Title: Identifying differentially expressed genes in the absence of replication

Authors: Pushpike J. Thilakarathne; Geert Verbeke; Kristof Engelen; Kathleen Marchal; Dan Lin

Addresses: Interuniversity Institute for Biostatistics and statistical Bioinformatics, Katholieke Universiteit Leuven, Kapucijnenvoer 35 Blok D, bus 7001, B3000 Leuven, Belgium; Universiteit Hasselt, Belgium ' Interuniversity Institute for Biostatistics and statistical Bioinformatics, Katholieke Universiteit Leuven, Kapucijnenvoer 35 Blok D, bus 7001, B3000 Leuven, Belgium; Universiteit Hasselt, Belgium ' Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20 – bus 02460, B-3001 Heverlee, Belgium ' Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20 – bus 02460, B-3001 Heverlee, Belgium ' Interuniversity Institute for Biostatistics and statistical Bioinformatics, Katholieke Universiteit Leuven, Kapucijnenvoer 35 Blok D, bus 7001, B3000 Leuven, Belgium; Universiteit Hasselt, Belgium

Abstract: In microarray data analysis, the comparison of gene expression levels in different conditions and selection of biologically relevant genes are essential tasks. In this study, we propose a novel statistical procedure based on standardised conditional residuals from a linear mixed-effects model which allows comparison of conditions, even if only one replicate per experimental condition is available. We illustrate this method by using three publicly available datasets. We show that this method can be extended to handle more complex designs. Finally, simulations show that the tests developed have good statistical power to detect true differences among conditions at the gene level.

Keywords: microarray data analysis; standardised conditional residuals; linear mixed-effects model; cDNA; gene expression; bioinformatics; differentially expressed genes.

DOI: 10.1504/IJBRA.2013.050654

International Journal of Bioinformatics Research and Applications, 2013 Vol.9 No.1, pp.71 - 90

Available online: 21 Nov 2012

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