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UC-4.3 — Functional Fingerprint of Pathway by Samples

Module: 4 – Functional and Genetic Profiling
Visualization type: Interactive radar (polar) plot (sample-level KO richness for a selected pathway)
Primary inputs: KEGG_Results.xlsx or KEGG_Results.csv (sample–KO–KEGG pathway associations)
Primary outputs: Polar "functional footprint" of a selected pathway across all samples

Scientific Question and Rationale

Question: For a given metabolic pathway, what is the relative KO annotation richness of each sample, and which samples have the most extensive KO annotations?

This use case focuses on a pathway-centric view: for a selected KEGG pathway, it compares all samples simultaneously in terms of the number of unique KEGG Orthology (KO) identifiers annotated for that pathway in each sample.

Instead of a Cartesian bar chart, the analysis uses a radar (polar) plot to provide an intuitive, shape-based representation of the KO annotation distribution of a pathway across the entire panel of samples. This can enable rapid visual identification of:

  • samples with high KO annotation richness for that pathway, and
  • balanced vs. skewed distributions of KO annotations across samples.

Data and Inputs

  • Primary data source: KEGG_Results.xlsx or KEGG_Results.csv (semicolon-delimited)
  • Key columns:
  • sample – identifier for each biological sample
  • pathname – KEGG pathway name or identifier

  • ko – KEGG Orthology (KO) identifier associated with that sample and pathway

  • User control:

  • A dropdown menu for selecting a single metabolic pathway (pathname) to analyze.

  • Output structure:

  • Axes (θ): one axis per sample, arranged around the circle
  • Radius ®: for each axis, the unique KO count for the selected pathway in that sample
  • Polygon: a closed shape connecting all sample points, representing the pathway's distribution of functional richness across the consortium

Analytical Workflow

  1. Pathway Selection (User Input)
    The user selects a metabolic pathway from an interactive dropdown menu.

  2. This selection corresponds internally to a specific pathname value.

  3. Dynamic Filtering

  4. The KEGG results table KEGG_Results.xlsx or KEGG_Results.csv is loaded.

  5. The dataset is filtered to retain only rows where:

    • pathname matches the selected pathway, and
    • sample and ko are valid and non-missing.

  6. Aggregation of KO Richness per Sample

  7. The filtered data is grouped by sample.
  8. For each sample, the number of distinct KO identifiers is computed (e.g., via nunique() on ko).

  9. This yields a vector of (sample, unique_ko_count) values describing pathway-specific KO richness for each sample.

  10. Rendering as Radar (Polar) Plot

  11. Each sample is mapped to an angular coordinate (θ) around the circle.
  12. The corresponding radius ® for each sample is the unique KO count.
  13. A closed polygon is drawn by connecting the points in order, optionally with markers at each vertex:
    • axes: samples
    • radius: pathway-specific KO richness

How to Read the Plot

  • Dropdown Menu (Pathway Selection)
  • Use the menu to select the Metabolic Pathway of interest.

  • The radar plot recomputes and updates automatically for the chosen pathway.

  • Axes (θ – Samples)

  • Each radial axis emanating from the center corresponds to one Sample.

  • All samples involved in the selected pathway are arranged around the circle.

  • Radius (r – KO Richness)

  • The distance from the center along a given axis represents the count of unique KOs that sample contributes to the selected pathway.

  • Larger radius values indicate greater pathway-specific KO richness.

  • Polygon Shape (KO Annotation Distribution)

  • The polygon connecting all sample points encodes the overall distribution of KO annotation richness for that pathway across samples:
    • a symmetrical, evenly expanded shape may indicate more balanced KO annotation coverage
    • a skewed shape stretched towards one or a few axes may highlight samples with particularly high KO annotation richness for that pathway

Representative Output

The image below illustrates a representative output generated by this use case using the example dataset.

Click on the image to enlarge and explore details.

Representative output for UC-4.3

Interpretation and Key Messages

  • Samples with High KO Annotation Richness
  • Points further from the center on a given axis may represent higher KO annotation richness for that sample in the selected pathway.

  • These high-radius samples could be annotation-level candidates for prioritized experimental investigation of that pathway (experimental validation required to confirm functional roles).

  • Identifying Samples with Concentrated KO Annotations

  • If the radar polygon is heavily skewed towards particular axes, it may indicate that a small subset of samples carries most of the KO annotations for the selected pathway.

  • Such samples may be worth examining as starting points for annotation-guided experimental design.

  • How KO Annotation Patterns Shift Across Pathways

  • By switching between different pathways via the dropdown, users can observe how KO annotation distributions shift from one pathway to another.

  • Distributed vs. Concentrated KO Annotations

  • A radar plot where several axes reach similar radii may suggest a pathway whose KO annotations are broadly distributed across samples, potentially indicating annotation-level redundancy.
  • Conversely, a plot where only one or two axes reach high values may suggest that the pathway's KO annotations are concentrated in few samples, which may be worth noting for annotation-guided hypothesis generation.

Reproducibility and Assumptions

  • Input Format
    The analysis requires a semicolon-delimited KEGG results table with at least:
  • sample,
  • pathname,

  • ko.

  • Definition of Pathway Richness

  • For each sample, pathway richness is defined as the count of unique KOs annotated to the selected pathway.

  • Multiple occurrences of the same (sample, pathname, ko) combination do not increase the count.

  • Scope and Limitations

  • The metric reflects KO annotation presence, not expression levels, regulatory control, or actual metabolic flux.
  • Radar plots are most interpretable when the number of samples (axes) is moderate; very large sample sets may require pre-filtering or grouping for clarity.

Activity diagram of the use case

Click on the image to enlarge and explore details.

Activity diagram of the use case