UC-6.4 — Enzymatic Hierarchy¶

Module: 6 – Hierarchical and Flow-based Functional Analysis
Visualization type: Treemap (three-level hierarchical composition)
Primary inputs: BioRemPP results table with enzyme_activity, compoundclass, genesymbol, and compoundname
Primary outputs: Hierarchical partitioning of substrate scope across enzyme activities → chemical classes → genes

Scientific Question and Rationale¶

Question: Which enzymatic functions are co-annotated with the widest range of unique compounds, how is this co-annotation breadth distributed across different chemical classes, and which specific genes are the primary contributors?

This use case provides a top-down annotation overview of the enzymatic co-annotation landscape in the dataset. It organizes the system into three levels—enzyme activities, compound classes, and genes—and quantifies for each branch how many unique compounds are co-annotated with it. The resulting treemap may highlight broadly co-annotated enzymatic functions, which chemical classes are their main co-annotation context, and which genes contribute the most to this compound co-annotation breadth.

Data and Inputs¶

Primary data source: BioRemPP_Results.xlsx or BioRemPP_Results.csv
Key columns:
enzyme_activity – functional category/label of the enzymatic activity
compoundclass – chemical class/category of the substrates
genesymbol – gene symbol or identifier implementing that activity in at least one sample
compoundname – specific compound name or identifier
Accepted format: semicolon-delimited text table (.txt or .csv)
Hierarchical structure:
Enzyme Activity (enzyme_activity)
Compound Class (compoundclass)
Gene Symbol (genesymbol)

Analytical Workflow¶

Data Loading
The primary results table (BioRemPP_Results.xlsx or BioRemPP_Results.csv) is loaded from its semicolon-delimited format.
Hierarchy Definition
A three-level hierarchy is defined:
Level 1: enzyme_activity
Level 2: compoundclass (nested within each enzyme activity)
Level 3: genesymbol (nested within each compound class)
Aggregation of Substrate Scope
The data is grouped by each unique (enzyme_activity, compoundclass, genesymbol) path:
for each group, the number of distinct compoundname entries is computed (e.g., via nunique()),
this count represents the substrate scope (number of unique compounds) associated with that gene within that functional and chemical context.
Value Propagation for Treemap
The unique compound counts at the lowest level (per gene) are used as the basic values:
higher-level values for compoundclass and enzyme_activity nodes are obtained by summing the values of all nested nodes,
this yields total substrate scope at each level of the hierarchy.
Rendering
The aggregated data is rendered as an interactive treemap:
each rectangle represents a node in the hierarchy (enzyme activity, compound class, gene),
the area of the rectangle is proportional to its total unique compound count,
color is also mapped to the unique compound count to reinforce the visual encoding.

How to Read the Plot¶

Nested Rectangles (Hierarchy)
The treemap uses nested rectangles to represent the hierarchy:
Outer rectangles represent enzyme activities (enzyme_activity),
within each activity, inner rectangles represent compound classes (compoundclass),
within each class, the smallest rectangles represent genes (genesymbol).
Area (Values) The area of each rectangle is proportional to the total number of unique co-annotated compounds:
for a gene node, area reflects how many distinct compounds that gene is co-annotated with under that activity–class context,
for a compound class node, area reflects the sum of unique compounds co-annotated with all genes contributing to that class,
for an enzyme activity node, area reflects the full compound co-annotation breadth of that activity across classes and genes.
Color Encoding Rectangle color also encodes the unique co-annotated compound count:
brighter or warmer colors indicate broader compound co-annotation coverage,
cooler colors indicate more limited compound co-annotation sets.
Interactivity
In the interactive view:
clicking on a rectangle zooms in to that part of the hierarchy,
hovering displays labels (enzyme activity, compound class, gene) and their associated unique compound counts.

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.

Interpretation and Key Messages¶

Broadly Co-annotated Enzyme Functions The largest and most intensely colored top-level rectangles may identify enzyme activities with the broadest compound co-annotation coverage:
these could correspond to broad annotation classes such as oxidoreductases, transferases, or hydrolases that are co-annotated with many different compounds,
they may represent annotation-rich enzymatic categories in the dataset.
Chemical Class Annotation Breadth within Activities Within a given enzyme activity, the largest compound class rectangles may reveal:
which chemical classes are most broadly co-annotated with that activity,
how compound co-annotation breadth is distributed across chemical space.
High-contributing Genes At the lowest level, large gene rectangles may identify broadly co-annotated genes:
genes co-annotated with many distinct compounds under a given activity–class context,
genes with broad compound co-annotation coverage that may be candidates for further investigation.
System-Level Annotation Overview Taken together, the treemap can offer a compact annotation overview:
it may show where compound co-annotation breadth is concentrated across enzyme activities,
how chemical classes distribute across different enzymatic annotation categories,
and which genes are most broadly co-annotated within those contexts.

Reproducibility and Assumptions¶

Input Format
The analysis assumes a semicolon-delimited table containing:
enzyme_activity, compoundclass, genesymbol, and compoundname.
Value Definition
The fundamental value driving the visualization is the count of unique compound names per (enzyme_activity, compoundclass, genesymbol) group.
Higher-level values are computed as sums of these counts across nested nodes.
Interpretation Scope
Unique-compound count is used as a measure of compound co-annotation breadth; it does not encode enzyme kinetics, expression levels, or in situ activity.
The treemap should therefore be interpreted as a structural and comparative annotation map of where compound co-annotation breadth is concentrated, guiding more detailed mechanistic or experimental studies rather than replacing them.

Activity diagram of the use case¶

Click on the image to enlarge and explore details.