Plotivy for Biology Research

From enzyme kinetics to cell imaging, create publication-ready biological data visualizations in minutes.

Visualize Your Biological Data

Enzyme Kinetics

Michaelis-Menten curves, Lineweaver-Burk plots, enzyme inhibition analysis with automatic Km and Vmax determination.

"Plot velocity vs substrate concentration, fit Michaelis-Menten, show Km"
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Growth Curves

Bacterial/cell growth curves with lag phase, exponential phase, and stationary phase identification. Doubling time calculations.

  • ✓ Automated growth phase detection
  • ✓ Doubling time calculation
  • ✓ Multiple condition comparison

Dose-Response Curves

IC50/EC50 determination, sigmoidal curve fitting, drug screening analysis with confidence intervals.

  • ✓ IC50/EC50 calculation
  • ✓ Hill slope determination
  • ✓ Statistical comparison

Gene Expression

qPCR analysis, RNA-seq visualization, heatmaps for differential expression, volcano plots, and GO enrichment.

  • ✓ ΔΔCt calculations
  • ✓ Fold change visualization
  • ✓ Statistical significance

Example Biology Datasets

Enzyme Kinetics

Michaelis-Menten enzyme rate data with Km determination

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Growth Curve

Bacterial growth with exponential phase analysis

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Dose-Response

IC50 calculation for drug screening

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Chart gallery

Gallery templates for biology workflows

Open the ready-made gallery recipes most biologists need: violin and box plots for group comparisons, plus heatmaps for gene expression.

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Violin plot comparing score distributions across 3 groups with inner box plots
Distribution•seaborn, matplotlib
From the chart gallery•Comparing treatment effects across groups

Violin Plot

Combines box plots with kernel density to show distribution shape across groups.

Sample code / prompt

Create a violin plot comparing 'Exam Scores' across 3 treatment groups: Control, Treatment A, and Treatment B. Generate realistic educational data with 50 students per group. Control: mean=72, sd=12 (normal). Treatment A: mean=78, sd=10 (slight improvement). Treatment B: mean=82, sd=8 (significant improvement, less variance). Include embedded box plots showing quartiles, median line, and mean diamond marker. Add individual data points as a strip plot with jitter (alpha=0.3). Perform and annotate ANOVA p-value. Use distinct colors for each group. Add horizontal reference line at passing score (70). Title: 'Effect of Study Interventions on Exam Performance'.
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Box and whisker plot comparing gene expression across 4 genotypes with significance brackets
Distribution•seaborn, matplotlib
From the chart gallery•Comparing experimental groups in scientific research

Box and Whisker Plot

Displays data distribution using quartiles, median, and outliers in a standardized format.

Sample code / prompt

Create a publication-ready box plot comparing 'Gene Expression Levels' (normalized counts) across 4 genotypes: WT (Wild Type), KO1 (Knockout 1), KO2 (Knockout 2), and Mutant. Generate a realistic dataset with n=20 biological replicates per group, with KO1 showing upregulation (~1.5x WT), KO2 showing downregulation (~0.8x WT), and Mutant showing moderate increase (~1.2x WT). Overlay jittered individual data points with transparency. Perform pairwise t-tests against WT control and add significance brackets with stars (* p<0.05, ** p<0.01, *** p<0.001, ns for non-significant). Use a colorblind-friendly palette, add y-axis label with units, and include sample size (n=) in x-axis labels.
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Correlation heatmap with diverging color scale and coefficient annotations
Statistical•seaborn, matplotlib
From the chart gallery•Correlation analysis between variables

Heatmap

Represents data values as colors in a two-dimensional matrix format.

Sample code / prompt

Create a heatmap showing the correlation matrix for 8 financial metrics: 'Revenue', 'Profit', 'Expenses', 'ROI', 'Customer Count', 'Avg Order Value', 'Marketing Spend', 'Employee Count'. Generate realistic correlation data where logically related metrics are positively correlated (Revenue-Profit: 0.85, Marketing-Revenue: 0.72) and others have weak or negative correlations (Expenses-Profit: -0.45). Use a diverging RdBu colorscale centered at zero (-1 to +1 range). Display correlation coefficients inside each cell with 2 decimal places. Mask the upper triangle to avoid redundancy. Add clear axis labels, a color bar, and title 'Financial Metrics Correlation Matrix'.
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