Hedegaard J, Arce C, Bicciato S, Bonnet A, Buitenhuis B, Collado-Romero M, Conley LN, SanCristobal M, Ferrari F, Garrido JJ, Groenen MA, Hornshøj H, Hulsegge I, Jiang L, Jiménez-Marín Á, Kommadath A, Lagarrigue S, Leunissen JA, Liaubet L, Neerincx PB, Nie H, Poel JVD, Prickett D, Ramirez-Boo M, Rebel JM, Robert-Granié C, Skarman A, Smits MA, Sørensen P, Tosser-Klopp G, Watson M. Methods for interpreting lists of affected genes obtained in a DNA microarray experiment.
BMC Proc 2009;
3 Suppl 4:S5. [PMID:
19615118 PMCID:
PMC2712748 DOI:
10.1186/1753-6561-3-s4-s5]
[Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND
The aim of this paper was to describe and compare the methods used and the results obtained by the participants in a joint EADGENE (European Animal Disease Genomic Network of Excellence) and SABRE (Cutting Edge Genomics for Sustainable Animal Breeding) workshop focusing on post analysis of microarray data. The participating groups were provided with identical lists of microarray probes, including test statistics for three different contrasts, and the normalised log-ratios for each array, to be used as the starting point for interpreting the affected probes. The data originated from a microarray experiment conducted to study the host reactions in broilers occurring shortly after a secondary challenge with either a homologous or heterologous species of Eimeria.
RESULTS
Several conceptually different analytical approaches, using both commercial and public available software, were applied by the participating groups. The following tools were used: Ingenuity Pathway Analysis, MAPPFinder, LIMMA, GOstats, GOEAST, GOTM, Globaltest, TopGO, ArrayUnlock, Pathway Studio, GIST and AnnotationDbi. The main focus of the approaches was to utilise the relation between probes/genes and their gene ontology and pathways to interpret the affected probes/genes. The lack of a well-annotated chicken genome did though limit the possibilities to fully explore the tools. The main results from these analyses showed that the biological interpretation is highly dependent on the statistical method used but that some common biological conclusions could be reached.
CONCLUSION
It is highly recommended to test different analytical methods on the same data set and compare the results to obtain a reliable biological interpretation of the affected genes in a DNA microarray experiment.
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