Semi-supervised Significance Score of Differential Gene Expressions

In gene expression analyses for DNA microarray data, various statistical scores have been proposed for evaluating significance of genes exhibiting differential expression between two or more controlled conditions. To consider an unsupervised case or a semi-supervised case rather than a well-studied supervised case, we assume a latent variable model and apply the optimal discovery procedure (ODP) proposed by Storey (2005) to the model. Theoretical consideration leads to two different interpretations of the hidden variable, i.e., it only implicitly affects the alternative model through the model parameters, or is explicitly included in the alternative model, so that they correspond to two different implementations of ODP. By comparing the two implementations through experiments with simulation data, we found that sharing the latent variable estimation as in the latter case is effective in increasing the detectability of truly active genes. We also propose unsupervised and semi-supervised rating of genes and show its effectiveness as a significance score.

Authors and Affiliations

1. Graduate School of Information Science, Nara Institute of Science and Technology,  

   Shigeyuki Oba & Shin Ishii

Editors and Affiliations

1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

   Stefanos Kollias

2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

   Andreas Stafylopatis

3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

   Włodzisław Duch

4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

   Erkki Oja

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