Biology Data Visualization: From Western Blots to Dose-Response Curves
Biology is messy. Unlike physics or chemistry where laws are often absolute, biological data is full of noise, variability, and outliers. If you've ever stared at a Western blot wondering how to turn it into a convincing bar chart, or spent hours trying to fit a dose-response curve that just won't cooperate—you're not alone.
Visualizing biological data effectively means finding the signal in the noise without misrepresenting the underlying variability. Whether you're a molecular biologist, ecologist, or neuroscientist, this guide covers the essential plot types you need for your next paper.
📋 What You'll Learn
- • How to create publication-ready dose-response curves
- • Kaplan-Meier survival analysis best practices
- • Western blot quantification that reviewers love
- • Gene expression heatmaps with proper color scales
1. Dose-Response Curves (IC50/EC50)
Pharmacology and biochemistry rely heavily on dose-response curves. The key is fitting a 4-parameter logistic (4PL) or Hill equation to your data—but getting the fit right can be frustrating.
✅ Best Practice: Log-Scale X-Axis
Always plot concentration on a logarithmic scale (X-axis) to see the sigmoidal shape clearly. Ensure your error bars (SEM or SD) are visible at each concentration point.
Prompt: "Create a dose-response curve of Drug Concentration vs Cell Viability. Use a log scale for the X-axis and fit a sigmoidal curve to calculate IC50."2. Survival Analysis (Kaplan-Meier)
For clinical trials or animal studies, Kaplan-Meier plots are the gold standard. They show the probability of survival (or any event) over time, and reviewers expect them to follow specific conventions.
🔑 Key Elements Reviewers Look For
- Censored Data: Mark censored subjects (those who left the study or didn't experience the event) with tick marks on the curve.
- P-value: Include the Log-rank test p-value directly on the plot—don't make reviewers hunt for it.
- Risk Table: Align a "Number at Risk" table below the X-axis to show sample sizes at each time point.
3. Western Blot Quantification
"Representative images" are great, but quantification is what convinces reviewers. The problem? Bar charts hide too much information when sample sizes are small (n < 10).
✅ Best Practice: Dot Plots with Error Bars
Overlay individual data points on top of your summary statistics (mean ± SEM). This shows the reader exactly how many replicates you ran and the spread of the data—which is increasingly required by top journals.
Try plotting Western Blot data →4. Heatmaps for Gene Expression
When dealing with RNA-seq or microarray data, heatmaps are essential for visualizing clusters of up-regulated and down-regulated genes. But color choice can make or break your figure.
🎨 Color Choice Matters
Avoid the red-green color scale—about 8% of men are colorblind and cannot distinguish between them. Use a diverging Blue-White-Red or Purple-White-Orange scale instead.
Pro tip: Many journals now require colorblind-accessible figures. Using the right palette from the start saves revision headaches.
Common Biology Visualization Mistakes
After reviewing hundreds of biology papers, these mistakes show up again and again:
- Using Bar Charts for Paired Data: If you measured the same subject before and after treatment, use a paired line plot (slopegraph) instead of two bars. It shows the effect size much more clearly.
- Hiding N: Always state the number of biological replicates (n) in the figure legend. Reviewers will ask for it if it's missing—and they'll be annoyed.
- Over-smoothing: Don't smooth your line plots unless you have a mathematical model justifying it. Connect points with straight lines for time-series data.
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