Welcome to Clustergrammer’s Documentation!

Clustergrammer is a web-based tool for visualizing high-dimensional data (e.g. a matrix) as an interactive and shareable hierarchically clustered heatmap. 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. 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:

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 notebook workflow, the Clustergrammer Jupyter Widget enables visualizations to be built within Jupyter notebooks and shared through Jupyter’s nbviewer (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.

Use Cases

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 (e.g. a matrix). Below are links to several example use cases (see Case Studies and Examples for more information):

Funding and Contact

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.

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

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