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

Nicolas F. Fernandez, Gregory W. Gundersen, Adeeb Rahman, Mark L. Grimes, Klarisa Rikova, Peter Hornbeck, Avi Maayan

Research output: Contribution to journalArticlepeer-review

150 Scopus citations

Abstract

Most tools developed to visualize hierarchically clustered heatmaps generate static images. Clustergrammer is a web-based visualization tool with interactive features such as: Zooming, panning, filtering, reordering, sharing, performing enrichment analysis, and providing dynamic gene annotations. Clustergrammer can be used to generate shareable interactive visualizations by uploading a data table to a web-site, or by embedding Clustergrammer in Jupyter Notebooks. The Clustergrammer core libraries can also be used as a toolkit by developers to generate visualizations within their own applications. Clustergrammer is demonstrated using gene expression data from the cancer cell line encyclopedia (CCLE), original post-translational modification data collected from lung cancer cells lines by a mass spectrometry approach, and original cytometry by time of flight (CyTOF) single-cell proteomics data from blood. Clustergrammer enables producing interactive web based visualizations for the analysis of diverse biological data.

Original languageEnglish
Article number170151
JournalScientific data
Volume4
DOIs
StatePublished - Oct 10 2017

Fingerprint

Dive into the research topics of 'Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data'. Together they form a unique fingerprint.

Cite this