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bioRxiv · 10.1101/2022.04.18.488630

TITAN: A Toolbox for Information-Theoretic Analysis of Molecular Networks

Bergmans, T.·Celikel, T.
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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.

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Bergmans, T., Celikel, T.. 2022-04-18. TITAN: A Toolbox for Information-Theoretic Analysis of Molecular Networks. https://doi.org/10.1101/2022.04.18.488630

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