Abstract
Clustered heat maps are the most frequently used graphics for visualization and interpretation of genome-scale molecular profiling data in biology. Construction of a heat map generally requires the assistance of a biostatistician or bioinformatics analyst capable of working in R or a similar programming language to transform the study data, perform hierarchical clustering, and generate the heat map. Our web-based Interactive Heat Map Builder can be used by investigators with no bioinformatics experience to generate high-caliber, publication quality maps. Preparation of the data and construction of a heat map is rarely a simple linear process. Our tool allows a user to move back and forth iteratively through the various stages of map generation to try different options and approaches. Finally, the heat map the builder creates is available in several forms, including an interactive Next-Generation Clustered Heat Map that can be explored dynamically to investigate the results more fully.
Highlights
Many thousands of publications on genomics studies include clustered heat maps (CHMs) because the hierarchical clustering and intuitive visualization provide insight into the relationships among sample sub-groups and key biological processes[1,2,3,4,5,6,7,8]
Construction of a CHM requires data transformation, application of clustering methods, association of covariate data, and production of the heat map visualization. Those tasks require the assistance of an analyst with biostatistics or bioinformatics skills who can work in R or a similar language to manipulate the study data and generate the map. This is usually not a simple linear process because data transformation and clustering methods are often revisited to find the ideal match for the study, and modifications are often made to heat map visualizations to select the best colors, adjust covariates, insert gaps, etc
Our Iterative CHM Builder is a web-based tool for generation of high-quality heat maps that can be used by investigators with no bioinformatics experience and only modest exposure to biostatistical methods
Summary
Many thousands of publications on genomics studies include clustered heat maps (CHMs) because the hierarchical clustering and intuitive visualization provide insight into the relationships among sample sub-groups and key biological processes[1,2,3,4,5,6,7,8]. Construction of a CHM requires data transformation, application of clustering methods, association of covariate (classification) data, and production of the heat map visualization Those tasks require the assistance of an analyst with biostatistics or bioinformatics skills who can work in R or a similar language to manipulate the study data and generate the map. This is usually not a simple linear process because data transformation and clustering methods are often revisited to find the ideal match for the study, and modifications are often made to heat map visualizations to select the best colors, adjust covariates, insert gaps, etc. Other methods of producing NG-CHMs, including an R library and a set of tools for the Galaxy platform[10,11], are described at https://www.ngchm.net/
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