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. 2008 Feb 17:9:100.
doi: 10.1186/1471-2105-9-100.

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

Dominik Lutter  1 , Peter Ugocsai, Margot Grandl, Evelyn Orso, Fabian Theis, Elmar W Lang, Gerd Schmitz
Affiliations

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

Dominik Lutter et al. BMC Bioinformatics. .

Abstract

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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Figures

Figure 1
Figure 1
MAP kinase pathway analysis of the meta-modes. Yellow boxes correspond to genes mapped to the apoptosis meta-mode, red boxes to regulatory sequences and blue to signal transduction meta-mode, respectively. Solid arrows indicate direct and dashed arrows indirect activation. (Detailed legend information can be found on the KEGG website [63])
Figure 2
Figure 2
BiblioSphere pathway view shows the mapped Genes of sub-mode 12.2. Genes passed the MAPK filter are highlighted blue. Cited relationships between two genes make up the edges. Display of edges is restricted to those that constitute the shortest path from the central node. If a gene that codes for a transcription factor is connected to a gene that is known to contain a binding site for this transcription factor in its promoter, the connecting line is colored green over half of its length near the gene containing the binding site. Arrowheads at the ends of a connecting line symbolize that gene X regulates gene Y.
Figure 3
Figure 3
Overview over the main pathways involved in M-CSF dependent differentiation. The blue, purple, green and red colored boxes mediate proliferation, differentiation, actin re-organization and apoptosis. The yellow box mediates those common to proliferation and differentiation. Genes or gene products colored in intense yellow could be identified with our ICA analysis.
Figure 4
Figure 4
Histograms and expression profiles of an untransformed (A) and logarithmic corrected (B) microarray expression data set.
Figure 5
Figure 5
Maximum-likelihood Pearson fit of the EM-densities, for EM number 3 in (a) and number 12 in (b). The corresponding four moments are μ(y3) = 1.4, σ(y3) = 1.0, skewness(y3) = -0.95 and kurtosis(y3) = 4.0 for (a) and μ(y3) = -0.84, σ(y3) = 1.0, skewness(y3) = 0.49 and kurtosis(y3) = 4.4 for (b).
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References

    1. Makeigh S, Bell A, Jung TP, Sejnowski T. Advances in Neural Information Processing Systems 8 (NIPS'1995) Cambridge, Masachusetts: MIT Press; 1995. Independent component analysis of electroencephalographic data.
    1. Habl M, Bauer C, Ziegaus C, Lang EW. Perspectives in Neuroscience: Artificial Neural Networks in Medicine and Biology. Springer Publishers Berlin 2000 chap. Analyzing Brain Tumor Related EEG Signals With ICA Algorithms; pp. 131–136.
    1. Vigario R, Jousmäki V, Hämäläinen M, Hari R, Oja E. Advances in Neural Information Processing Systems 10 (NIPS'1997) Cambridge, Masachusetts: MIT Press; 1997. Independent component analysis for identification of artifacts in Magnetoencephalographic recordings; pp. 229–235.
    1. Yang K, Rajapakse JC. ICA gives higher-order functional connectivity of brain. Neural Information Processing – Letters and Reviews. 2004;2:27–32.
    1. Keck IR, Theis FJ, Gruber P, Lang EW, Specht K, Puntonet CG. 3D spatial analysis of fMRI Data on a word perception task. In: Puntonet CG, Prieto A, editor. Lecture Notes in Computer Science, LNCS 3195. Berlin: Springer Verlag; 2004. pp. 977–984.

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