In the world of biological research, the relationships between genes, species, or even ecosystems can get incredibly complicated. How do you make sense of it all? That’s where biostatistics network plots come into play!
If you’re a beginner, student, researcher, or even a seasoned professional just starting to explore network analysis, this post is for you. I’ll walk you through what network plots are, why they matter in biostatistics, and how you can start creating your own. Ready? Let’s dive in!
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Network Plot in OriginPro |
What Are Biostatistics Network Plots?
In simple terms, a network plot is a visual representation of relationships (or connections) between different entities — often called nodes — and their interactions, which are called edges.
In biostatistics, these nodes and edges could represent:
- Genes and their co-expression relationships
- Species and their ecological interactions
- Patients and shared clinical characteristics
- Biomolecules like proteins interacting in a pathway
Why Are Network Plots Important in Biostatistics?
Network plots aren’t just pretty diagrams — they offer powerful insights:
- Identify hubs: Find genes, species, or individuals that are highly connected.
- Detect communities: Spot clusters or groups that behave similarly.
- Understand complexity: Simplify massive datasets into interpretable visuals.
- Hypothesis generation: Discover unexpected relationships worth further study.
- Communicate findings: Share your data stories more effectively with peers, journals, and the public.
Common Types of Biostatistics Network Plots
Depending on your data and research question, you might use different styles of network plots. Here are some of the most popular:
1. Simple Undirected Networks
- Use case: Showing basic connections without a sense of direction.
- Example: A plot showing which species in a habitat are linked through food sources.
2. Directed Networks
- Use case: Indicating directionality (e.g., gene A activates gene B).
- Example: Pathway diagrams in cellular biology.
3. Weighted Networks
- Use case: Showing the strength of the relationships.
- Example: Stronger genetic similarities between patients are shown with thicker edges.
4. Bipartite Networks
- Use case: Connecting two distinct types of entities.
- Example: Linking microbial species to the metabolites they produce.
Key Elements of a Network Plot
To create or interpret a network plot, you need to understand its basic components:
Element | Description | Example in Biology |
---|---|---|
Node | Entity or individual item | Gene, protein, species |
Edge | Connection between nodes | Co-expression, interaction |
Weight | Strength of the connection | Correlation coefficient |
Direction | Flow of relationship | Gene regulation, disease transmission |
Cluster | Group of closely connected nodes | Functional module, ecological guild |
Download Sample OriginPro Network Plot File
If you prefer working with OriginPro, I’ve prepared a sample data file for you!
You can download it below and start exploring network plots right away.
👉 Download Data File
👉 Download OriginPro Project File
How to Create a Network Plot in OriginPro
Layout Methods in OriginPro – When to Use Them:
Method | Description | When to Use |
---|---|---|
Fruchterman-Reingold | Force-directed layout where edges act like springs and nodes repel each other | ✅ General-purpose; great for clear structure; good for your microbiome data |
Kamada-Kawai | Similar to Fruchterman but emphasizes preserving edge lengths | ✅ Better if you want more uniform distances between connected nodes |
Multidimensional Scaling (MDS) | Projects high-dimensional relationships into 2D or 3D space | ⚠️ Can be used, but might lose some network structure clarity |
Stress | MDS with focus on minimizing stress between distances | ⚠️ Similar to MDS; good for low-dimensional embeddings but less intuitive for network topologies |
Circle | Places nodes in a circular layout | ❌ Not ideal for co-occurrence; doesn't show relationships well unless you're comparing equal roles or groups |
Pivot MDS | Faster, approximate MDS | ⚠️ Good for very large networks, but not better than Fruchterman/Kamada for small microbiome data |
Sparse Stress | Combines force layout with MDS-like stress minimization | ✅ Good if your network is sparse and you want smooth layout |
ForceAtlas 2 | Advanced force-directed algorithm (used in Gephi); preserves clusters well | ✅ Excellent for biological networks; shows modularity and hubs effectively |
Best Practices for Designing Clear Network Plots
When making network plots, keep these tips in mind:
- Avoid clutter: Too many nodes? Consider thresholding weaker edges.
- Use color wisely: Group nodes by type or behavior for easier interpretation.
- Label smartly: Only label key nodes if the network is too crowded.
- Think about layout: Use algorithms (e.g., force-directed layouts) that highlight structure.
- Tell a story: Focus your network on answering a research question.
Real-World Applications of Network Plots in Biostatistics
Wondering where you might see these plots in action? Here are some real-world examples:
- Gene Co-Expression Networks: Understanding which genes work together in disease progression.
- Ecological Networks: Studying predator-prey relationships in a habitat.
- Microbiome Studies: Mapping how different bacterial species interact in the human gut.
- Clinical Networks: Exploring shared symptoms or risk factors across patient populations.