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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 :ref:`intro_heatmap_clustergram`). Clustergrammer's front end (:ref:`clustergrammer_js`) is built using D3.js and its back end (:ref:`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 :ref:`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 above to see some of Clustergrammer's interactive features and refer to :ref:`interacting_with_viz` for more information.

What's New

JupyterCon 2018

The Clustergrammer-Widget was recently presented at JupyterCon 2018.

Clustergrammer2

demo GIF

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

Try running the Clustergrammer2 Jupyter widget on MyBinder

version

version

and see Clustergrammer2-Examples.

Using Clustergrammer

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

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

Web developers can use Clustergrammer's core libraries, :ref:`clustergrammer_js` and :ref:`clustergrammer_py`, or the :ref:`clustergrammer_web_api` to dynamically generate visualizations for their own web applications (see examples in :ref:`app_integration`).

Please read the :doc:`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 :ref:`case_studies` 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.

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).

Funding

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.

Contents:

.. toctree::
   :maxdepth: 2

   getting_started
   clustergrammer_web
   clustergrammer_widget
   clustergrammer2
   interacting_with_viz
   biology_specific_features
   case_studies
   matrix_format_io
   building_webpage
   clustergrammer_js
   clustergrammer_gl
   clustergrammer_py
   app_integration
   developing_with_clustergrammer
   license

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