Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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QCanvas: An Advanced Tool for Data Clustering and Visualization of Genomics Data

  • Kim, Nayoung (Department of Biological Sciences, Sookmyung Women's University) ;
  • Park, Herin (Department of Biological Sciences, Sookmyung Women's University) ;
  • He, Ningning (Department of Biological Sciences, Sookmyung Women's University) ;
  • Lee, Hyeon Young (Department of Biological Sciences, Sookmyung Women's University) ;
  • Yoon, Sukjoon (Department of Biological Sciences, Sookmyung Women's University)
  • Received : 2012.11.02
  • Accepted : 2012.11.16
  • Published : 2012.12.31
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Abstract

We developed a user-friendly, interactive program to simultaneously cluster and visualize omics data, such as DNA and protein array profiles. This program provides diverse algorithms for the hierarchical clustering of two-dimensional data. The clustering results can be interactively visualized and optimized on a heatmap. The present tool does not require any prior knowledge of scripting languages to carry out the data clustering and visualization. Furthermore, the heatmaps allow the selective display of data points satisfying user-defined criteria. For example, a clustered heatmap of experimental values can be differentially visualized based on statistical values, such as p-values. Including diverse menu-based display options, QCanvas provides a convenient graphical user interface for pattern analysis and visualization with high-quality graphics.

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References

  1. Kim N, He N, Kim C, Zhang F, Lu Y, Yu Q, et al. Systematic analysis of genotype-specific drug responses in cancer. Int J Cancer 2012;131:2456-2464. https://doi.org/10.1002/ijc.27529
  2. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439:353-357. https://doi.org/10.1038/nature04296
  3. Markert EK, Mizuno H, Vazquez A, Levine AJ. Molecular classification of prostate cancer using curated expression signatures. Proc Natl Acad Sci U S A 2011;108:21276-21281. https://doi.org/10.1073/pnas.1117029108
  4. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998;95:14863-14868. https://doi.org/10.1073/pnas.95.25.14863
  5. Saldanha AJ. Java Treeview: extensible visualization of microarray data. Bioinformatics 2004;20:3246-3248. https://doi.org/10.1093/bioinformatics/bth349

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