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UC-8.6 — Pathway-Centric Consortium Design by KO Coverage

Module: 8 – Assembly of Functional Consortia
Visualization type: Interactive UpSet plot (KO set intersections across samples for a selected pathway)
Primary inputs: HADEG_Results.xlsx or HADEG_Results.csv (sample–KO–pathway associations)
Primary outputs: Set- and intersection-level view of KO coverage across samples for one target pathway

Scientific Question and Rationale

Question: For a specific metabolic pathway, how are its annotated KO identifiers distributed across different samples, and what does their overlap reveal about annotation redundancy and complementarity?

The analysis focuses on a single target degradation pathway at a time and examines how its annotated KEGG Orthology (KO) identifiers are distributed across the available samples. By representing each sample as a set of pathway-specific KOs and analyzing the intersections of these sets via an UpSet plot, this visualization can provide a pathway-centric lens on:

  • annotation redundancy (shared KOs),
  • annotation complementarity (unique KOs contributed by different samples), and
  • potential keystone samples that uniquely carry KOs absent from all other samples.

This may support annotation-guided, pathway-oriented hypothesis generation for candidate consortium assembly (experimental validation required to confirm functional capacity).

Data and Inputs

  • Primary data source: HADEG_Results.xlsx or HADEG_Results.csv (semicolon-delimited)
  • Key columns:
  • sample – identifier for each biological sample
  • ko – KEGG Orthology (KO) identifier annotated for that sample

  • compound_pathway – HADEG pathway label associated with the KO

  • Set-based elements:

  • Sets: Individual samples (each represented by the KOs it contributes to the selected pathway).
  • Elements: Unique KOs required for that pathway within the dataset.

Analytical Workflow

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

  2. Internally, this corresponds to a specific compound_pathway value.

  3. Dynamic Filtering
    The HADEG results table HADEG_Results.xlsx or HADEG_Results.csv is filtered to retain only rows where:

  4. compound_pathway equals the selected pathway, and
  5. both sample and ko are valid and non-missing.

This subset represents all observed KO contributions to the chosen pathway across all samples.

  1. Set Construction (Sample-wise KO Repertoires)
    The filtered data is grouped by sample.
  2. For each sample, a set of unique KOs (ko) associated with the selected pathway is constructed.

  3. Each set defines the pathway-specific KO annotation profile of that sample.

  4. Intersection Calculation and UpSet Data Preparation

  5. All sample-specific KO sets are used to compute set sizes (per sample) and intersection sizes (KO counts shared by specific combinations of samples).

  6. Intersections are ranked by cardinality (number of KOs in each intersection) to emphasize the most functionally relevant overlaps.

  7. Rendering as UpSet Plot
    The processed set and intersection statistics are rendered as an UpSet plot composed of:

  8. a left bar chart (set sizes),
  9. a bottom intersection matrix (connected dots),
  10. and a top bar chart (intersection sizes).

How to Read the Plot

  • Dropdown Menu
    Use the menu to select the Target Pathway (compound_pathway) to analyze.
    The entire analysis (filtering, set construction, intersection computation, and plotting) is recomputed for the selected pathway.

  • Set Size (Left Bar Chart)

  • Each bar on the left represents one Sample.
  • The height of the bar equals the number of unique KOs from that pathway present in that sample.

  • This provides a direct measure of how many pathway KOs each sample contributes individually.

  • Intersection Matrix (Bottom Panel)

  • Each row corresponds to a sample.
  • Each column corresponds to a specific intersection (combination of samples).
  • Connected dots in a column indicate which samples participate in that particular intersection.

  • For example, a column with dots connected for Sample A and Sample B (and no others) represents KOs shared exclusively between A and B (for this pathway).

  • Intersection Size (Top Bar Chart)

  • Each bar above the matrix represents the size of the intersection defined by the connected dots directly below.
  • Bar height equals the number of KOs in that specific combination of samples.
  • Taller bars correspond to large shared KO subsets, while shorter bars indicate smaller overlaps or unique contributions.

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-8.6

Interpretation and Key Messages

  • KO Annotation Redundancy
  • Large bars over intersections involving multiple samples may indicate strong annotation redundancy: many samples share the same pathway KOs.

  • Redundant KOs at the annotation level may be a starting point for robustness hypotheses, though functional compensation requires experimental validation.

  • KO Annotation Complementarity

  • Intersections with small but distinct KO sets, especially when combined across columns, may highlight complementary annotation contributions.

  • If the union of KOs across several intersections is required to cover the full pathway KO set in the dataset, the pathway has distributed annotation coverage across multiple samples; candidate consortia may be worth exploring.

  • Annotation-unique Samples

  • Bars above a single, unconnected dot (i.e., KOs present only in one sample) may indicate unique KO annotations contributed by that sample.

  • A sample that uniquely carries KOs relevant to pathway completeness at the annotation level could be considered an annotation-level keystone candidate for that pathway (experimental validation required).

  • Annotation-guided Consortium Hypothesis

  • The UpSet plot may help identify minimal candidate consortia at the annotation level:
    • prioritize samples that jointly cover the largest number of unique pathway KOs,
    • minimize annotation redundancy when desired (for parsimony), or preserve it as a proxy for potential robustness.
  • Users can conceptually apply set cover reasoning: choose the smallest set of samples whose KO union approximates or achieves full pathway KO coverage in the dataset.

Reproducibility and Assumptions

  • Input Format
    The analysis requires a semicolon-delimited HADEG results table containing at least:
  • sample,
  • ko,

  • compound_pathway (pathway identifier to be used in the dropdown selection).

  • Set Definition

  • Each sample's set consists of unique KOs for the selected pathway only.

  • Multiple occurrences of the same (sample, ko, compound_pathway) entry in the raw data do not affect set size (duplicates are removed).

  • Pathway KO Universe

  • The total KO universe for a pathway is defined by all unique KOs observed for that compound_pathway in HADEG_Results.xlsx or HADEG_Results.csv.

  • No external canonical KO list is enforced.

  • Interpretation Scope

  • The UpSet plot encodes KO annotation presence/absence relationships, not kinetic rates, expression levels, confirmed functional capacity, or regulatory control.
  • It should be interpreted as a structural map of KO annotation distribution across samples, to be integrated with completeness scores (UC-8.4 / UC-8.5) and other BioRemPP analyses during annotation-guided hypothesis generation.

Activity diagram of the use case

Click on the image to enlarge and explore details.

Activity diagram of the use case