This article is part of the supplement: Selected articles from ISCB-Asia 2012
Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset
1 Department of Medical and Life Science, Faculty of Pharmaceutical Science, Tokyo University of Science, 2641 Yamazaki, Noda, 278-8510, Japan
2 Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan
Citation and License
BMC Genomics 2013, 14(Suppl 2):S9 doi:10.1186/1471-2164-14-S2-S9Published: 15 February 2013
Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset.
As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes.
The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions.