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’s front end (Clustergrammer-JS) is built using D3.js and its back end (Clustergrammer-PY) is built using Python. 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 and can be found on GitHub. Press play or interact with the gene-expression demo below to see some of Clustergrammer’s interactive features and refer to Interacting with the Visualization for more information:

Using Clustergrammer

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

The Clustergrammer Web App 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 Jupyter 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 Integration Examples).

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 Examples and links below for more information:

Contact

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

Funding

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

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