Welcome to Clustergrammer’s Documentation!

Clustergrammer is a web-based tool for visualizing and analyzing high-dimensional data as interactive and shareable hierarchically clustered heatmaps (see Introduction to Clustergrams). Clustergrammer produces highly interactive visualizations that enable intuitive exploration of high-dimensional data and has several optional biology-specific features (e.g. enrichment analysis; see Biology-Specific Features) to facilitate the exploration of gene-level biological data. The project is free and open-source (all code is available on GitHub).

Press play or interact with the gene-expression demo above to see some of Clustergrammer’s interactive features and refer to Interacting with the Visualization for more information.

What’s New


Clustergrammer is currently being re-built using the WebGL library regl:

demo GIF

Users can try out Clustergrammer2 on NBViewer, MyBinder, Kaggle, and Saturn.io (see PBMC 2700).

version version

Also, see Case Studies and Tutorials and Clustergrammer2-Examples.

JupyterCon 2018

The Clustergrammer-Widget was presented at JupyterCon 2018.

Using Clustergrammer

The easiest ways to use Clustergrammer to produce an interactive visualization of your data are to:

The Clustergrammer-Web is the quickest way to generate an interactive and shareable visualization (see example visualization and getting started Web-app). For users who want to visualize their data within a Jupyter notebook, the Clustergrammer-Widget enables visualizations to be embedded into shareable Jupyter notebooks (see example notebook and Getting Started Widget).

Web developers can use Clustergrammer’s core libraries, Clustergrammer-JS and Clustergrammer-PY, or the Clustergrammer-Web API to dynamically generate visualizations for their own web applications (see examples in App Integrations).

Please read the Getting Started guide for more information.

Case Studies and Examples

Clustergrammer was developed to visualize high-dimensional biological data (e.g. genome-wide expression data), but it can also generally be applied to any high-dimensional data. Please refer to the Case Studies and Tutorials and links below for more information:


Please contact Nicolas Fernandez (nicolas.fernandez@mssm.edu) and Avi Ma’ayan (avi.maayan@mssm.edu) for support, comments, and suggestions.

Citing Clustergrammer

Please consider supporting Clustergrammer by citing our publication:

Fernandez, N. F. et al. Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data. Sci. Data 4:170151 doi: 10.1038/sdata.2017.151 (2017).


Clustergrammer is being developed by the Ma’ayan Lab and the Human Immune Monitoring Center at the Icahn School of Medicine at Mount Sinai for the BD2K-LINCS DCIC and the KMC-IDG.