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Extending the pathway analysis framework with a test for transcriptional variance implicates novel pathway modulation during myogenic differentiation
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| Zeitschriftentitel: | Bioinformatics |
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| Personen und Körperschaften: | , , |
| In: | Bioinformatics, 23, 2007, 11, S. 1356-1362 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
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| Schlagwörter: |
| Zusammenfassung: | <jats:title>Abstract</jats:title><jats:p>Motivation: We describe an extension of the pathway-based enrichment approach for analyzing microarray data via a robust test for transcriptional variance. The use of a variance test is intended to identify additional patterns of transcriptional regulation in which many genes in a pathway are up- and down-regulated. Such patterns may be indicative of the reciprocal regulation of pathway activators and inhibitors or of the differential regulation of separate biological sub-processes and should extend the number of detectable patterns of transcriptional modulation.</jats:p><jats:p>Results: We validated this new statistical approach on a microarray experiment that captures the temporal transcriptional profile of muscle differentiation in mouse C2C12 cells. Comparisons of the transcriptional state of myoblasts and differentiated myotubes via a robust variance test implicated several novel pathways in muscle cell differentiation previously overlooked by a standard enrichment analysis. Specifically, pathways involved in cell structure, calcium-mediated signaling and muscle-specific signaling were identified as differentially modulated based on their increased transcriptional variance. These biologically relevant results validate this approach and demonstrate the flexible nature of pathway-based methods of data analysis.</jats:p><jats:p>Availability: The software is available as Supplementary Material.</jats:p><jats:p>Contact: joseph.szustakowski@novartis.com</jats:p><jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
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| Umfang: | 1356-1362 |
| ISSN: |
1367-4803
1367-4811 |
| DOI: | 10.1093/bioinformatics/btm116 |