Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica

  • Authors:

  • D. Lutter;Th. Langmann;P. Ugocsai;C. Moehle;E. Seibold;W. D. Splettstoesser;P. Gruber;E. W. Lang;G. Schmitz

  • Affiliations:

  • Clinical Chemistry, University Clinic, 93053 Regensburg, Germany and CIML Group, Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany and Institute of Bioinformatics and Sy ...;Clinical Chemistry, University Clinic, 93053 Regensburg, Germany and Institute of Human Genetics, University Clinic, 93053 Regensburg, Germany;Clinical Chemistry, University Clinic, 93053 Regensburg, Germany;Clinical Chemistry, University Clinic, 93053 Regensburg, Germany;Bundeswehr Institute of Microbiology, Neuherbergstr. 11, 80937 Munich, Germany;Bundeswehr Institute of Microbiology, Neuherbergstr. 11, 80937 Munich, Germany;CIML Group, Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany;CIML Group, Institute of Biophysics, University of Regensburg, 93040 Regensburg, Germany;Clinical Chemistry, University Clinic, 93053 Regensburg, Germany

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2009

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Abstract

The analysis of large-scale gene expression profiles is still a demanding and extensive task. Modern machine learning and data mining techniques developed in linear algebra, like Independent Component Analysis (ICA), become increasingly popular as appropriate tools for analyzing microarray data. We applied ICA to analyze kinetic gene expression profiles of human monocyte derived macrophages (MDM) from three different donors infected with Francisella tularensis holartica and compared them to more classical methods like hierarchical clustering. Results were compared using a pathway analysis tool, based on the Gene Ontology and the MeSH database. We could show that both methods lead to time-dependent gene regulatory patterns which fit well to known TNF@a induced immune responses. In comparison, the nonexclusive attribute of ICA results in a more detailed view and a higher resolution in time dependent behavior of the immune response genes. Additionally, we identified NF@kB as one of the main regulatory genes during response to F. tularensis infection.