Dimensionality reduction
algorithms
BioVinci automatically runs state-of-the-art methods and recommends the best one to visualize your high dimensional data.
See what's available:
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Principal component analysis (PCA) in 2D/3D |
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t-Distributed Stochastic Neighbor Embedding (t-SNE) |
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Uniform Manifold Approximation and Projection (UMAP) |
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Isometric feature mapping (Isomap) |
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Locally Linear Embedding (LLE) |
Feature selection
algorithm
- Find presentative features for a cluster
- Explore your data using the decision tree model
Interactive heatmap
- Quickly create heatmaps for datasets as large as
105 rows x 105 columns - Easily customize your heatmap
Elegant graphs
for life-science
- Drag and drop to visualize your data quickly
- Instantly apply publication-standard formats
- Flexibly edit graphs
- Export to PNG, SVG
