Evaluation of Plaid Models in Biclustering of Gene Expression Data

Background. Biclustering algorithms for the analysis of high-dimensional gene expression data were proposed. Among them, the plaid model is arguably one of the most flexible biclustering models up to now. Objective. The main goal of this study is to provide an evaluation of plaid models. To that end, we will investigate this model on both simulation data and real gene expression datasets. Methods. Two simulated matrices with different degrees of overlap and noise are generated and then the intrinsic structure of these data is compared with biclusters result. Also, we have searched biologically significant discovered biclusters by GO analysis. Results. When there is no noise the algorithm almost discovered all of the biclusters but when there is moderate noise in the dataset, this algorithm cannot perform very well in finding overlapping biclusters and if noise is big, the result of biclustering is not reliable. Conclusion. The plaid model needs to be modified because when there is a moderate or big noise in the data, it cannot find good biclusters. This is a statistical model and is a quite flexible one. In summary, in order to reduce the errors, model can be manipulated and distribution of error can be changed.