Data Mining and Knowledge Discovery

, Volume 27, Issue 1, pp 146–165

Visualizing dimensionality reduction of systems biology data

  • Andreas Lehrmann
  • Michael Huber
  • Aydin C. Polatkan
  • Albert Pritzkau
  • Kay Nieselt
Article

DOI: 10.1007/s10618-012-0268-8

Cite this article as:
Lehrmann, A., Huber, M., Polatkan, A.C. et al. Data Min Knowl Disc (2013) 27: 146. doi:10.1007/s10618-012-0268-8

Abstract

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system but which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21.

Keywords

Dimension reduction Principal component analysis Independent component analysis Local linear embedding Systems biology 

Mathematics Subject Classification (2000)

62H25 15A18 

Copyright information

© The Author(s) 2012

Authors and Affiliations

  • Andreas Lehrmann
    • 1
  • Michael Huber
    • 2
  • Aydin C. Polatkan
    • 1
  • Albert Pritzkau
    • 3
  • Kay Nieselt
    • 1
  1. 1.Center for Bioinformatics TübingenUniversity of TübingenTübingenGermany
  2. 2.Wilhelm Schickard Institute for Computer ScienceUniversity of TübingenTübingenGermany
  3. 3.Bild- und SignalverarbeitungUniversity of LeipzigLeipzigGermany

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