Clustered heat maps are the most frequently used graphics for visualization of molecular profiling data in biology. However, they are generally rendered as static, or only modestly interactive, images. We have now used recent advances in web technologies to produce interactive “next-generation” clustered heat maps (NG-CHMs) that enable extreme zooming and navigation without loss of resolution. NG-CHMs also provide link-outs to additional information sources and include other features that facilitate deep exploration of the biology behind the image. Here, we describe an implementation of the NG-CHM system in the Galaxy bioinformatics platform. We illustrate the algorithm and available computational tool using RNA-seq data from The Cancer Genome Atlas program’s Kidney Clear Cell Carcinoma project.
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. How to cite this article: et al. Interactive Clustered Heat Map Builder: An easy web-based tool for F1000Research 2020, (ISCB Comm J):1750 ( creating sophisticated clustered heat maps [version 2; peer review: 2 approved] 8 ) https://doi., (ISCB Comm J):1750 ( ) First published: 8 https://doi.org/10.12688/f1000research.20590.1 This version of the article has been revised to address the comments and questions of reviewers and to update the manuscript to reflect a few updates in the latest release of the Interactive CHM Builder. Any further responses from the reviewers can be found at the end of the article REVISED PubMed Abstract | Publisher Full Text 2. Weinstein JN, Myers TG, O'Connor PM, et al.: An information-intensive approach to the molecular pharmacology of cancer. Science. 1997; 275(5298): 343-9. PubMed Abstract | Publisher Full Text 3. Myers TG, Anderson NL, Waltham M, et al.: A protein expression database for the molecular pharmacology of cancer. Electrophoresis. 1997; 18(3-4): 647-53. PubMed Abstract | Publisher Full Text 4. Eisen MB, Spellman PT, Brown PO, et al.: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A. 1998; 95(25): 14863-8. PubMed Abstract | Publisher Full Text | Free Full Text 5. Scherf U, Ross DT, Waltham M, et al.: A gene expression database for the molecular pharmacology of cancer. Nat Genet. 2000; 24(3): 236-44. PubMed Abstract | Publisher Full Text 6. Ross DT, Scherf U, Eisen MB, et al.: Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000; 24(3): 227-35. PubMed Abstract | Publisher Full Text 7. Zeeberg BR, Qin H, Narasimhan S, et al.: High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID). BMC Bioinformatics. 2005; 6: 168. PubMed Abstract | Publisher Full Text | Free Full Text 8. Weinstein JN: Biochemistry. A postgenomic visual icon. Science. 2008; 319(5871): 1772-3. PubMed ...
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.
OmicPioneer-sc is an open-source data visualization/analysis package that integrates dimensionality-reduction plots (DRPs) such as t-SNE and UMAP with Next-Generation Clustered Heat Maps (NGCHMs) and Pathway Visualization Modules (PVMs) in a seamless, highly interactive exploratory environment. It includes fluent zooming and navigation, a statistical toolkit, dozens of link-outs to external public bioinformatic resources, high-resolution graphics that meet the requirements of all major journals, and the ability to store all metadata needed to reproduce the visualizations at a later time. A user-friendly, multi-panel graphical interface enables non-informaticians to interact with the system without programming, asking and answering questions that require navigation among the three types of modules or extension from them to the Gene Ontology or information on therapies. The visual integration can be useful for detective work to identify and annotate cell-types for color-coding of the DRPs, and multiple NGCHMs can be layered on top of each other (with toggling among them) as an aid to multi-omic analysis. The tools are available in containerized form with APIs to facilitate incorporation as a plug-in to other bioinformatic environments. The capabilities of OmicPioneer-sc are illustrated here through application to a single-cell RNA-seq airway dataset pertinent to the biology of both cancer and COVID-19
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