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Publication-ready visualizations for molecular biology, genomics, cell biology, ecology, and clinical research. Every chart includes complete Python code you can copy and run.
Biology papers rely on precise visual communication of experimental results. From volcano plots showing differential gene expression to Kaplan-Meier survival curves, each chart type serves a specific scientific purpose. The code examples below follow journal formatting standards (300+ DPI, proper font sizes, colorblind-safe palettes) so your figures are ready for submission.
Biology chart selection
Start with the biological question. Group comparisons need charts that reveal spread and sample size, omics workflows need charts that make many features readable, and survival studies need time-to-event visualization. The links below connect this chart index to the relevant Plotivy guides.
Use box plots, violin plots, dot plots, or bar charts with error bars when comparing experimental groups.
Use volcano plots and heatmaps when the goal is to surface gene-level or matrix-level patterns.
Use Kaplan-Meier survival curves when the key measurement is time until an event occurs.
Statistical tests, curve fitting, and analysis techniques with code.
T-tests, ANOVA, regression, and p-value annotation in Python.
Complete tutorial for biological data analysis and plotting.
Forest plots, CONSORT diagrams, and clinical trial figures.
Matplotlib is the standard for publication figures due to its precise control over formatting. Seaborn adds statistical plotting functions (violin plots, heatmaps) and works on top of matplotlib. For interactive exploration, Plotly is excellent. Most biology journals accept figures generated by any of these libraries as long as formatting requirements are met.
Use matplotlib lines and text annotations to draw brackets between groups, or use the statannotations library for automated placement. The standard notation is: ns (not significant), * (p < 0.05), ** (p < 0.01), *** (p < 0.001). See our t-test visualization guide for complete code.
Most journals now recommend showing individual data points for experiments with n < 20. A dot plot or strip plot overlaid on a bar chart (showing the mean and error bars) is the current best practice. This approach reveals the actual data distribution rather than hiding it behind a bar.
300 DPI minimum for line art and combinations, 600 DPI for line art only. Nature, Science, Cell, and most biology journals require 300 DPI at final print size. Always export at the exact column width specified by the journal to avoid rescaling artifacts.