disease-stratified-network-visualization
Summary
Construct and render bipartite metabolite-protein association networks stratified by disease class, with visual encoding of association strength, confidence, and disease type. This skill transforms disease-specific correlation and prediction data into publication-ready network plots for systems-level interpretation of metabolic dysregulation.
When to use
You have metabolite-disease correlation scores and protein association predictions from a deep learning model (such as DeepMSProfiler) and need to visualize which metabolites and proteins co-associate within specific disease groups. Use this skill when stakeholders or reviewers require interpretable network-level evidence of disease-specific metabolic rewiring, especially when the number of associations is too large to present as tables or simple heatmaps.
When NOT to use
- Input data are already aggregated into a single network without disease stratification — use a standard network visualization tool instead.
- Metabolite or protein sets are too sparse (< 10 nodes per disease group) — consider alternative summaries such as ranked lists or heatmaps.
- Association scores lack confidence estimates or uncertainty quantification — network edge transparency cannot be meaningfully encoded.
Inputs
- metabolite-disease correlation matrix (numpy array or CSV: metabolites × diseases)
- protein association predictions (scores, confidence values per metabolite-protein pair)
- disease class labels (sample-level or metabolite-level annotations)
- association strength thresholds (optional: filtering cutoffs for edge inclusion)
Outputs
- bipartite network graph object (NetworkX or equivalent)
- network node layout coordinates (2D or 3D positions)
- high-resolution network plot image (PNG, SVG, or PDF suitable for publication)
- network graph file (optional: GraphML, GML, or JSON for external tools)
How to apply
Load metabolite-disease correlation data and protein predictions generated by the deep learning module. Construct a bipartite directed graph with metabolites and proteins as distinct node sets, filtering edges by association strength (e.g., correlation magnitude or prediction confidence above a threshold). Assign node size proportional to association magnitude and edge transparency or width proportional to confidence scores. Apply a force-directed or hierarchical layout algorithm to resolve node positions for visual clarity. Render the network with disease-type-specific color coding on nodes (e.g., one color per disease class), and export as high-resolution raster or vector format. Validate that nodes with high association strength cluster together and that disease-stratified coloring is visually distinct across groups.
Related tools
- DeepMSProfiler (source deep learning model that generates metabolite-disease correlations and protein association predictions) — https://github.com/yjdeng9/DeepMSProfiler
- NetworkX (Python library for constructing, manipulating, and analyzing bipartite graphs and applying layout algorithms)
- Cytoscape (optional interactive platform for post-hoc network refinement, filtering, and advanced visualization)
Examples
python showFeature.py # Invokes feature extraction and network visualization pipeline post-training
Evaluation signals
- Network nodes are correctly stratified by disease class, visible via distinct color coding with no overlap in node identities across classes.
- Node sizes scale monotonically with association strength (magnitude of correlation or prediction confidence); verify by spot-checking high-magnitude and low-magnitude associations.
- Edge transparency or line width reflects confidence scores: high-confidence edges appear darker or thicker, low-confidence edges fainter or thinner.
- Force-directed layout produces visually coherent clusters with metabolites and proteins associated in the same disease group positioned nearby; hierarchical layout shows clear bipartite layering.
- Output image resolution is ≥300 dpi and file size is suitable for publication (< 50 MB for raster, < 10 MB for vector); visual distinctness of nodes, edges, and labels is confirmed at intended display size.
Limitations
- Network scalability: rendering becomes visually cluttered with > 500 nodes; consider filtering or hierarchical abstraction for larger association sets.
- Layout algorithm sensitivity: force-directed methods are stochastic; multiple runs may yield different (though topologically equivalent) layouts; set random seed for reproducibility.
- Disease stratification assumes discrete disease classes; continuous or multi-state phenotypes require custom node coloring or faceted subplots.
- No built-in statistical testing of network properties (e.g., clustering coefficient, shortest path significance) — these must be computed separately if required.
- File format compatibility: SVG and vector exports may not preserve all visual properties (transparency, gradients) when imported into all document processors.
Evidence
- [other] Construct a bipartite network graph with metabolites and proteins as nodes, weighted by association strength and disease class.: "Construct a bipartite network graph with metabolites and proteins as nodes, weighted by association strength and disease class."
- [other] Apply network layout algorithm (force-directed or hierarchical) to position nodes for interpretability.: "Apply network layout algorithm (force-directed or hierarchical) to position nodes for interpretability."
- [other] Render the network plot with disease-type color coding, node size scaled by association strength, and edge transparency reflecting confidence.: "Render the network plot with disease-type color coding, node size scaled by association strength, and edge transparency reflecting confidence."
- [other] Export the finalized network plot as a high-resolution image file suitable for publication.: "Export the finalized network plot as a high-resolution image file suitable for publication."
- [readme] Disease-associated metabolite-protein network plots. Load the metabolite-disease correlation data and protein association predictions generated by DeepMSProfiler's deep learning module.: "Load the metabolite-disease correlation data and protein association predictions generated by DeepMSProfiler's deep learning module."
- [readme] It takes raw metabolomics data from different disease groups as input and provides three main outputs: 1. Sample disease type labels. 2. Heatmaps depicting the correlation of different metabolite [signals with diseases]. 3. Disease-associated metabolite-protein network plots.: "It takes raw metabolomics data from different disease groups as input and provides three main outputs: 1. Sample disease type labels. 2. Heatmaps depicting the correlation of different metabolite"
1---2name: disease-stratified-network-visualization3description: Use when you have metabolite-disease correlation scores and protein association predictions from a deep learning model (such as DeepMSProfiler) and need to visualize which metabolites and proteins co-associate within specific disease groups.4license: CC-BY-4.05---67# disease-stratified-network-visualization89## Summary1011Construct and render bipartite metabolite-protein association networks stratified by disease class, with visual encoding of association strength, confidence, and disease type. This skill transforms disease-specific correlation and prediction data into publication-ready network plots for systems-level interpretation of metabolic dysregulation.1213## When to use1415You have metabolite-disease correlation scores and protein association predictions from a deep learning model (such as DeepMSProfiler) and need to visualize which metabolites and proteins co-associate within specific disease groups. Use this skill when stakeholders or reviewers require interpretable network-level evidence of disease-specific metabolic rewiring, especially when the number of associations is too large to present as tables or simple heatmaps.1617## When NOT to use1819- Input data are already aggregated into a single network without disease stratification — use a standard network visualization tool instead.20- Metabolite or protein sets are too sparse (< 10 nodes per disease group) — consider alternative summaries such as ranked lists or heatmaps.21- Association scores lack confidence estimates or uncertainty quantification — network edge transparency cannot be meaningfully encoded.2223## Inputs2425- metabolite-disease correlation matrix (numpy array or CSV: metabolites × diseases)26- protein association predictions (scores, confidence values per metabolite-protein pair)27- disease class labels (sample-level or metabolite-level annotations)28- association strength thresholds (optional: filtering cutoffs for edge inclusion)2930## Outputs3132- bipartite network graph object (NetworkX or equivalent)33- network node layout coordinates (2D or 3D positions)34- high-resolution network plot image (PNG, SVG, or PDF suitable for publication)35- network graph file (optional: GraphML, GML, or JSON for external tools)3637## How to apply3839Load metabolite-disease correlation data and protein predictions generated by the deep learning module. Construct a bipartite directed graph with metabolites and proteins as distinct node sets, filtering edges by association strength (e.g., correlation magnitude or prediction confidence above a threshold). Assign node size proportional to association magnitude and edge transparency or width proportional to confidence scores. Apply a force-directed or hierarchical layout algorithm to resolve node positions for visual clarity. Render the network with disease-type-specific color coding on nodes (e.g., one color per disease class), and export as high-resolution raster or vector format. Validate that nodes with high association strength cluster together and that disease-stratified coloring is visually distinct across groups.4041## Related tools4243- **DeepMSProfiler** (source deep learning model that generates metabolite-disease correlations and protein association predictions) — https://github.com/yjdeng9/DeepMSProfiler44- **NetworkX** (Python library for constructing, manipulating, and analyzing bipartite graphs and applying layout algorithms)45- **Cytoscape** (optional interactive platform for post-hoc network refinement, filtering, and advanced visualization)4647## Examples4849```50python showFeature.py # Invokes feature extraction and network visualization pipeline post-training51```5253## Evaluation signals5455- Network nodes are correctly stratified by disease class, visible via distinct color coding with no overlap in node identities across classes.56- Node sizes scale monotonically with association strength (magnitude of correlation or prediction confidence); verify by spot-checking high-magnitude and low-magnitude associations.57- Edge transparency or line width reflects confidence scores: high-confidence edges appear darker or thicker, low-confidence edges fainter or thinner.58- Force-directed layout produces visually coherent clusters with metabolites and proteins associated in the same disease group positioned nearby; hierarchical layout shows clear bipartite layering.59- Output image resolution is ≥300 dpi and file size is suitable for publication (< 50 MB for raster, < 10 MB for vector); visual distinctness of nodes, edges, and labels is confirmed at intended display size.6061## Limitations6263- Network scalability: rendering becomes visually cluttered with > 500 nodes; consider filtering or hierarchical abstraction for larger association sets.64- Layout algorithm sensitivity: force-directed methods are stochastic; multiple runs may yield different (though topologically equivalent) layouts; set random seed for reproducibility.65- Disease stratification assumes discrete disease classes; continuous or multi-state phenotypes require custom node coloring or faceted subplots.66- No built-in statistical testing of network properties (e.g., clustering coefficient, shortest path significance) — these must be computed separately if required.67- File format compatibility: SVG and vector exports may not preserve all visual properties (transparency, gradients) when imported into all document processors.6869## Evidence7071- [other] Construct a bipartite network graph with metabolites and proteins as nodes, weighted by association strength and disease class.: "Construct a bipartite network graph with metabolites and proteins as nodes, weighted by association strength and disease class."72- [other] Apply network layout algorithm (force-directed or hierarchical) to position nodes for interpretability.: "Apply network layout algorithm (force-directed or hierarchical) to position nodes for interpretability."73- [other] Render the network plot with disease-type color coding, node size scaled by association strength, and edge transparency reflecting confidence.: "Render the network plot with disease-type color coding, node size scaled by association strength, and edge transparency reflecting confidence."74- [other] Export the finalized network plot as a high-resolution image file suitable for publication.: "Export the finalized network plot as a high-resolution image file suitable for publication."75- [readme] Disease-associated metabolite-protein network plots. Load the metabolite-disease correlation data and protein association predictions generated by DeepMSProfiler's deep learning module.: "Load the metabolite-disease correlation data and protein association predictions generated by DeepMSProfiler's deep learning module."76- [readme] It takes raw metabolomics data from different disease groups as input and provides three main outputs: 1. Sample disease type labels. 2. Heatmaps depicting the correlation of different metabolite [signals with diseases]. 3. Disease-associated metabolite-protein network plots.: "It takes raw metabolomics data from different disease groups as input and provides three main outputs: 1. Sample disease type labels. 2. Heatmaps depicting the correlation of different metabolite"
HolobiomicsLab