acore.network_analysis package

get_network_communities(graph, args)

Finds communities in a graph using different methods. For more information on the methods visit:

• https://networkx.github.io/documentation/latest/reference/algorithms/generated/networkx.algorithms.community.modularity_max.greedy_modularity_communities.html

• https://networkx.github.io/documentation/networkx-2.0/reference/algorithms/generated/networkx.algorithms.community.asyn_lpa.asyn_lpa_communities.html

• https://networkx.github.io/documentation/latest/reference/algorithms/generated/networkx.algorithms.community.centrality.girvan_newman.html

• https://networkx.github.io/documentation/latest/reference/generated/networkx.convert_matrix.to_pandas_adjacency.html

Parameters:

• graph (graph) – networkx graph

• args (dict) – config file arguments

Returns:

Dictionary of nodes and which community they belong to (from 0 to number of communities).

get_snf_clusters(data_tuples, num_clusters=None, metric='euclidean', k=5, mu=0.5)

Cluster samples based on Similarity Network Fusion (SNF) (ref: https://www.ncbi.nlm.nih.gov/pubmed/24464287)

Parameters:

• df_tuples – list of (dataset,metric) tuples

• index – how the datasets can be merged (common columns)

• num_clusters – number of clusters to be identified, if None, the algorithm finds the best number based on SNF algorithm (recommended)

• distance_metric – distance metric used to calculate the sample similarity network

• k – number of neighbors used to measure local affinity (KNN)

• mu – normalization factor to scale similarity kernel when constructing affinity matrix

Return tuple:

1) fused_aff: affinity clustered samples, 2) fused_labels: cluster labels, 3) num_clusters: number of clusters, 4) silhouette: average silhouette score

most_central_edge(G)

Compute the eigenvector centrality for the graph G, and finds the highest value.

Parameters:

G (graph) – networkx graph

Returns:

Highest eigenvector centrality value.

Return type:

float

get_louvain_partitions(G, weight)

Computes the partition of the graph nodes which maximises the modularity (or try..) using the Louvain heuristices. For more information visit https://python-louvain.readthedocs.io/en/latest/api.html.

Parameters:

• G (graph) – networkx graph which is decomposed.

• weight (str) – the key in graph to use as weight.

Returns:

The partition, with communities numbered from 0 to number of communities.

Return type:

dict

run_snf(df_dict, index, num_clusters=None, distance_metric='euclidean', k_affinity=5, mu_affinity=0.5)

Runs Similarity Network Fusion: integration of multiple omics datasets to identify similar samples (clusters) (ref: https://www.ncbi.nlm.nih.gov/pubmed/24464287). We make use of the pyton version SNFpy (rmarkello/snfpy)

Parameters:

• df_dict – dictionary of datasets to be used (i.e {‘rnaseq’: rnaseq_data, ‘proteomics’: proteomics_data})

• index – how the datasets can be merged (common columns)

• num_clusters – number of clusters to be identified, if None, the algorithm finds the best number based on SNF algorithm (recommended)

• distance_metric – distance metric used to calculate the sample similarity network

• k_affinity – number of neighbors used to measure local affinity (KNN)

• mu_ffinity – normalization factor to scale similarity kernel when constructing affinity matrix

Return tuple:

1) feature_df: SNF features and mutual information score (MIscore), 2) fused_aff: adjacent similarity matrix, 3)fused_labels: cluster labels, 4) silhouette: silhouette score