Abstract
Inferring gene regulatory networks from microarray data has become a popular activity in recent years, resulting in an ever-increasing volume of publications. There are many pitfalls in network analysis that remain either unnoticed or scantily understood. A critical discussion of such pitfalls is long overdue. Here we discuss one feature of microarray data the investigators need to be aware of when embarking on a study of putative associations between elements of networks and pathways.
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References
Chu T, Glymour C, Scheines R, Spirtes P (2003) A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays. Bioinformatics 19:1147–1152
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Acknowledgement
The study was supported by Grant MSM 0021620839 of the Ministry of Education, Czech Republic.
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Chen, L., Almudevar, A., Klebanov, L. (2013). Aggregation Effect in Microarray Data Analysis. In: Yakovlev, A., Klebanov, L., Gaile, D. (eds) Statistical Methods for Microarray Data Analysis. Methods in Molecular Biology, vol 972. Humana Press, New York, NY. https://doi.org/10.1007/978-1-60327-337-4_11
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DOI: https://doi.org/10.1007/978-1-60327-337-4_11
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Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-60327-336-7
Online ISBN: 978-1-60327-337-4
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