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

Lutter D, Ugocsai P, Grandl M, Orso E, Theis F, Lang EW, Schmitz G - BMC Bioinformatics (2008)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Clinical Chemistry and Laboratory Medicine, University of Regensburg, D-93042 Regensburg, Germany. dominik.lutter@klinik.uni-regensburg.de

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.

Show MeSH
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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2277398&req=5

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.

Mentions: Moreover, we were able to identify different regulatory mechanisms during M-CSF dependent differentiation. Although signal transduction pathways are mainly regulated by protein modifications like phosphorylation or acetylation, genes associated to specific pathways involved in macrophage differentiation could be separated into sub-modes only by analyzing gene expression signatures and their related gene expression modes. Furthermore, this analysis could be improved by combining gene expression sub-modes extracted from different microarray experiments into informative gene expression meta-modes. The results are in full agreement with the experimental literature on M-CSF dependent differentiation [55] and illustrate the potential power of such information-theory-based, unsupervised and data-driven analysis methods, see Figure 3.


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

Lutter D, Ugocsai P, Grandl M, Orso E, Theis F, Lang EW, Schmitz G - BMC Bioinformatics (2008)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2277398&req=5

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.
Mentions: Moreover, we were able to identify different regulatory mechanisms during M-CSF dependent differentiation. Although signal transduction pathways are mainly regulated by protein modifications like phosphorylation or acetylation, genes associated to specific pathways involved in macrophage differentiation could be separated into sub-modes only by analyzing gene expression signatures and their related gene expression modes. Furthermore, this analysis could be improved by combining gene expression sub-modes extracted from different microarray experiments into informative gene expression meta-modes. The results are in full agreement with the experimental literature on M-CSF dependent differentiation [55] and illustrate the potential power of such information-theory-based, unsupervised and data-driven analysis methods, see Figure 3.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute for Clinical Chemistry and Laboratory Medicine, University of Regensburg, D-93042 Regensburg, Germany. dominik.lutter@klinik.uni-regensburg.de

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.

Show MeSH