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)
    
    
    
      
      
	
	    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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        Article: Journal article
    
 
    
        Document type
        Scientific Article
    
 
    
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        english
    
 
    
        Publication Year
        2008
    
 
    
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        0
    
 
    
    
        ISSN (print) / ISBN
        1471-2105
    
 
    
        e-ISSN
        1471-2105
    
 
    
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	    Volume: 9,  
	    Issue: ,  
	    Pages: ,  
	    Article Number: 100 
	    Supplement: ,  
	
    
 
    
        
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            BioMed Central
        
 
        
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        Reviewing status
        Peer reviewed
    
 
     
    
        POF-Topic(s)
        30205 - Bioengineering and Digital Health
    
 
    
        Research field(s)
        Enabling and Novel Technologies
    
 
    
        PSP Element(s)
        G-503800-001
    
 
    
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        Erfassungsdatum
        2008-12-31