NETSCOPE: Information-Theory Based Network Discovery and Analysis

NETSCOPE: Information-Theory Based Network Discovery and Analysis | bioRxiv Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results NETSCOPE: Information-Theory Based Network Discovery and Analysis Tido Bergmans , Tousif Jamal , Aya Rezeika , Chih-Chia Hsing , View ORCID Profile Tansu Celikel doi: https://doi.org/10.1101/2022.04.18.488630 Tido Bergmans 1 Donders Institute for Brain, Cognition, and Behaviour, Radboud University , Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tousif Jamal 1 Donders Institute for Brain, Cognition, and Behaviour, Radboud University , Nijmegen, the Netherlands 2 Social Brain Lab, Netherlands Institute for Neuroscience , Amsterdam, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aya Rezeika 1 Donders Institute for Brain, Cognition, and Behaviour, Radboud University , Nijmegen, the Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chih-Chia Hsing 3 School of Psychology, Georgia Institute of Technology , Atlanta, GA, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tansu Celikel 1 Donders Institute for Brain, Cognition, and Behaviour, Radboud University , Nijmegen, the Netherlands 3 School of Psychology, Georgia Institute of Technology , Atlanta, GA, USA 4 Georgia Tech-CNRS IRL 2958, Georgia Institute of Technology - Europe , Metz, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tansu Celikel For correspondence: celikel{at}gatech.edu Abstract Full Text Info/History Metrics Preview PDF Abstract Biological systems are naturally described as networks, spanning molecular interactions, cellular circuits, and brain-wide functional connectivity. Despite the ubiquity of network data, workflows for inferring network structure and then applying comparable graph analyses across modalities remain fragmented. We present NETSCOPE, an open-source, multi-platform toolbox for information-theoretic network inference and analysis. NETSCOPE estimates pairwise statistical dependence with mutual information (MI), derives weighted adjacency matrices, removes likely spurious edges using shuffle-based thresholds, and prunes indirect connections using the data processing inequality (DPI). A key feature is the conversion of MI-based similarity into a metric space via (normalized) variation of information (VI), enabling weighted shortest-path and centrality analyses that require distance-like edge weights. We validate the toolbox on synthetic data with known ground-truth topology and by reconstructing published molecular networks in Saccharomyces cerevisiae . We further demonstrate cross-domain use cases in single-cell transcriptomic networks, cell-level anatomical maps, EEG connectivity, and resting-state fMRI. NETSCOPE runs in Python and MATLAB/Octave, and is compatible with Jupyter/Colab workflows. Competing Interest Statement The authors have declared no competing interest. Footnotes The toolbox is validated with structural and functional data. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Back to top Previous Next Posted March 27, 2026. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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