API

Import NetworkCommons as:

import networkcommons as nc

Methods

MOON

methods.prepare_metab_inputs(metab_input, ...)	Prepares the metabolite inputs by adding compartment codes.
methods.is_expressed(x, expressed_genes_entrez)	Determines if a gene is expressed based on the given criteria.
methods.filter_pkn_expressed_genes(...)	Filters out unexpressed nodes from the prior knowledge network (PKN).
methods.filter_input_nodes_not_in_pkn(data, pkn)	Filters the input nodes in the 'data' dictionary that are not present in the PKN.
methods.keep_controllable_neighbours(...)	This function filters out nodes from a dictionary of source nodes that are not controllable from the graph.
methods.keep_observable_neighbours(...)	This function filters out nodes from a dictionary of target nodes that are not observable from the graph.
methods.compress_same_children(...)	Compresses nodes in the graph that have the same children by relabeling them with a common signature.
methods.run_moon_core([upstream_input, ...])	Runs the MOON algorithm to iteratively infer MOON scores from downstream nodes.
methods.filter_incoherent_TF_target(...)	Filters incoherent TF-target interactions from the meta_network based on the given inputs.
methods.decompress_moon_result(moon_res, ...)	Decompresses the moon_res dataframe by mapping the compressed nodes to their corresponding original source using the provided meta_network_compressed_list and the filtered meta_network.
methods.reduce_solution_network(moon_res, ...)	Reduces the solution network based on MOON score cutoffs and returns the reduced network and attribute table.
methods.translate_res(untranslated_network, ...)	Translates the network and attribute table based on the given mapping dataframe.

Topological methods

methods.run_shortest_paths(network, ...[, ...])	Calculate the shortest paths between sources and targets.
methods.run_sign_consistency(network, paths, ...)	Calculate the sign consistency between sources and targets.
methods.run_reachability_filter(network, ...)	Filters out all nodes from the graph which cannot be reached from
methods.run_all_paths(network, source_dict, ...)	Calculate all paths between sources and targets.
methods.compute_all_paths(network, source, ...)	Compute all paths between source and targets.

Random Walk with Restart

methods.add_pagerank_scores(network, ...[, ...])	Add PageRank scores to the nodes of the network.
methods.compute_ppr_overlap(network[, ...])	Compute the overlap of nodes that exceed the personalized PageRank

CORNETO

methods.run_corneto_carnival(network, ...[, ...])

Run the Vanilla Carnival algorithm via CORNETO.

SignalingProfiler

methods.run_signalingprofiler(sources, ...)

Contextualize networks by the SignalingProfiler algorithm.

Prior Knowledge

data.network.get_omnipath([genesymbols, ...])	Retrieves the Omnipath network with directed interactions and specific criteria.
data.network.get_lianaplus([resource, update])	Retrieves the metabolic network used in COSMOS from the server
data.network.get_phosphositeplus([update])	Retrieves the PhosphoSitePlus network from the server
data.network.get_cosmos_pkn([update])	Retrieves the metabolic network used in COSMOS from the server

Datasets

Utils

data.omics.datasets()	Built-in omics datasets.
data.omics.deseq2(counts, metadata, ...[, ...])	Runs DESeq2 analysis on the given counts and metadata.
data.omics.get_ensembl_mappings([update])	Retrieves the mapping between Ensembl attributes for human genes.
data.omics.convert_ensembl_to_gene_symbol(...)	Converts Ensembl IDs to gene symbols using an equivalence dataframe, handles partial matches, and summarizes duplicated entries by taking the maximum value.

DecryptM

data.omics.decryptm_datasets([update])	Table of all DecryptM datasets.
data.omics.decryptm_table(experiment, ...)	One table of omics data from DecryptM.
data.omics.decryptm_experiment(experiment[, ...])	Tables from one DecrptM experiment of one omics modality.

PANACEA

data.omics.panacea_experiments([update])	Table describing the experiments (drug-cell combinations) contained in the Panacea dataset and merging it with the presence of TF_scores.
data.omics.panacea_datatypes()	Table describing the available data types in the Panacea dataset.
data.omics.panacea_tables([cell_line, drug, ...])	One table of countdata and one table of metadata from Panacea if raw data is selected.
data.omics.panacea_gold_standard([update])	Retrieves the gold standard (primary targets and offtargets linked to drugs) used in Panacea from the server.

scPerturb

data.omics.scperturb(dataset)	Access an scPerturb dataset.
data.omics.scperturb_metadata()	Metadata for the scPerturb deposited datasets.
data.omics.scperturb_datasets()	List the datasets available in scPerturb.

CPTAC

data.omics.cptac_cohortsize([update])	Table describing the number of tumor and normal samples per CPTAC cancer type.
data.omics.cptac_fileinfo([update])	Table describing the files contained in the CPTAC dataset.
data.omics.cptac_table(data_type, ...)	One table of omics data from CPTAC.
data.omics.cptac_datatypes()	List describing the data types available in the CPTAC dataset.
data.omics.cptac_extend_dataframe(df)	Extends the DataFrame by duplicating rows based on Tumor and Normal columns.

NCI60

data.omics.nci60_datasets([update])	Table of all NCI60 datasets (cell types).
data.omics.nci60_datatypes()	Table of all NCI60 data types.
data.omics.nci60_table(cell_line, data_type)	One table of omics data from NCI60.

Phospho-EGF meta-analysis

data.omics.phospho_egf_datatypes()	Table describing the available data types in the Phospho EGF dataset.
data.omics.phospho_egf_tables([type])	A table with the corresponding data type for the phospho EGF dataset.

Evaluation and description

eval.get_number_nodes(network)	Get the number of nodes in the network.
eval.get_number_edges(network)	Get the number of edges in the network.
eval.get_mean_degree(network)	Get the mean degree centrality of the network.
eval.get_mean_betweenness(network)	Get the mean betweenness centrality of the network.
eval.get_mean_closeness(network)	Get the mean closeness centrality of the network.
eval.get_connected_targets(network, target_dict)	Get the number of connected targets in the network.
eval.get_recovered_offtargets(network, ...)	Get the number of off-targets recovered by the network.
eval.get_graph_metrics(network, target_dict)	Get the graph metrics of a network.
eval.get_metric_from_networks(networks, ...)	Get the graph metrics of multiple networks.
eval.get_ec50_evaluation(network, ec50_dict)	Get the EC50 evaluation of a network.
eval.run_ora(graph, net[, metric, ascending])	Run over-representation analysis on a custom set of genes.
eval.get_phosphorylation_status(network, ...)	Calculates the phosphorylation status metrics for a given network and dataframe.
eval.perform_random_controls(graph, ...[, ...])	Performs random controls of a network by shuffling node labels and running the inference function.
eval.shuffle_dict_keys(dictionary, items)	Shuffle the keys of a dictionary.

Visualization

visual.NetworkXVisualizer(network[, color_by])	An advanced visualizer that extends NetworkVisualizerBase with additional features.
visual.YFilesVisualizer(network)	A class for visualizing network graphs interactively using the yFiles HTML widget.
visual.get_styles()	Return a dictionary containing styles for different types of networks specific to yFiles visualizations.
visual.set_style_attributes(item, base_style)	Set attributes for a graph item (node or edge) based on the given styles.
visual.merge_styles(default_style, custom_style)	Merge custom styles with default styles to ensure all necessary fields are present.
visual.get_comparison_color(category, styles)	Get the color for nodes or edges based on the comparison category.
visual.get_edge_color(effect, styles)	Get the color for an edge based on its effect.
visual.update_node_property(node[, type, value])
visual.update_edge_property(edge[, type, value])
visual.apply_node_style(node, style)	Apply the given style to a node.
visual.apply_edge_style(edge, style)	Apply the given style to an edge.
visual.build_volcano_plot(data[, log2fc, ...])
visual.build_ma_plot(data, log2fc, mean_exp)
visual.plot_pca(dataframe, metadata[, ...])	Plots the PCA (Principal Component Analysis) of a dataframe.
visual.build_heatmap_with_tree(distance_df)	Build a heatmap with hierarchical clustering based on a Jaccard distance matrix.
visual.lollipop_plot(df, label_col, value_col)	Function to plot metrics using a lollipop plot from a DataFrame.
visual.create_heatmap(results[, terms, ...])	Create a heatmap.
visual.plot_density(df, gene_ids[, ...])	Plots density of intensity values for specified genes, including mean and quantile lines, and separates distributions by groups if metadata is provided.
visual.plot_scatter(df[, summarise_df, ...])	Plot a scatter plot with customizable column labels.
visual.plot_rank(df[, bio_ids, figsize, ...])	Plot a protein abundance rank plot.

Utilities

utils.to_cornetograph(graph)	Convert a networkx graph to a corneto graph, if needed.
utils.to_networkx(graph[, ...])	Convert a corneto graph to a networkx graph, if needed.
utils.read_network_from_file(file_path[, ...])	Read network from a file.
utils.network_from_df(network_df[, ...])	Create a network from a DataFrame.
utils.get_subnetwork(network, paths)	Creates a subnetwork from a list of paths.
utils.decoupler_formatter(df, columns)	Format dataframe to be used by decoupler.
utils.targetlayer_formatter(df[, ...])	Format dataframe to be used by the network methods.
utils.handle_missing_values(df[, threshold, ...])	Handles missing values in a DataFrame by filling them with a specified function or value, or dropping the rows.
utils.subset_df_with_nodes(network, dataframe)	Subsets a dataframe using the nodes of a network as the index.
utils.node_attrs_from_corneto(graph)	Extract node attributes from a corneto graph to a pandas dataframe.
utils.edge_attrs_from_corneto(graph)	Extract edge attributes from a corneto graph to a pandas dataframe.