", - " - WikiPathways (```MATCH (n:Pathway {source: \"WikiPathways\"}) RETURN count(n)```) = 678\n", - " - OpenTargets, Reactome (```MATCH (n:Pathway {source: \"OpenTargets\"}) RETURN count(n)```) = 1154\n", - " - MINERVA (```MATCH (n:Pathway {source: \"MINERVA\"}) RETURN count(n)```) = 15\n", - " - Biological Process (```MATCH (n:`Biological Process`) RETURN count(n)```) = 4624\n", - " - Molecular Function (```MATCH (n:`Molecular Function`) RETURN count(n)```) = 1327\n", - " - Cellular Component (```MATCH (n:`Cellular Component`) RETURN count(n)```) = 736\n", - " - Disease (```MATCH (n:Disease) RETURN count(n)```) = 2914\n", - " - DISGENET (```MATCH (n:Disease {source: \"DISGENET\"}) RETURN count(n)```) = 2913\n", - " - Literature (```MATCH (n:Disease {source: \"PMID: 37675861\"}) RETURN count(n)```) = 1\n", - " - Compound (```MATCH (n:Compound) RETURN count(n)```) = 2244\n", - " - Side Effect (```MATCH (n:`Side Effect`) RETURN count(n)```) = 4501\n", - "- Count the number of each edge type\n", - " - total (```MATCH ()-[r]->() RETURN count(r)```) = 101659\n", - " - interacts_with (```MATCH ()-[r:interacts_with]->() RETURN count(r)```) = 16844\n", - " - part_of (```MATCH ()-[r:part_of]->() RETURN count(r)```) = 30066 \n", - " - WikiPathways (```MATCH ()-[r:part_of {source: \"WikiPathways\"}]->() RETURN count(r)```) = 3174\n", - " - OpenTargets, Reactome (```MATCH ()-[r:part_of {source: \"OpenTargets\"}]->() RETURN count(r)```) = 26784\n", - " - MINERVA (```MATCH ()-[r:part_of {source: \"MINERVA\"}]->() RETURN count(r)```) = 108\n", - " - activates (```MATCH ()-[r:activates]->() RETURN count(r)```) = 499\n", - " - treats (```MATCH ()-[r:treats]->() RETURN count(r)```) = 8215\n", - " - has_side_effect (```MATCH ()-[r:has_side_effect]->() RETURN count(r)```) = 38328\n", - " - inhibits (```MATCH ()-[r:inhibits]->() RETURN count(r)```) = 71\n", - " - associated_with (```MATCH ()-[r:associated_with]->() RETURN count(r)```) = 7636\n", - " - Literature (```MATCH ()-[r:associated_with {source: \"DISGENET\"}]->() RETURN count(r)```) = 7607\n", - " - DISGENET (```MATCH ()-[r:associated_with{source: \"PMID: 37675861\"}]->() RETURN count(r)```) = 29\n", - "\n", - "- Export the graph as a `.csv` file\n", - "\n", - " ```call apoc.export.csv.all(\"pcs_networkx_graph.csv\",{})```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dreamwalk algoritm" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'e:\\\\BioDataFuse\\\\pyBiodatafuse\\\\examples\\\\usecases\\\\PCS\\\\DREAMwalk'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import os\n", - "\n", - "new_path = os.path.join(os.getcwd(), \"DREAMwalk\")\n", - "\n", - "\n", - "os.chdir(new_path)\n", - "\n", - "# Set the current working directory\n", - "current_dir = os.getcwd()\n", - "current_dir" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "e:\\BioDataFuse\\pyBiodatafuse\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "import DREAMwalk.generate_dis_sim as dis_gen\n", - "import DREAMwalk.generate_files as gen\n", - "import pandas as pd\n", - "from DREAMwalk.calculate_drug_scores import find_candidates\n", - "from DREAMwalk.generate_embeddings import save_embedding_files\n", - "from DREAMwalk.generate_similarity_net import save_sim_graph\n", - "from DREAMwalk.predict_associations import predict_dda" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\Tooba\\AppData\\Local\\Temp\\ipykernel_696\\3278806773.py:2: DtypeWarning: Columns (1,2,3,4,5,6,7,8,9,10,11,13,16,17,18,20,21,22,23,28) have mixed types. Specify dtype option on import or set low_memory=False.\n", - " kg_data= pd.read_csv(\"../pcs_networkx_graph.csv\")\n" - ] - }, - { - "data": { - "text/html": [ - "
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_id_labelsDOEFOEnsemblHPOMESHMONDONCIOMIM...is_approvednamesource_start_end_typeeielscoresource.1
039718.0:GeneNaNNaNENSG00000152592NaNNaNNaNNaNNaN...NaNDMP1BridgeDBNaNNaNNaNNaNNaNNaNNaN
139719.0:DiseaseNaNNaNNaNHPO_HP:0004912MESH_D063730MONDO_0000044, MONDO_0024300NCI_C131449NaN...NaNHypophosphatemic RicketsDISGENETNaNNaNNaNNaNNaNNaNNaN
239720.0:DiseaseDO_0050949NaNNaNNaNMESH_C562792MONDO_0009430, MONDO_0017324NCI_C123187OMIM_241520...NaNAutosomal recessive hypophosphatemic vitamin D...DISGENETNaNNaNNaNNaNNaNNaNNaN
339721.0:DiseaseDO_0050949NaNNaNNaNMESH_C562792MONDO_0009430, MONDO_0017324NaNOMIM_600980, OMIM_241520...NaNHypophosphatemic Rickets, Autosomal Recessive, 1DISGENETNaNNaNNaNNaNNaNNaNNaN
439722.0:PathwayNaNNaNNaNNaNNaNNaNNaNNaN...NaNOSX and miRNAs in tooth developmentWikiPathwaysNaNNaNNaNNaNNaNNaNNaN
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5 rows × 31 columns

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" - ], - "text/plain": [ - " _id _labels DO EFO Ensembl HPO \\\n", - "0 39718.0 :Gene NaN NaN ENSG00000152592 NaN \n", - "1 39719.0 :Disease NaN NaN NaN HPO_HP:0004912 \n", - "2 39720.0 :Disease DO_0050949 NaN NaN NaN \n", - "3 39721.0 :Disease DO_0050949 NaN NaN NaN \n", - "4 39722.0 :Pathway NaN NaN NaN NaN \n", - "\n", - " MESH MONDO NCI \\\n", - "0 NaN NaN NaN \n", - "1 MESH_D063730 MONDO_0000044, MONDO_0024300 NCI_C131449 \n", - "2 MESH_C562792 MONDO_0009430, MONDO_0017324 NCI_C123187 \n", - "3 MESH_C562792 MONDO_0009430, MONDO_0017324 NaN \n", - "4 NaN NaN NaN \n", - "\n", - " OMIM ... is_approved \\\n", - "0 NaN ... NaN \n", - "1 NaN ... NaN \n", - "2 OMIM_241520 ... NaN \n", - "3 OMIM_600980, OMIM_241520 ... NaN \n", - "4 NaN ... NaN \n", - "\n", - " name source _start \\\n", - "0 DMP1 BridgeDB NaN \n", - "1 Hypophosphatemic Rickets DISGENET NaN \n", - "2 Autosomal recessive hypophosphatemic vitamin D... DISGENET NaN \n", - "3 Hypophosphatemic Rickets, Autosomal Recessive, 1 DISGENET NaN \n", - "4 OSX and miRNAs in tooth development WikiPathways NaN \n", - "\n", - " _end _type ei el score source.1 \n", - "0 NaN NaN NaN NaN NaN NaN \n", - "1 NaN NaN NaN NaN NaN NaN \n", - "2 NaN NaN NaN NaN NaN NaN \n", - "3 NaN NaN NaN NaN NaN NaN \n", - "4 NaN NaN NaN NaN NaN NaN \n", - "\n", - "[5 rows x 31 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# GENERSTE FILES\n", - "kg_data = pd.read_csv(\"../pcs_networkx_graph.csv\")\n", - "kg_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['_id', '_labels', 'DO', 'EFO', 'Ensembl', 'HPO', 'MESH', 'MONDO', 'NCI',\n", - " 'OMIM', 'ORDO', 'UMLS', 'adverse_effect_count', 'chembl_id',\n", - " 'clincal_trial_phase', 'compound_cid', 'disease_type',\n", - " 'disease_umlscui', 'drugbank_id', 'gene_count', 'id', 'is_approved',\n", - " 'name', 'source', '_start', '_end', '_type', 'ei', 'el', 'score',\n", - " 'source.1'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kg_data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Graph file is saved!\n", - "Node types file is saved!\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'DataFrame' object has no attribute 'colmuns'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_696\\2648921939.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgen\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate_files\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkg_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32me:\\BioDataFuse\\pyBiodatafuse\\examples\\usecases\\PCS\\DREAMwalk\\DREAMwalk\\generate_files.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(kg_data)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;31m## generate hierarchy file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[1;31m# filter rows with ':Compound' in '_labels'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkg_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkg_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'_labels'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m':Compound'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolmuns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 66\u001b[0m \u001b[0mcompounds_filtered\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkg_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkg_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'_labels'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m':Compound'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compound_cid'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'atcClassification'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 67\u001b[0m \u001b[0mcompound_hierarchy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgenerate_drug_hierarchy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcompounds_filtered\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[0mcompound_hierarchy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hierarchy.csv'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\",\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32me:\\BioDataFuse\\pyBiodatafuse\\.venv\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5985\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5986\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5987\u001b[0m ):\n\u001b[0;32m 5988\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5989\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'colmuns'" - ] - } - ], - "source": [ - "gen.generate_files(kg_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'type'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32me:\\BioDataFuse\\pyBiodatafuse\\.venv\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3653\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3652\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[1;32me:\\BioDataFuse\\pyBiodatafuse\\.venv\\lib\\site-packages\\pandas\\_libs\\index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32me:\\BioDataFuse\\pyBiodatafuse\\.venv\\lib\\site-packages\\pandas\\_libs\\index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'type'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_files\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkg_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3\u001b[0m dis_gen\u001b[38;5;241m.\u001b[39msave_dis_sim(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m../pcs_networkx_graph.csv\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdis_sim.tsv\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[1;32me:\\BioDataFuse\\pyBiodatafuse\\examples\\usecases\\PCS\\DREAMwalk\\DREAMwalk\\generate_files.py:22\u001b[0m, in \u001b[0;36mgenerate_files\u001b[1;34m(kg_data)\u001b[0m\n\u001b[0;32m 19\u001b[0m label_map_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(id_map[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmapped_id\u001b[39m\u001b[38;5;124m'\u001b[39m], id_map[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_label\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[0;32m 21\u001b[0m \u001b[38;5;66;03m# filter rows with 'INTERACTS_WITH', 'TARGETS', or 'IS_ASSOCIATED_WITH' in 'type'\u001b[39;00m\n\u001b[1;32m---> 22\u001b[0m edges_filtered \u001b[38;5;241m=\u001b[39m kg_data[\u001b[43mkg_data\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtype\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39misin([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mactivates\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124minhibits\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124massociated_with\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minteracts_with\u001b[39m\u001b[38;5;124m'\u001b[39m])]\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m 24\u001b[0m count \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 25\u001b[0m output_graph \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msource\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtarget\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124medgetype\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124medge_id\u001b[39m\u001b[38;5;124m'\u001b[39m])\n", - "File \u001b[1;32me:\\BioDataFuse\\pyBiodatafuse\\.venv\\lib\\site-packages\\pandas\\core\\frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 3760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3763\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", - "File \u001b[1;32me:\\BioDataFuse\\pyBiodatafuse\\.venv\\lib\\site-packages\\pandas\\core\\indexes\\base.py:3655\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3653\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3654\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m-> 3655\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3656\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3657\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3658\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3659\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3660\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[1;31mKeyError\u001b[0m: 'type'" - ] - } - ], - "source": [ - "dis_gen.save_dis_sim(\"../pcs_networkx_graph.csv\", \"dis_sim.tsv\")" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.9.13" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/usecases/PCS/data/PCS_gene_list.csv b/examples/usecases/PCS/data/PCS_gene_list.csv new file mode 100644 index 00000000..172464db --- /dev/null +++ b/examples/usecases/PCS/data/PCS_gene_list.csv @@ -0,0 +1,2355 @@ +identifier +CTLA4 +PTPN22 +KIT +KRAS +NF1 +RET +CTNNB1 +SDHC +SMAD4 +SYK +CCDC103 +CCDC40 +CCDC65 +CD19 +CD79B +DRC1 +DYX1C1 +FAS +FCGR2B +HEATR2 +HYDIN +IL2RA +KIF1B +MAX +MYH6 +MYL2 +PIK3CA +PRF1 +RAG1 +RAG2 +RSPH1 +RSPH4A +SDHAF2 +TERT +ATL1 +BRCA1 +BTK +CASR +CC2D2A +CD3D +CD3E +CD79A +CHRNB1 +CLCNKB +DNAI2 +FGF8 +GLI2 +GNAQ +GP1BB +HLA-DRB1 +HRAS +KCNJ5 +LDB3 +MEFV +MEN1 +MKS1 +MYH7 +PRKAR1A +RAD51 +RTN2 +SCN1B +SCN4A +SCN5A +SCN9A +SHH +SIX3 +STAT5B +ZIC2 +ACTA1 +ACTA2 +ATXN2 +B9D1 +BSCL2 +C10orf2 +CASQ2 +CDH23 +CDON +CHRNA1 +CHRND +CHRNE +COL3A1 +CPOX +CRX +DNMT3A +DOK7 +ENPP1 +ERBB4 +ERCC2 +ESR1 +FANCA +FANCC +FANCD2 +FANCM +FASLG +FKRP +GUCA1B +IKZF1 +KCND3 +KCNT1 +KIAA0196 +KIF5A +MFN2 +MUSK +MYBPC3 +NIPA1 +NKX2-5 +NLRP3 +NODAL +NR2E3 +NRAS +NRL +OCA2 +PALB2 +PDE6G +PHKB +PLP1 +POLG2 +PRKACA +PRPH2 +PRSS1 +RAPSN +RDH12 +RPGR +RPGRIP1L +RYR2 +SCN1A +SCN2A +SCN8A +SETD2 +SLC25A13 +SLC7A7 +SNRPN +SPAST +SPATA7 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+SLC35G2 +TPM3 +REG1A +LINC01133 +AFAP1L2 +PSENEN +FAM72A +LINC00467 +HELLS +LINC00367 +PLXNA4 +C11orf73 +KLF7 +YBEY +OIT3 +LOC101929681 +PTPRD +LOC100422737 +LINC01411 +TSPAN17 +UGT1A10 +IFT22 +RPS10P7 +DBIL5P2 +IFI44 +BTK +MDP1 +LOC284080 +CYP2C18 +FBXW12 +CORO7-PAM16 +TMEM14B +POLQ +AFF4 +LHFPL4 +ABTB2 +NOMO1 +FHDC1 +TRIM38 +CTSV +GATA3 +LINCR-0002 +CFAP20 +NDUFB6 +RASA4 +LOC100288798 +CFAP206 +ROR1 +ACOT13 +LOC285626 +BANF1 +DCAF4L2 +SH3BGR +OTOA +CD226 +SLC29A4 +RPL18 +PRDX3 +FGB +TEX14 +FBN1 +EPHA3 \ No newline at end of file diff --git a/examples/usecases/PCS/graph_algorithm.ipynb b/examples/usecases/PCS/graph_algorithm.ipynb new file mode 100644 index 00000000..3e928ae3 --- /dev/null +++ b/examples/usecases/PCS/graph_algorithm.ipynb @@ -0,0 +1,98 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "html" + } + }, + "source": [ + "# Running the DREAMWalk algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "new_path = os.path.join(os.getcwd(), \"DREAMwalk\")\n", + "\n", + "\n", + "os.chdir(new_path)\n", + "\n", + "# Set the current working directory\n", + "current_dir = os.getcwd()\n", + "current_dir" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import DREAMwalk.generate_dis_sim as dis_gen\n", + "import DREAMwalk.generate_files as gen\n", + "import pandas as pd\n", + "from DREAMwalk.calculate_drug_scores import find_candidates\n", + "from DREAMwalk.generate_embeddings import save_embedding_files\n", + "from DREAMwalk.generate_similarity_net import save_sim_graph\n", + "from DREAMwalk.predict_associations import predict_dda" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# GENERSTE FILES\n", + "kg_data = pd.read_csv(\"../pcs_networkx_graph.csv\")\n", + "kg_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "gen.generate_files(kg_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dis_gen.save_dis_sim(\"../pcs_networkx_graph.csv\", \"dis_sim.tsv\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pybiodatafuse", + "language": "python", + "name": "pybiodatafuse" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/usecases/PCS/graph_generation.ipynb b/examples/usecases/PCS/graph_generation.ipynb new file mode 100644 index 00000000..a315fc60 --- /dev/null +++ b/examples/usecases/PCS/graph_generation.ipynb @@ -0,0 +1,1838 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example: PCS use case\n", + "\n", + "This notebook shows all the steps to generate PCS KG and the downstream analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/yojana/Documents/GitHub/pyBiodatafuse\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "os.chdir(\"../../../\")\n", + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import modules\n", + "import pickle\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "from pyBiodatafuse import id_mapper\n", + "from pyBiodatafuse.annotators import disgenet, minerva, opentargets, stringdb, wikipathways\n", + "from pyBiodatafuse.constants import (\n", + " DISGENET_DISEASE_COL,\n", + " MINERVA,\n", + " OPENTARGETS_DISEASE_COMPOUND_COL,\n", + " OPENTARGETS_GENE_COMPOUND_COL,\n", + " OPENTARGETS_GO_COL,\n", + " OPENTARGETS_REACTOME_COL,\n", + " STRING_PPI_COL,\n", + " WIKIPATHWAYS,\n", + ")\n", + "from pyBiodatafuse.graph import generator\n", + "from pyBiodatafuse.utils import (\n", + " combine_sources,\n", + " create_harmonized_input_file,\n", + " create_or_append_to_metadata,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load input genes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total number of genes: 2325\n" + ] + }, + { + "data": { + "text/html": [ + "
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identifier
0CTLA4
1PTPN22
2KIT
3KRAS
4NF1
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" + ], + "text/plain": [ + " identifier\n", + "0 CTLA4\n", + "1 PTPN22\n", + "2 KIT\n", + "3 KRAS\n", + "4 NF1" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_input = pd.read_csv(os.path.join(os.getcwd(), r\"examples/usecases/PCS/data/PCS_gene_list.csv\"))\n", + "print(\"Total number of genes:\", len(data_input.drop_duplicates()))\n", + "data_input.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Entity resolution using BridgeDB" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "pickle_path = os.path.join(os.getcwd(), \"examples/usecases/PCS/data/PCS_gene_list.pkl\")\n", + "metadata_path = os.path.join(os.getcwd(), \"examples/usecases/PCS/data/PCS_gene_list_metadata.pkl\")\n", + "\n", + "if not os.path.exists(pickle_path):\n", + " bridgedb_df, bridgedb_metadata = id_mapper.bridgedb_xref(\n", + " identifiers=data_input,\n", + " input_species=\"Human\",\n", + " input_datasource=\"HGNC\",\n", + " output_datasource=\"All\",\n", + " )\n", + " bridgedb_df.to_pickle(pickle_path)\n", + " with open(metadata_path, \"wb\") as file:\n", + " pickle.dump(bridgedb_metadata, file)\n", + "else:\n", + " bridgedb_df = pd.read_pickle(pickle_path)\n", + " with open(metadata_path, \"rb\") as file:\n", + " bridgedb_metadata = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of genes with mapping in BridgeDb: 1958\n" + ] + }, + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.source
0CTLA4HGNCHGNC:2505HGNC Accession Number
1CTLA4HGNCCTLA4HGNC
2CTLA4HGNC1493NCBI Gene
3CTLA4HGNCENSG00000163599Ensembl
4CTLA4HGNCP16410Uniprot-TrEMBL
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" + ], + "text/plain": [ + " identifier identifier.source target target.source\n", + "0 CTLA4 HGNC HGNC:2505 HGNC Accession Number\n", + "1 CTLA4 HGNC CTLA4 HGNC\n", + "2 CTLA4 HGNC 1493 NCBI Gene\n", + "3 CTLA4 HGNC ENSG00000163599 Ensembl\n", + "4 CTLA4 HGNC P16410 Uniprot-TrEMBL" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"Number of genes with mapping in BridgeDb:\", len(bridgedb_df[\"identifier\"].unique()))\n", + "bridgedb_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Step-by-step graph generation based on data source of interest\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gene-Disease edges\n", + "\n", + "Here we use Disgenet database. To run the following code, you would need the API key from DisGeNet by creating an account [here](https://disgenet.com/Profile-area#apiKey)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "disgenet_api_key = \"89ba9e26-dc4d-45de-a92d-79fe45d9ae1c\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.sourceDISGENET_diseases
0A2ML1HGNC144568NCBI Gene[{'disease_name': 'Noonan Syndrome', 'HPO': ''...
1AAMDCHGNC28971NCBI Gene[{'disease_name': nan, 'HPO': nan, 'NCI': nan,...
2ABCA1HGNC19NCBI Gene[{'disease_name': 'Tangier Disease', 'HPO': ''...
3ABCB1HGNC5243NCBI Gene[{'disease_name': 'Liver cell carcinoma', 'HPO...
4ABCC6P1HGNC653190NCBI Gene[{'disease_name': nan, 'HPO': nan, 'NCI': nan,...
\n", + "
" + ], + "text/plain": [ + " identifier identifier.source target target.source \\\n", + "0 A2ML1 HGNC 144568 NCBI Gene \n", + "1 AAMDC HGNC 28971 NCBI Gene \n", + "2 ABCA1 HGNC 19 NCBI Gene \n", + "3 ABCB1 HGNC 5243 NCBI Gene \n", + "4 ABCC6P1 HGNC 653190 NCBI Gene \n", + "\n", + " DISGENET_diseases \n", + "0 [{'disease_name': 'Noonan Syndrome', 'HPO': ''... \n", + "1 [{'disease_name': nan, 'HPO': nan, 'NCI': nan,... \n", + "2 [{'disease_name': 'Tangier Disease', 'HPO': ''... \n", + "3 [{'disease_name': 'Liver cell carcinoma', 'HPO... \n", + "4 [{'disease_name': nan, 'HPO': nan, 'NCI': nan,... " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "disgenet_pickle_path = os.path.join(os.getcwd(), \"examples/usecases/PCS/data/disgenet_df.pkl\")\n", + "disgenet_metadata_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/disgenet_metadata.pkl\"\n", + ")\n", + "\n", + "if not os.path.exists(disgenet_pickle_path):\n", + " disgenet_df, disgenet_metadata = disgenet.get_gene_disease(\n", + " api_key=disgenet_api_key, bridgedb_df=bridgedb_df\n", + " )\n", + " disgenet_df.to_pickle(disgenet_pickle_path)\n", + " with open(disgenet_metadata_path, \"wb\") as file:\n", + " pickle.dump(disgenet_metadata, file)\n", + "else:\n", + " disgenet_df = pd.read_pickle(disgenet_pickle_path)\n", + " with open(disgenet_metadata_path, \"rb\") as file:\n", + " disgenet_metadata = pickle.load(file)\n", + "\n", + "disgenet_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'disease_name': 'Noonan Syndrome',\n", + " 'HPO': '',\n", + " 'NCI': 'NCI_C34854',\n", + " 'OMIM': 'OMIM_176876, OMIM_163950',\n", + " 'MONDO': 'MONDO_0018997',\n", + " 'ORDO': 'ORDO_648',\n", + " 'EFO': '',\n", + " 'DO': 'DO_0060254, DO_11983, DO_11725, DO_2962, DO_3490, DO_14681, DO_14796, DO_6683',\n", + " 'MESH': 'MESH_D009634',\n", + " 'UMLS': 'UMLS_C0028326',\n", + " 'disease_type': 'disease',\n", + " 'disease_umlscui': 'C0028326',\n", + " 'score': 0.7,\n", + " 'ei': 0.8333333333333334,\n", + " 'el': None},\n", + " {'disease_name': 'Otitis Media',\n", + " 'HPO': 'HPO_HP:0000388',\n", + " 'NCI': 'NCI_C34885',\n", + " 'OMIM': '',\n", + " 'MONDO': 'MONDO_0005441',\n", + " 'ORDO': '',\n", + " 'EFO': 'EFO_0004992',\n", + " 'DO': 'DO_10754',\n", + " 'MESH': 'MESH_D010033',\n", + " 'UMLS': 'UMLS_C0029882',\n", + " 'disease_type': 'disease',\n", + " 'disease_umlscui': 'C0029882',\n", + " 'score': 0.65,\n", + " 'ei': 1.0,\n", + " 'el': None},\n", + " {'disease_name': 'NOONAN SYNDROME 1',\n", + " 'HPO': '',\n", + " 'NCI': 'NCI_C75459',\n", + " 'OMIM': 'OMIM_163950, OMIM_176876',\n", + " 'MONDO': 'MONDO_0008104, MONDO_0018997',\n", + " 'ORDO': 'ORDO_648',\n", + " 'EFO': '',\n", + " 'DO': 'DO_0060578, DO_3490',\n", + " 'MESH': 'MESH_D009634',\n", + " 'UMLS': 'UMLS_C4551602',\n", + " 'disease_type': 'disease',\n", + " 'disease_umlscui': 'C4551602',\n", + " 'score': 0.4,\n", + " 'ei': 1.0,\n", + " 'el': None},\n", + " {'disease_name': 'OTITIS MEDIA, SUSCEPTIBILITY TO',\n", + " 'HPO': '',\n", + " 'NCI': '',\n", + " 'OMIM': 'OMIM_166760, OMIM_610627',\n", + " 'MONDO': 'MONDO_0008162',\n", + " 'ORDO': '',\n", + " 'EFO': '',\n", + " 'DO': '',\n", + " 'MESH': '',\n", + " 'UMLS': 'UMLS_C1833692',\n", + " 'disease_type': 'phenotype',\n", + " 'disease_umlscui': 'C1833692',\n", + " 'score': 0.4,\n", + " 'ei': 1.0,\n", + " 'el': None}]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Example of metadata extracted from DisGeNET\n", + "disgenet_df[DISGENET_DISEASE_COL][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Disease-compound edges\n", + "\n", + "We added these edges using output from DisGeNet and querying OpenTargets." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.source
0UMLS_C0029882UMLSEFO_0004992EFO
1UMLS_C0004153UMLSEFO_0003914EFO
2UMLS_C0004153UMLSEFO_1000819EFO
3UMLS_C0010054UMLSEFO_0001645EFO
4UMLS_C0028754UMLSEFO_0001073EFO
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" + ], + "text/plain": [ + " identifier identifier.source target target.source\n", + "0 UMLS_C0029882 UMLS EFO_0004992 EFO\n", + "1 UMLS_C0004153 UMLS EFO_0003914 EFO\n", + "2 UMLS_C0004153 UMLS EFO_1000819 EFO\n", + "3 UMLS_C0010054 UMLS EFO_0001645 EFO\n", + "4 UMLS_C0028754 UMLS EFO_0001073 EFO" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "disease_mapping_df = create_harmonized_input_file(disgenet_df, DISGENET_DISEASE_COL, \"EFO\", \"UMLS\")\n", + "disease_mapping_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.sourceOpenTargets_disease_compounds
0UMLS_C0000786UMLSEFO_1001255EFO[{'chembl_id': 'CHEMBL1276308', 'drugbank_id':...
1UMLS_C0000889UMLSEFO_1000660EFO[{'chembl_id': 'CHEMBL1431', 'drugbank_id': 'D...
2UMLS_C0001125UMLSEFO_1000036EFO[{'chembl_id': 'CHEMBL306823', 'drugbank_id': ...
3UMLS_C0001175UMLSEFO_0000765EFO[{'chembl_id': 'CHEMBL704', 'drugbank_id': 'DB...
4UMLS_C0001306UMLSEFO_1001345EFO[{'chembl_id': 'CHEMBL628', 'drugbank_id': 'DB...
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" + ], + "text/plain": [ + " identifier identifier.source target target.source \\\n", + "0 UMLS_C0000786 UMLS EFO_1001255 EFO \n", + "1 UMLS_C0000889 UMLS EFO_1000660 EFO \n", + "2 UMLS_C0001125 UMLS EFO_1000036 EFO \n", + "3 UMLS_C0001175 UMLS EFO_0000765 EFO \n", + "4 UMLS_C0001306 UMLS EFO_1001345 EFO \n", + "\n", + " OpenTargets_disease_compounds \n", + "0 [{'chembl_id': 'CHEMBL1276308', 'drugbank_id':... \n", + "1 [{'chembl_id': 'CHEMBL1431', 'drugbank_id': 'D... \n", + "2 [{'chembl_id': 'CHEMBL306823', 'drugbank_id': ... \n", + "3 [{'chembl_id': 'CHEMBL704', 'drugbank_id': 'DB... \n", + "4 [{'chembl_id': 'CHEMBL628', 'drugbank_id': 'DB... " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "opentargets_dc_pickle_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/opentargets_disease_compound_df.pkl\"\n", + ")\n", + "opentargets_dc_metadata_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/opentargets_disease_compound_metadata.pkl\"\n", + ")\n", + "\n", + "if not os.path.exists(opentargets_dc_pickle_path):\n", + " opentargets_disease_compound_df, opentargets_disease_compound_metadata = (\n", + " opentargets.get_disease_compound_interactions(disease_mapping_df, cache_pubchem_cid=True)\n", + " )\n", + "\n", + " opentargets_disease_compound_df.to_pickle(opentargets_dc_pickle_path)\n", + " with open(opentargets_dc_metadata_path, \"wb\") as file:\n", + " pickle.dump(opentargets_disease_compound_metadata, file)\n", + "else:\n", + " opentargets_disease_compound_df = pd.read_pickle(opentargets_dc_pickle_path)\n", + " with open(opentargets_dc_metadata_path, \"rb\") as file:\n", + " opentargets_disease_compound_metadata = pickle.load(file)\n", + "opentargets_disease_compound_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gene-Compound edges\n", + "\n", + "These edges are extracted from OpenTargets" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.sourceOpenTargets_gene_compounds
0A2ML1HGNCENSG00000166535Ensembl[{'chembl_id': nan, 'drugbank_id': nan, 'compo...
1AAMDCHGNCENSG00000087884Ensembl[{'chembl_id': nan, 'drugbank_id': nan, 'compo...
2ABCA1HGNCENSG00000165029Ensembl[{'chembl_id': 'CHEMBL608', 'drugbank_id': 'DB...
3ABCB1HGNCENSG00000085563Ensembl[{'chembl_id': 'CHEMBL1086218', 'drugbank_id':...
4ABCC13HGNCENSG00000243064Ensembl[{'chembl_id': nan, 'drugbank_id': nan, 'compo...
\n", + "
" + ], + "text/plain": [ + " identifier identifier.source target target.source \\\n", + "0 A2ML1 HGNC ENSG00000166535 Ensembl \n", + "1 AAMDC HGNC ENSG00000087884 Ensembl \n", + "2 ABCA1 HGNC ENSG00000165029 Ensembl \n", + "3 ABCB1 HGNC ENSG00000085563 Ensembl \n", + "4 ABCC13 HGNC ENSG00000243064 Ensembl \n", + "\n", + " OpenTargets_gene_compounds \n", + "0 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", + "1 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", + "2 [{'chembl_id': 'CHEMBL608', 'drugbank_id': 'DB... \n", + "3 [{'chembl_id': 'CHEMBL1086218', 'drugbank_id':... \n", + "4 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "opentargets_gc_pickle_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/opentargets_gene_compound_df.pkl\"\n", + ")\n", + "opentargets_gc_metadata_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/opentargets_gene_compound_metadata.pkl\"\n", + ")\n", + "\n", + "if not os.path.exists(opentargets_gc_pickle_path):\n", + " opentargets_gene_compound_df, opentargets_gene_compound_metadata = (\n", + " opentargets.get_gene_compound_interactions(bridgedb_df)\n", + " )\n", + "\n", + " opentargets_gene_compound_df.to_pickle(opentargets_gc_pickle_path)\n", + " with open(opentargets_gc_metadata_path, \"wb\") as file:\n", + " pickle.dump(opentargets_gene_compound_metadata, file)\n", + "else:\n", + " opentargets_gene_compound_df = pd.read_pickle(opentargets_gc_pickle_path)\n", + " with open(opentargets_gc_metadata_path, \"rb\") as file:\n", + " opentargets_gene_compound_metadata = pickle.load(file)\n", + "\n", + "opentargets_gene_compound_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gene-Pathways edges\n", + "\n", + "These edges are extracted from MINERVA, WikiPathways, and OpenTargets" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.sourceMINERVA
0A2ML1HGNCENSG00000166535Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
1AAMDCHGNCENSG00000087884Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
2ABCA1HGNCENSG00000165029Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
3ABCB1HGNCENSG00000085563Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
4ABCC13HGNCENSG00000243064Ensembl[{'pathway_id': nan, 'pathway_label': nan, 'pa...
\n", + "
" + ], + "text/plain": [ + " identifier identifier.source target target.source \\\n", + "0 A2ML1 HGNC ENSG00000166535 Ensembl \n", + "1 AAMDC HGNC ENSG00000087884 Ensembl \n", + "2 ABCA1 HGNC ENSG00000165029 Ensembl \n", + "3 ABCB1 HGNC ENSG00000085563 Ensembl \n", + "4 ABCC13 HGNC ENSG00000243064 Ensembl \n", + "\n", + " MINERVA \n", + "0 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", + "1 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", + "2 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", + "3 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", + "4 [{'pathway_id': nan, 'pathway_label': nan, 'pa... " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "minerva_pickle_path = os.path.join(os.getcwd(), \"examples/usecases/PCS/data/minerva_df.pkl\")\n", + "minerva_metadata_path = os.path.join(os.getcwd(), \"examples/usecases/PCS/data/minerva_metadata.pkl\")\n", + "\n", + "if not os.path.exists(minerva_pickle_path):\n", + " minerva_df, minerva_metadata = minerva.get_gene_minerva_pathways(\n", + " bridgedb_df, map_name=\"COVID19 Disease Map\"\n", + " )\n", + " minerva_df.to_pickle(minerva_pickle_path)\n", + " with open(minerva_metadata_path, \"wb\") as file:\n", + " pickle.dump(minerva_metadata, file)\n", + "else:\n", + " minerva_df = pd.read_pickle(minerva_pickle_path)\n", + " with open(minerva_metadata_path, \"rb\") as file:\n", + " minerva_metadata = pickle.load(file)\n", + "\n", + "minerva_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.sourceOpenTargets_reactome
0A2ML1HGNCENSG00000166535Ensembl[{'pathway_label': nan, 'pathway_id': nan}]
1AAMDCHGNCENSG00000087884Ensembl[{'pathway_label': nan, 'pathway_id': nan}]
2ABCA1HGNCENSG00000165029Ensembl[{'pathway_label': 'PPARA activates gene expre...
3ABCB1HGNCENSG00000085563Ensembl[{'pathway_label': 'Abacavir transmembrane tra...
4ABCC13HGNCENSG00000243064Ensembl[{'pathway_label': nan, 'pathway_id': nan}]
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" + ], + "text/plain": [ + " identifier identifier.source target target.source \\\n", + "0 A2ML1 HGNC ENSG00000166535 Ensembl \n", + "1 AAMDC HGNC ENSG00000087884 Ensembl \n", + "2 ABCA1 HGNC ENSG00000165029 Ensembl \n", + "3 ABCB1 HGNC ENSG00000085563 Ensembl \n", + "4 ABCC13 HGNC ENSG00000243064 Ensembl \n", + "\n", + " OpenTargets_reactome \n", + "0 [{'pathway_label': nan, 'pathway_id': nan}] \n", + "1 [{'pathway_label': nan, 'pathway_id': nan}] \n", + "2 [{'pathway_label': 'PPARA activates gene expre... \n", + "3 [{'pathway_label': 'Abacavir transmembrane tra... \n", + "4 [{'pathway_label': nan, 'pathway_id': nan}] " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "opentargets_reactome_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/opentargets_reactome_df.pkl\"\n", + ")\n", + "opentargets_reactome_metadata_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/opentargets_reactome_metadata.pkl\"\n", + ")\n", + "\n", + "if not os.path.exists(opentargets_reactome_path):\n", + " opentargets_reactome_df, opentargets_reactome_metadata = opentargets.get_gene_reactome_pathways(\n", + " bridgedb_df=bridgedb_df\n", + " )\n", + " opentargets_reactome_df.to_pickle(opentargets_reactome_path)\n", + " with open(opentargets_reactome_metadata_path, \"wb\") as file:\n", + " pickle.dump(opentargets_reactome_metadata, file)\n", + "\n", + "else:\n", + " opentargets_reactome_df = pd.read_pickle(opentargets_reactome_path)\n", + " with open(opentargets_reactome_metadata_path, \"rb\") as file:\n", + " opentargets_reactome_metadata = pickle.load(file)\n", + "\n", + "opentargets_reactome_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gene annotation\n", + "\n", + "We extracted gene annotation from GO through OpenTargets." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.sourceOpenTargets_go
0A2ML1HGNCENSG00000166535Ensembl[{'go_id': 'GO:0052548', 'go_name': 'regulatio...
1AAMDCHGNCENSG00000087884Ensembl[{'go_id': 'GO:0005737', 'go_name': 'cytoplasm...
2ABCA1HGNCENSG00000165029Ensembl[{'go_id': 'GO:0005524', 'go_name': 'ATP bindi...
3ABCB1HGNCENSG00000085563Ensembl[{'go_id': 'GO:0008559', 'go_name': 'ABC-type ...
4ABCC13HGNCENSG00000243064Ensembl[{'go_id': nan, 'go_name': nan, 'go_type': nan}]
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" + ], + "text/plain": [ + " identifier identifier.source target target.source \\\n", + "0 A2ML1 HGNC ENSG00000166535 Ensembl \n", + "1 AAMDC HGNC ENSG00000087884 Ensembl \n", + "2 ABCA1 HGNC ENSG00000165029 Ensembl \n", + "3 ABCB1 HGNC ENSG00000085563 Ensembl \n", + "4 ABCC13 HGNC ENSG00000243064 Ensembl \n", + "\n", + " OpenTargets_go \n", + "0 [{'go_id': 'GO:0052548', 'go_name': 'regulatio... \n", + "1 [{'go_id': 'GO:0005737', 'go_name': 'cytoplasm... \n", + "2 [{'go_id': 'GO:0005524', 'go_name': 'ATP bindi... \n", + "3 [{'go_id': 'GO:0008559', 'go_name': 'ABC-type ... \n", + "4 [{'go_id': nan, 'go_name': nan, 'go_type': nan}] " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "opentargets_go_pickle_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/opentargets_go_df.pkl\"\n", + ")\n", + "opentargets_go_metadata_path = os.path.join(\n", + " os.getcwd(), \"examples/usecases/PCS/data/opentargets_go_metadata.pkl\"\n", + ")\n", + "\n", + "if not os.path.exists(opentargets_go_pickle_path):\n", + " opentargets_go_df, opentargets_go_metadata = opentargets.get_gene_go_process(\n", + " bridgedb_df=bridgedb_df\n", + " )\n", + " opentargets_go_df.to_pickle(opentargets_go_pickle_path)\n", + " with open(opentargets_go_metadata_path, \"wb\") as file:\n", + " pickle.dump(opentargets_go_metadata, file)\n", + "\n", + "else:\n", + " opentargets_go_df = pd.read_pickle(opentargets_go_pickle_path)\n", + " with open(opentargets_go_metadata_path, \"rb\") as file:\n", + " opentargets_go_metadata = pickle.load(file)\n", + "\n", + "opentargets_go_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Protein-Protein edges\n", + "\n", + "We extracted these edges from StringDB." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.sourceStringDB_ppi
0CTLA4HGNCENSG00000163599Ensembl[{'stringdb_link_to': 'EFNA2', 'Ensembl': 'ENS...
1PTPN22HGNCENSG00000134242Ensembl[{'stringdb_link_to': 'TG', 'Ensembl': 'ENSP00...
2KITHGNCENSG00000157404Ensembl[{'stringdb_link_to': 'GNA11', 'Ensembl': 'ENS...
3KRASHGNCENSG00000133703Ensembl[{'stringdb_link_to': 'ERCC1', 'Ensembl': 'ENS...
4NF1HGNCENSG00000196712Ensembl[{'stringdb_link_to': 'GNA11', 'Ensembl': 'ENS...
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" + ], + "text/plain": [ + " identifier identifier.source target target.source \\\n", + "0 CTLA4 HGNC ENSG00000163599 Ensembl \n", + "1 PTPN22 HGNC ENSG00000134242 Ensembl \n", + "2 KIT HGNC ENSG00000157404 Ensembl \n", + "3 KRAS HGNC ENSG00000133703 Ensembl \n", + "4 NF1 HGNC ENSG00000196712 Ensembl \n", + "\n", + " StringDB_ppi \n", + "0 [{'stringdb_link_to': 'EFNA2', 'Ensembl': 'ENS... \n", + "1 [{'stringdb_link_to': 'TG', 'Ensembl': 'ENSP00... \n", + "2 [{'stringdb_link_to': 'GNA11', 'Ensembl': 'ENS... \n", + "3 [{'stringdb_link_to': 'ERCC1', 'Ensembl': 'ENS... \n", + "4 [{'stringdb_link_to': 'GNA11', 'Ensembl': 'ENS... " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ppi_pickle_path = os.path.join(os.getcwd(), \"examples/usecases/PCS/data/string_ppi_df.pkl\")\n", + "ppi_metadata_path = os.path.join(os.getcwd(), \"examples/usecases/PCS/data/string_ppi_metadata.pkl\")\n", + "\n", + "if not os.path.exists(ppi_pickle_path):\n", + " string_ppi_df, string_ppi_metadata = stringdb.get_ppi(bridgedb_df=bridgedb_df)\n", + " string_ppi_df.to_pickle(ppi_pickle_path)\n", + " with open(ppi_metadata_path, \"wb\") as file:\n", + " pickle.dump(string_ppi_metadata, file)\n", + "\n", + "else:\n", + " string_ppi_df = pd.read_pickle(ppi_pickle_path)\n", + " with open(ppi_metadata_path, \"rb\") as file:\n", + " string_ppi_metadata = pickle.load(file)\n", + "\n", + "string_ppi_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating the main graph" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "combined_df = combine_sources(\n", + " bridgedb_df,\n", + " [\n", + " disgenet_df,\n", + " opentargets_gene_compound_df,\n", + " minerva_df,\n", + " opentargets_reactome_df,\n", + " opentargets_go_df,\n", + " string_ppi_df,\n", + " ],\n", + ")\n", + "combined_metadata = create_or_append_to_metadata(\n", + " bridgedb_metadata,\n", + " [\n", + " disgenet_metadata,\n", + " opentargets_disease_compound_metadata,\n", + " opentargets_gene_compound_metadata,\n", + " minerva_metadata,\n", + " opentargets_reactome_metadata,\n", + " opentargets_go_metadata,\n", + " string_ppi_metadata,\n", + " ],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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identifieridentifier.sourcetargettarget.sourceDISGENET_diseasesOpenTargets_gene_compoundsMINERVAOpenTargets_reactomeOpenTargets_goStringDB_ppi
0CTLA4HGNCENSG00000163599Ensembl[{'disease_name': 'Melanoma', 'HPO': 'HPO_HP:0...[{'chembl_id': 'CHEMBL1789844', 'drugbank_id':...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_label': 'RUNX1 and FOXP3 control th...[{'go_id': 'GO:0005794', 'go_name': 'Golgi app...[{'stringdb_link_to': 'EFNA2', 'Ensembl': 'ENS...
1PTPN22HGNCENSG00000134242Ensembl[{'disease_name': 'Diabetes Mellitus, Insulin-...[{'chembl_id': nan, 'drugbank_id': nan, 'compo...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_label': 'Phosphorylation of CD3 and...[{'go_id': 'GO:0005515', 'go_name': 'protein b...[{'stringdb_link_to': 'TG', 'Ensembl': 'ENSP00...
2KITHGNCENSG00000157404Ensembl[{'disease_name': 'Acute myeloid leukemia', 'H...[{'chembl_id': 'CHEMBL941', 'drugbank_id': 'DB...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_label': 'KIT mutants bind TKIs', 'p...[{'go_id': 'GO:0004713', 'go_name': 'protein t...[{'stringdb_link_to': 'GNA11', 'Ensembl': 'ENS...
3KRASHGNCENSG00000133703Ensembl[{'disease_name': 'Cardiofaciocutaneous Syndro...[{'chembl_id': 'CHEMBL4535757', 'drugbank_id':...[{'pathway_id': nan, 'pathway_label': nan, 'pa...[{'pathway_label': 'Signalling to RAS', 'pathw...[{'go_id': 'GO:0005886', 'go_name': 'plasma me...[{'stringdb_link_to': 'ERCC1', 'Ensembl': 'ENS...
\n", + "
" + ], + "text/plain": [ + " identifier identifier.source target target.source \\\n", + "0 CTLA4 HGNC ENSG00000163599 Ensembl \n", + "1 PTPN22 HGNC ENSG00000134242 Ensembl \n", + "2 KIT HGNC ENSG00000157404 Ensembl \n", + "3 KRAS HGNC ENSG00000133703 Ensembl \n", + "\n", + " DISGENET_diseases \\\n", + "0 [{'disease_name': 'Melanoma', 'HPO': 'HPO_HP:0... \n", + "1 [{'disease_name': 'Diabetes Mellitus, Insulin-... \n", + "2 [{'disease_name': 'Acute myeloid leukemia', 'H... \n", + "3 [{'disease_name': 'Cardiofaciocutaneous Syndro... \n", + "\n", + " OpenTargets_gene_compounds \\\n", + "0 [{'chembl_id': 'CHEMBL1789844', 'drugbank_id':... \n", + "1 [{'chembl_id': nan, 'drugbank_id': nan, 'compo... \n", + "2 [{'chembl_id': 'CHEMBL941', 'drugbank_id': 'DB... \n", + "3 [{'chembl_id': 'CHEMBL4535757', 'drugbank_id':... \n", + "\n", + " MINERVA \\\n", + "0 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", + "1 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", + "2 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", + "3 [{'pathway_id': nan, 'pathway_label': nan, 'pa... \n", + "\n", + " OpenTargets_reactome \\\n", + "0 [{'pathway_label': 'RUNX1 and FOXP3 control th... \n", + "1 [{'pathway_label': 'Phosphorylation of CD3 and... \n", + "2 [{'pathway_label': 'KIT mutants bind TKIs', 'p... \n", + "3 [{'pathway_label': 'Signalling to RAS', 'pathw... \n", + "\n", + " OpenTargets_go \\\n", + "0 [{'go_id': 'GO:0005794', 'go_name': 'Golgi app... \n", + "1 [{'go_id': 'GO:0005515', 'go_name': 'protein b... \n", + "2 [{'go_id': 'GO:0004713', 'go_name': 'protein t... \n", + "3 [{'go_id': 'GO:0005886', 'go_name': 'plasma me... \n", + "\n", + " StringDB_ppi \n", + "0 [{'stringdb_link_to': 'EFNA2', 'Ensembl': 'ENS... \n", + "1 [{'stringdb_link_to': 'TG', 'Ensembl': 'ENSP00... \n", + "2 [{'stringdb_link_to': 'GNA11', 'Ensembl': 'ENS... \n", + "3 [{'stringdb_link_to': 'ERCC1', 'Ensembl': 'ENS... " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined_df.head(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'datasource': 'DISGENET',\n", + " 'metadata': {'lastUpdate': '26 Sep 2024', 'version': 'DISGENET v24.3'},\n", + " 'query': {'size': 1882,\n", + " 'input_type': 'NCBI Gene',\n", + " 'time': '0:38:00.274435',\n", + " 'date': '2024-10-12 09:54:50',\n", + " 'url': 'https://api.disgenet.com/api/v1/gda/summary',\n", + " 'number_of_added_nodes': 4815,\n", + " 'number_of_added_edges': 14306}}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined_metadata[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2632, 10)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "combined_df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exporting the combined data and network" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Combined DataFrame saved in /Users/yojana/Documents/GitHub/pyBiodatafuse/examples/usecases/PCS/PCS_df.pkl\n", + "Metadata saved in /Users/yojana/Documents/GitHub/pyBiodatafuse/examples/usecases/PCS//PCS_metadata.pkl\n", + "Building graph: 100%|██████████| 2632/2632 [28:20<00:00, 1.55it/s]\n", + "Graph is built successfully\n", + "Graph saved in /Users/yojana/Documents/GitHub/pyBiodatafuse/examples/usecases/PCS//PCS_graph.pkl and /Users/yojana/Documents/GitHub/pyBiodatafuse/examples/usecases/PCS//PCS_graph.gml\n" + ] + } + ], + "source": [ + "generator.save_graph(\n", + " combined_df=combined_df,\n", + " combined_metadata=combined_metadata,\n", + " graph_name=\"PCS\",\n", + " graph_dir=os.path.join(os.getcwd(), \"examples/usecases\"),\n", + " disease_compound=opentargets_disease_compound_df,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "pygraph = nx.read_gml(os.path.join(os.getcwd(), \"examples/usecases/PCS/PCS_graph.gml\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize the graph" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pos = nx.circular_layout(pygraph)\n", + "\n", + "plt.figure(3, figsize=(30, 30))\n", + "nx.draw(pygraph, pos)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Exporting graph to external resources or databases" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Cytoscape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyBiodatafuse.graph import cytoscape\n", + "\n", + "cytoscape.load_graph(pygraph, network_name=\"PCS network\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Neo4j" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "from pyBiodatafuse.graph import neo4j\n", + "\n", + "neo4j.save_graph_to_graphml(\n", + " pygraph,\n", + " output_path=os.path.join(\n", + " os.getcwd(), \"examples\", \"usecases\", \"PCS\", \"pcs_networkx_graph.graphml\"\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Steps to load the graph in Neo4j" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- Add `.graphml` file in **import** subfolder of the DBMS folder\n", + "- Install apoc plugin\n", + "- Create `apoc.conf` file:\n", + " ```\n", + " apoc.trigger.enabled=true\n", + " apoc.import.file.enabled=true\n", + " apoc.export.file.enabled=true\n", + " apoc.import.file.use_neo4j_config=true\n", + " ```\n", + "- Add `apoc.conf` file to **conf** subfolder of the DBMS folder\n", + "- Open Neo4j Browser\n", + "- (Optionl, only run if you have imported a graph before) Remove all the nodes before importing `.graphml` file\n", + "\n", + " ```\n", + " MATCH (n) DETACH DELETE n\n", + " ```\n", + "\n", + "- Import `.graphml` file\n", + "\n", + " ```\n", + " call apoc.import.graphml('file:///pcs_networkx_graph.graphml',{readLabels:TRUE})\n", + " ```\n", + "\n", + "- Add indexes after importing the graph for improving the performance of queries\n", + "\n", + " ```\n", + " create index Gene for (n:Gene) on (n.node_type)\n", + " create index Pathway for (n:Pathway) on (n.node_type)\n", + " create index `Biological Process` for (n:`Biological Process`) on (n.node_type)\n", + " create index `Molecular Function` for (n:`Molecular Function`) on (n.node_type)\n", + " create index `Cellular Component` for (n:`Cellular Component`) on (n.node_type)\n", + " create index Disease for (n:Disease) on (n.node_type)\n", + " create index Compound for (n:Compound) on (n.node_type)\n", + " create index `Side Effect` for (n:`Side Effect`) on (n.node_type)\n", + " ```\n", + "\n", + "- Count the number of each node type\n", + " - total (```MATCH (n) RETURN count(n)```) = 19860\n", + " - Gene (```MATCH (n:Gene) RETURN count(n)```) = 1667\n", + " - Pathway (```MATCH (n:Pathway) RETURN count(n)```) = 1847\n", + " - WikiPathways (```MATCH (n:Pathway {source: \"WikiPathways\"}) RETURN count(n)```) = 678\n", + " - OpenTargets, Reactome (```MATCH (n:Pathway {source: \"OpenTargets\"}) RETURN count(n)```) = 1154\n", + " - MINERVA (```MATCH (n:Pathway {source: \"MINERVA\"}) RETURN count(n)```) = 15\n", + " - Biological Process (```MATCH (n:`Biological Process`) RETURN count(n)```) = 4624\n", + " - Molecular Function (```MATCH (n:`Molecular Function`) RETURN count(n)```) = 1327\n", + " - Cellular Component (```MATCH (n:`Cellular Component`) RETURN count(n)```) = 736\n", + " - Disease (```MATCH (n:Disease) RETURN count(n)```) = 2914\n", + " - DISGENET (```MATCH (n:Disease {source: \"DISGENET\"}) RETURN count(n)```) = 2913\n", + " - Literature (```MATCH (n:Disease {source: \"PMID: 37675861\"}) RETURN count(n)```) = 1\n", + " - Compound (```MATCH (n:Compound) RETURN count(n)```) = 2244\n", + " - Side Effect (```MATCH (n:`Side Effect`) RETURN count(n)```) = 4501\n", + "- Count the number of each edge type\n", + " - total (```MATCH ()-[r]->() RETURN count(r)```) = 101659\n", + " - interacts_with (```MATCH ()-[r:interacts_with]->() RETURN count(r)```) = 16844\n", + " - part_of (```MATCH ()-[r:part_of]->() RETURN count(r)```) = 30066 \n", + " - WikiPathways (```MATCH ()-[r:part_of {source: \"WikiPathways\"}]->() RETURN count(r)```) = 3174\n", + " - OpenTargets, Reactome (```MATCH ()-[r:part_of {source: \"OpenTargets\"}]->() RETURN count(r)```) = 26784\n", + " - MINERVA (```MATCH ()-[r:part_of {source: \"MINERVA\"}]->() RETURN count(r)```) = 108\n", + " - activates (```MATCH ()-[r:activates]->() RETURN count(r)```) = 499\n", + " - treats (```MATCH ()-[r:treats]->() RETURN count(r)```) = 8215\n", + " - has_side_effect (```MATCH ()-[r:has_side_effect]->() RETURN count(r)```) = 38328\n", + " - inhibits (```MATCH ()-[r:inhibits]->() RETURN count(r)```) = 71\n", + " - 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "graph_obj.count_edge_by_type(plot=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pybiodatafuse", + "language": "python", + "name": "pybiodatafuse" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.17" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From a1b74d4c1b7a12529aaf0fdd53557b6f84ab2117 Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Mon, 14 Oct 2024 11:48:48 +0200 Subject: [PATCH 07/10] refactor codebase --- src/pyBiodatafuse/analyzer/summarize.py | 12 ++++++++---- src/pyBiodatafuse/annotators/wikipathways.py | 2 +- src/pyBiodatafuse/graph/generator.py | 2 +- src/pyBiodatafuse/id_mapper.py | 6 +++--- 4 files changed, 13 insertions(+), 9 deletions(-) diff --git a/src/pyBiodatafuse/analyzer/summarize.py b/src/pyBiodatafuse/analyzer/summarize.py index 9e36bd6d..7007b0fa 100644 --- a/src/pyBiodatafuse/analyzer/summarize.py +++ b/src/pyBiodatafuse/analyzer/summarize.py @@ -29,8 +29,8 @@ def __init__(self, graph=None, graph_path=None, graph_format="pickle"): self.node_count = self.count_nodes_by_type() self.edge_count = self.count_edge_by_type() - self.node_source_count = self.get_node_counts_by_source() - self.edge_source_count = self.get_edge_counts_by_source() + self.node_source_count = self.count_nodes_by_source() + self.edge_source_count = self.count_edge_by_source() self.graph_summary = self.get_graph_summary() def get_graph_summary(self) -> str: @@ -88,6 +88,7 @@ def count_nodes_by_type( if plot: self._plot_type_count(node_count, interactive, count_type="Node") + return None return node_count @@ -102,6 +103,7 @@ def count_edge_by_type( if plot: self._plot_type_count(edge_count, interactive, count_type="Edge") + return None return edge_count @@ -122,7 +124,7 @@ def _plot_source_count(self, source_count_df: pd.DataFrame, count_type: str = "N ) fig.show() - def get_node_counts_by_source(self, plot: bool = False) -> Optional[pd.DataFrame]: + def count_nodes_by_source(self, plot: bool = False) -> Optional[pd.DataFrame]: """Get the count of nodes by data source.""" node_data = pd.DataFrame(self.graph.nodes(data=True), columns=["node", "data"]) node_data["node_type"] = node_data["data"].apply(lambda x: x["labels"]) @@ -133,10 +135,11 @@ def get_node_counts_by_source(self, plot: bool = False) -> Optional[pd.DataFrame if plot: self._plot_source_count(node_source_count, count_type="Node") + return None return node_source_count - def get_edge_counts_by_source(self, plot: bool = False) -> Optional[pd.DataFrame]: + def count_edge_by_source(self, plot: bool = False) -> Optional[pd.DataFrame]: """Get the count of edges by data source.""" edge_data = pd.DataFrame(self.graph.edges(data=True), columns=["source", "target", "data"]) edge_data["edge_type"] = edge_data["data"].apply(lambda x: x["label"]) @@ -148,6 +151,7 @@ def get_edge_counts_by_source(self, plot: bool = False) -> Optional[pd.DataFrame if plot: self._plot_source_count(edge_source_count, count_type="Edge") + return None return edge_source_count diff --git a/src/pyBiodatafuse/annotators/wikipathways.py b/src/pyBiodatafuse/annotators/wikipathways.py index 3a0f7f55..5cbf12c8 100644 --- a/src/pyBiodatafuse/annotators/wikipathways.py +++ b/src/pyBiodatafuse/annotators/wikipathways.py @@ -11,9 +11,9 @@ from typing import Any, Dict import pandas as pd -from tqdm import tqdm from SPARQLWrapper import JSON, SPARQLWrapper from SPARQLWrapper.SPARQLExceptions import SPARQLWrapperException +from tqdm import tqdm from pyBiodatafuse.constants import ( WIKIPATHWAYS, diff --git a/src/pyBiodatafuse/graph/generator.py b/src/pyBiodatafuse/graph/generator.py index 1c8d5751..4c2c13be 100644 --- a/src/pyBiodatafuse/graph/generator.py +++ b/src/pyBiodatafuse/graph/generator.py @@ -8,9 +8,9 @@ from logging import Logger from typing import Any, Dict -from tqdm import tqdm import networkx as nx import pandas as pd +from tqdm import tqdm from pyBiodatafuse.constants import ( BGEE_ANATOMICAL_NODE_ATTRS, diff --git a/src/pyBiodatafuse/id_mapper.py b/src/pyBiodatafuse/id_mapper.py index 94c0cb39..abb2ae3f 100644 --- a/src/pyBiodatafuse/id_mapper.py +++ b/src/pyBiodatafuse/id_mapper.py @@ -2,20 +2,20 @@ """Python file for mapping identifiers using BridgeDb.""" -import os -import json import csv import datetime +import json import logging +import os import time from importlib import resources -from tqdm import tqdm from typing import List, Optional, Tuple import pandas as pd import requests from pubchempy import BadRequestError, PubChemHTTPError, get_compounds, get_synonyms from rdkit.Chem import CanonSmiles +from tqdm import tqdm from pyBiodatafuse.constants import BRIDGEDB_ENDPOINT From 615bbf5465928050e8f92d388d29b2e4b1862197 Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Mon, 14 Oct 2024 11:51:11 +0200 Subject: [PATCH 08/10] fix flake --- src/pyBiodatafuse/annotators/wikipathways.py | 5 ----- src/pyBiodatafuse/graph/generator.py | 2 +- 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/src/pyBiodatafuse/annotators/wikipathways.py b/src/pyBiodatafuse/annotators/wikipathways.py index 5cbf12c8..90af7e48 100644 --- a/src/pyBiodatafuse/annotators/wikipathways.py +++ b/src/pyBiodatafuse/annotators/wikipathways.py @@ -113,11 +113,6 @@ def get_gene_wikipathways(bridgedb_df: pd.DataFrame): intermediate_df = pd.DataFrame() for gene_list_str in tqdm(query_gene_lists, desc="Querying WikiPathways"): - if query_count > 10: - print("Sleeping for 5 seconds to avoid overloading the server.") - time.sleep(5) - query_count = 0 - sparql_query_template = Template(sparql_query) substit_dict = dict(gene_list=gene_list_str) sparql_query_template_sub = sparql_query_template.substitute(substit_dict) diff --git a/src/pyBiodatafuse/graph/generator.py b/src/pyBiodatafuse/graph/generator.py index 4c2c13be..e690efaa 100644 --- a/src/pyBiodatafuse/graph/generator.py +++ b/src/pyBiodatafuse/graph/generator.py @@ -954,7 +954,7 @@ def save_graph( # Save the graph g = build_networkx_graph(combined_df, disease_compound) - logger.warning(f"Graph is built successfully") + logger.warning("Graph is built successfully") with open(graph_path_pickle, "wb") as f: pickle.dump(g, f) From f4ae486ee40f404e13b4ffbaa59d7d985f8cfcf1 Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Mon, 14 Oct 2024 11:53:01 +0200 Subject: [PATCH 09/10] remove unsued variable --- src/pyBiodatafuse/annotators/wikipathways.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/pyBiodatafuse/annotators/wikipathways.py b/src/pyBiodatafuse/annotators/wikipathways.py index 90af7e48..ac57f585 100644 --- a/src/pyBiodatafuse/annotators/wikipathways.py +++ b/src/pyBiodatafuse/annotators/wikipathways.py @@ -108,8 +108,6 @@ def get_gene_wikipathways(bridgedb_df: pd.DataFrame): sparql = SPARQLWrapper(WIKIPATHWAYS_ENDPOINT) sparql.setReturnFormat(JSON) - query_count = 0 - intermediate_df = pd.DataFrame() for gene_list_str in tqdm(query_gene_lists, desc="Querying WikiPathways"): From f0eff6820932534fa604c0c6de8d4e923f4dde31 Mon Sep 17 00:00:00 2001 From: Yojana Gadiya <45199062+YojanaGadiya@users.noreply.github.com> Date: Mon, 14 Oct 2024 13:05:35 +0200 Subject: [PATCH 10/10] clean code --- examples/usecases/PCS/graph_generation.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/usecases/PCS/graph_generation.ipynb b/examples/usecases/PCS/graph_generation.ipynb index a315fc60..d462f835 100644 --- a/examples/usecases/PCS/graph_generation.ipynb +++ b/examples/usecases/PCS/graph_generation.ipynb @@ -310,7 +310,7 @@ "metadata": {}, "outputs": [], "source": [ - "disgenet_api_key = \"89ba9e26-dc4d-45de-a92d-79fe45d9ae1c\"" + "disgenet_api_key = \"PASTE YOU API KEY HERE\"" ] }, {