Skip to main content
Log in

An interactive cluster heat map to visualize and explore multidimensional metabolomic data

  • Short Communication
  • Published:
Metabolomics Aims and scope Submit manuscript

Abstract

Heat maps are a commonly used visualization tool for metabolomic data where the relative abundance of ions detected in each sample is represented with color intensity. A limitation of applying heat maps to global metabolomic data, however, is the large number of ions that have to be displayed and the lack of information provided about important metabolomic parameters such as m/z and retention time. Here we address these challenges by introducing the interactive cluster heat map in the data-processing software XCMS Online. XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process, statistically evaluate, and visualize mass-spectrometry based metabolomic data. An interactive heat map is provided for all data processed by XCMS Online. The heat map is clickable, allowing users to zoom and explore specific metabolite metadata (EICs, Box-and-whisker plots, mass spectra) that are linked to the METLIN metabolite database. The utility of the XCMS interactive heat map is demonstrated on metabolomic data set generated from different anatomical regions of the mouse brain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

References

  • Deu-Pons, J., Schroeder, M. P., & Lopez-Bigas, N. (2014). jHeatmap: An interactive heatmap viewer for the web. Bioinformatics,. doi:10.1093/bioinformatics/btu094.

    PubMed  Google Scholar 

  • Dumas, M. E., & Davidovic, L. (2013). Metabolic phenotyping and systems biology approaches to understanding neurological disorders. F1000Prime Reports, 5, 5–18.

  • Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, 95, 14863–14868.

    Article  CAS  Google Scholar 

  • Fahy, E., Sud, M., Cotter, D., & Subramaniam, S. (2007). LIPID MAPS online tools for lipid research. Nucleic Acids Research, 35, 21.

    Article  Google Scholar 

  • Gowda, H., et al. (2014). Interactive XCMS Online: Simplifying advanced metabolomic data processing and subsequent statistical analyses. Analytical Chemistry, 86, 6931–6939.

  • Ivanisevic, J., et al. (2014). Brain region mapping using global metabolomics. Chemistry & Biology, 21, 1575–1584.

  • Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28, 27–30.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Mandal, R., et al. (2012). Multi-platform characterization of the human cerebrospinal fluid metabolome: A comprehensive and quantitative update. Genome Medicine, 4, 38.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Meunier, B., Dumas, E., Piec, I., Béchet, D., Hébraud, M., & Hocquette, J.-F. (2006). Assessment of hierarchical clustering methodologies for proteomic data mining. Journal of Proteome Research, 6, 358–366. doi:10.1021/pr060343h.

    Article  Google Scholar 

  • Nicholson, J. K., Holmes, E., Kinross, J. M., Darzi, A. W., Takats, Z., & Lindon, J. C. (2012). Metabolic phenotyping in clinical and surgical environments. Nature, 491, 384–392.

    Article  CAS  PubMed  Google Scholar 

  • Patti, G. J., Yanes, O., & Siuzdak, G. (2012a). Innovation: Metabolomics: The apogee of the omics trilogy. Nature Reviews Molecular Cell Biology, 13, 263–269.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Patti, G. J., et al. (2012b). A view from above: Cloud plots to visualize global metabolomic data. Analytical Chemistry, 85, 798–804. doi:10.1021/ac3029745.

    Article  PubMed Central  PubMed  Google Scholar 

  • Piomelli, D., Astarita, G., & Rapaka, R. (2007). A neuroscientist’s guide to lipidomics. Nature Reviews Neuroscience, 8, 743–754.

    Article  CAS  PubMed  Google Scholar 

  • Rinehart, D., et al. (2014). Metabolomic data streaming for biology-dependent data acquisition. Nature Biotechnology, 32, 524–527. doi:10.1038/Nbt.2927.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Skuta, C., Bartunek, P., & Svozil, D. (2014). InCHlib—Interactive cluster heatmap for web applications. Journal of Cheminformatics,. doi:10.1186/S13321-014-0044-4.

    PubMed Central  PubMed  Google Scholar 

  • Tautenhahn, R., Cho, K., Uritboonthai, W., Zhu, Z., Patti, G. J., & Siuzdak, G. (2012). An accelerated workflow for untargeted metabolomics using the METLIN database. Nature Biotechnology, 30, 826–828.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  • Wilkinson, L., & Friendly, M. (2009). The history of the cluster heat map. The American Statistician, 63, 179–184. doi:10.1198/tas.2009.0033.

    Article  Google Scholar 

  • Wishart, D. S., et al. (2009). HMDB: A knowledgebase for the human metabolome. Nucleic Acids Research, 37, 25.

    Article  Google Scholar 

  • Wu, W., & Noble, W. S. (2004). Genomic data visualization on the web. Bioinformatics, 20, 1804–1805. doi:10.1093/bioinformatics/bth154.

    Article  CAS  PubMed  Google Scholar 

  • Xia, J., & Wishart, D. S. (2011). Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nature Protocols, 6, 743–760.

    Article  CAS  PubMed  Google Scholar 

  • Zhu, Z.-J., et al. (2013). Liquid chromatography quadrupole time-of-flight mass spectrometry characterization of metabolites guided by the METLIN database. Nature Protocols, 8, 451–460.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported, in part, by the University of Nebraska Foundation which includes individual donations from Dr. Carol Swarts and Frances and Louie Blumkin and National Institutes of Health Grants P01 MH64570, RO1 MH104147, P01 DA028555, R01 NS36126, P01 NS31492, 2R01 NS034239, P01 NS43985, P30 MH062261 and R01 AG043540.

Conflict of interest

The authors declare no conflict of interests.

Compliance with ethical requirements

NOD scid IL2 receptor gamma chain knockout, NOD.Cg-Prkdc scid Il2rg tm1Wjl/SzJ, (NSG) mice (The Jackson Laboratories, Bar Harbor, Maine, USA; stock number 005557) were obtained from an established breeding colony and housed under pathogen-free conditions in accordance with ethical guidelines for care of laboratory animals at the National Institutes of Health and the University of Nebraska Medical Center.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gary Siuzdak.

Additional information

Julijana Ivanisevic and H. Paul Benton have contributed equally to this work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 1285 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ivanisevic, J., Benton, H.P., Rinehart, D. et al. An interactive cluster heat map to visualize and explore multidimensional metabolomic data. Metabolomics 11, 1029–1034 (2015). https://doi.org/10.1007/s11306-014-0759-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11306-014-0759-2

Keywords

Navigation