Cluster analysis for networks using a fuzzy approach

As the network representation is widely used to describe problems in an increasing number of disciplines, novel methodologies are needed to handle such complexity. In particular, cluster analysis is an interesting and challenging task in the network framework. In this work, we focus on how to represent networks for fuzzy clustering and how to apply standard fuzzy algorithms for clustering multiple networks on synthetic data

TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models

Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses. This repository has an extended scope for use in graph classification, clustering, community detection, and graph embedding studies. Along with the data, we developed and provided Met2Graph, an R package for creating three different types of metabolic graphs, depending on the desired nodes and edges: Metabolites-, Enzymes-, and Reactions-based graphs. This package allows the easy generation of datasets for downstream analysis

TumorMet repository

Summarization of the repository conten

Simplified networks

Metabolites-based_tissue sub-networks of a subset of kidney and lung cancer samples in graphml format; sample_sheet.tsv tabular files containing GDC samples metadata, namely clinical information of samples

Prostate

The folder contains: Metabolites-based_tissue and Metabolites_, Enzymes-, Reactions-based_PDGSMMs networks of prostate cancer in graphml format; sample_sheet.tsv tabular file containing GDC samples metadata, namely clinical information of samples; dictionary_id excel file for correspondence between Biomodel (PDSGSMM) id and TCGA id

Brain

The folder contains: Metabolites-based_tissue and Metabolites_, Enzymes-, Reactions-based_PDGSMMs networks of brain cancer in graphml format; sample_sheet.tsv tabular file containing GDC samples metadata, namely clinical information of samples; dictionary_id excel file for correspondence between Biomodel (PDSGSMM) id and TCGA id

Ovary

The folder contains: Metabolites-based_tissue and Metabolites_, Enzymes-, Reactions-based_PDGSMMs networks of ovary cancer in graphml format; sample_sheet.tsv tabular file containing GDC samples metadata, namely clinical information of samples; sample_sheet_subtypes tabular file containing for each GDC sample the assignment of HGSOC subtype; dictionary_id excel file for correspondence between Biomodel (PDSGSMM) id and TCGA id

Breast

The folder contains: Metabolites-based_tissue and Metabolites_, Enzymes-, Reactions-based_PDGSMMs networks of breast cancer in graphml format; sample_sheet.tsv tabular file containing GDC samples metadata, namely clinical information of samples; dictionary_id excel file for correspondence between Biomodel (PDSGSMM) id and TCGA id

Kidney

The folder contains: Metabolites-based_tissue and Metabolites_, Enzymes-, Reactions-based_PDGSMMs networks of kidney cancer in graphml format; sample_sheet.tsv tabular file containing GDC samples metadata, namely clinical information of samples; dictionary_id excel file for correspondence between Biomodel (PDSGSMM) id and TCGA id

Lung

The folder contains: Metabolites-based_tissue and Metabolites_, Enzymes-, Reactions-based_PDGSMMs networks of lung cancer in graphml format; sample_sheet.tsv tabular file containing GDC samples metadata, namely clinical information of samples; dictionary_id excel file for correspondence between Biomodel (PDSGSMM) id and TCGA id