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Lutter, D.* ; Ugocsai, P.* ; Grandl, M.* ; Orso, E.* ; Theis, F.J.* ; Lang, E.W.* ; Schmitz, G.*

Analyzing M-CSF dependent monocyte/macrophage differentiation: expression modes and meta-modes derived from an independent component analysis.

BMC Bioinformatics 9:100 (2008)
Verlagsversion DOI PMC
Open Access Gold
BACKGROUND: The analysis of high-throughput gene expression data sets derived from microarray experiments still is a field of extensive investigation. Although new approaches and algorithms are published continuously, mostly conventional methods like hierarchical clustering algorithms or variance analysis tools are used. Here we take a closer look at independent component analysis (ICA) which is already discussed widely as a new analysis approach. However, deep exploration of its applicability and relevance to concrete biological problems is still missing. In this study, we investigate the relevance of ICA in gaining new insights into well characterized regulatory mechanisms of M-CSF dependent macrophage differentiation. RESULTS: Statistically independent gene expression modes (GEM) were extracted from observed gene expression signatures (GES) through ICA of different microarray experiments. From each GEM we deduced a group of genes, henceforth called sub-mode. These sub-modes were further analyzed with different database query and literature mining tools and then combined to form so called meta-modes. With them we performed a knowledge-based pathway analysis and reconstructed a well known signal cascade. CONCLUSION: We show that ICA is an appropriate tool to uncover underlying biological mechanisms from microarray data. Most of the well known pathways of M-CSF dependent monocyte to macrophage differentiation can be identified by this unsupervised microarray data analysis. Moreover, recent research results like the involvement of proliferation associated cellular mechanisms during macrophage differentiation can be corroborated.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Sprache englisch
Veröffentlichungsjahr 2008
HGF-Berichtsjahr 0
ISSN (print) / ISBN 1471-2105
e-ISSN 1471-2105
Zeitschrift BMC Bioinformatics
Quellenangaben Band: 9, Heft: , Seiten: , Artikelnummer: 100 Supplement: ,
Verlag BioMed Central
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
PubMed ID 18279525
DOI 10.1186/1471-2105-9-100
Erfassungsdatum 2008-12-31
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