Microarray data mining using Bioconductor packagesReport as inadecuate

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BMC Proceedings

, 3:S9

First Online: 16 July 2009DOI: 10.1186-1753-6561-3-S4-S9

Cite this article as: Nie, H., Neerincx, P.B., Poel, J

et al. BMC Proc 2009 3Suppl 4: S9. doi:10.1186-1753-6561-3-S4-S9


BackgroundThis paper describes the results of a Gene Ontology GO term enrichment analysis of chicken microarray data using the Bioconductor packages. By checking the enriched GO terms in three contrasts, MM8-PM8, MM8-MA8, and MM8-MM24, of the provided microarray data during this workshop, this analysis aimed to investigate the host reactions in chickens occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria. The results of GO enrichment analysis using GO terms annotated to chicken genes and GO terms annotated to chicken-human orthologous genes were also compared. Furthermore, a locally adaptive statistical procedure LAP was performed to test differentially expressed chromosomal regions, rather than individual genes, in the chicken genome after Eimeria challenge.

ResultsGO enrichment analysis identified significant raw p-value < 0.05 GO terms for all three contrasts included in the analysis. Some of the GO terms linked to, generally, primary immune responses or secondary immune responses indicating the GO enrichment analysis is a useful approach to analyze microarray data. The comparisons of GO enrichment results using chicken gene information and chicken-human orthologous gene information showed more refined GO terms related to immune responses when using chicken-human orthologous gene information, this suggests that using chicken-human orthologous gene information has higher power to detect significant GO terms with more refined functionality. Furthermore, three chromosome regions were identified to be significantly up-regulated in contrast MM8-PM8 q-value < 0.01.

ConclusionOverall, this paper describes a practical approach to analyze microarray data in farm animals where the genome information is still incomplete. For farm animals, such as chicken, with currently limited gene annotation, borrowing gene annotation information from orthologous genes in well-annotated species, such as human, will help improve the pathway analysis results substantially. Furthermore, LAP analysis approach is a relatively new and very useful way to be applied in microarray analysis.

List of abbreviations usedDEDifferentially Expressed

FDRFalse Discovery Rate

GOGene Ontology

GO BPGene Ontology Biological Process

PMPBS-E. Maxima

MME. maxima-E. Maxima

MAE. maxima-E. acervulina

LAPlocally adaptive statistical procedure

Electronic supplementary materialThe online version of this article doi:10.1186-1753-6561-3-S4-S9 contains supplementary material, which is available to authorized users.

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