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Single-cell data
visualization

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:

Principal component analysis (PCA) in 2D/3D

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Uniform Manifold Approximation and Projection (UMAP)

Isometric feature mapping (Isomap)

Locally Linear Embedding (LLE)

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Feature selection
algorithm

  • Find presentative features for a cluster
  • Explore your data using the decision tree model
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Interactive heatmap

  • Quickly create heatmaps for datasets as large as
    105 rows x 105 columns
  • Easily customize your heatmap
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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
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histogram
pie
bar
line
scatter
violin
box
venn
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