Align, analyze, & ah-ha!! Next Generation Dynamic Clustered Heat Maps (NG-CHM) in Galaxy
Year: 2018
Type: other
Abstract: Clustered heat maps are the most frequently used graphics for visualization of molecular profiling data in biology, appearing in many thousands of publications— but as static images. To provide more capability for exploration of large data matrices, we have now developed highly interactive “Next Generation” Clustered Heat Maps (NG-CHMs) and implemented them in the Galaxy framework. NG-CHMs enable the user to zoom and navigate dynamically and link out to dozens of external data resources and tools. NG-CHMs exploit recent advances in web technology to improve performance, provide a highly responsive user experience, and facilitate deep exploration of the biology behind the image. NG-CHMs include the following interactive capabilities (among many others): Extreme zooming without loss of resolution for drill-down into large data matrices. Fluent navigation. Link-outs from labels or pixels to a variety of pertinent annotation resources, including GeneCards, PubMed, the Gene Ontology, Google, and cBioPortal. Annotation with pathway data. Flexible real-time recoloring. High-resolution graphics that meet the requirements of all major journals. Capture of all metadata necessary to reproduce any chosen state of the map, even months or years later. NG-CHMs have been widely used throughout projects of The Cancer Genome Atlas (TCGA) project of the NIH. An interactive compendium of 297 NG-CHMs for TCGA data at the protein, RNA, and (soon) DNA levels is at http://tcga.ngchm.net . We will illustrate the capabilities of NG-CHMs using data from the compendium. However, the system fully supports data from other domains, as well as many other forms of heat map. NG-CHMs are available via the Galaxy Toolshed at https://toolshed.g2.bx.psu.edu/ enter “Heat Map” in the search box. Two versions are available for your local Galaxy installation – normal and advanced.
Authors: Bob Brown, Mike Ryan, Bradley Broom
Institution: The University of Texas MD Anderson Cancer Center
Language: Hebrew
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W2965855138
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