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Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

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Published on: October 2, 2016

Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data.

Nicolas F Fernandez¹, Gregory W Gundersen¹, Adeeb Rahman²

¹Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, BD2K-LINCS Data Coordination and Integration Center (DCIC), Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA.

Scientific Data

|October 11, 2017

Summary

This summary is machine-generated.

Clustergrammer offers interactive, web-based visualizations for complex biological data, moving beyond static images. This tool enhances data analysis through features like zooming, filtering, and dynamic annotations for gene expression and proteomics.

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Area of Science:

Bioinformatics
Computational Biology
Data Visualization

Background:

Traditional heatmap visualization tools produce static images, limiting interactive data exploration.
Hierarchically clustered heatmaps are crucial for analyzing complex biological datasets.

Purpose of the Study:

To introduce Clustergrammer, a novel web-based tool for interactive visualization of hierarchically clustered heatmaps.
To provide a flexible platform for researchers to explore, share, and analyze biological data dynamically.

Main Methods:

Development of a web-based visualization tool, Clustergrammer.
Integration of interactive features including zooming, panning, filtering, and reordering.
Demonstration using diverse biological datasets: gene expression (CCLE), mass spectrometry (PTM), and single-cell proteomics (CyTOF).

Main Results:

Clustergrammer generates shareable, interactive visualizations from uploaded data tables.
The tool can be embedded within Jupyter Notebooks for seamless integration into existing workflows.
Core libraries allow developers to incorporate interactive visualizations into custom applications.

Conclusions:

Clustergrammer overcomes limitations of static heatmap visualizations.
It facilitates dynamic analysis and sharing of diverse biological data, including gene expression and proteomics.
The toolkit empowers researchers and developers with advanced interactive visualization capabilities.