Visualize complex matrices and gene expression data with hierarchical clustering. Plotivy handles the normalization, clustering, and annotation tracks automatically.
Choosing the right distance metric (Euclidean, Pearson) and linkage method (Ward, Complete) requires deep statistical knowledge.
Adding color bars for sample metadata (e.g., Treatment, Time) in Python or R often involves complex data wrangling and grid layouts.
Setting the right center point (z-score = 0) and choosing colorblind-friendly palettes is crucial but often overlooked.
Upload your gene expression matrix, correlation table, or any numerical dataset.
"Cluster rows and columns. Add annotation bars for 'Group' and 'Batch'. Use a blue-white-red color scale."
Get a high-DPI image with perfectly aligned dendrograms and legends.
✨ Plotivy AI generates the complex seaborn clustermap code automatically.
Automatically group similar rows and columns. Choose from various linkage methods and distance metrics.
Easily add color-coded bars to visualize sample metadata alongside your heatmap.
Normalize data by row or column to highlight relative differences rather than absolute values.
Export vector graphics (PDF/SVG) where text remains editable and resolution is infinite.