Biclustering Analysis Using Plaid Model on Gene Expression Data of Colon Cancer

Abstract

Unlike other typical clustering analysis, which considers column only, biclustering analysis processes a matrix into sub-matrices based on rows and columns simultaneously. One method of bicluster analysis uses the probabilistic model, like the plaid model, that provides overlapping bicluster. The plaid model calculates the value of an element given from a particular sub-matrix for each cell; thus, the value can be seen as the number of contributions of a particular bicluster. The algorithm begins with preparing the input data as a matrix, then an initial model is assessed and makes a residual matrix from the model. After that, we determine bicluster candidates, which are evaluated for its effect parameters and bicluster membership parameters. Finally, the bicluster candidate is pruned to give the optimal bicluster. We implemented the algorithm on gene expression dataset of colon cancer, where the rows and columns contain observations and types of genes, respectively. We carried out in six distinct scenarios in which each scenario uses different model parameters and threshold values. We measured the results using Jaccard index and coherence variance. Our experiments show that biclustering analysis on a model with mean, row, and column effects of colon cancer data output low coherence variance.

Pattern Recognition of Food Security in Indonesia Using Biclustering Plaid Model

“…The use of different threshold values is interesting as it helps to obtain optimal biclustering results. While previous studies suggested using thresholds 𝜏 1 and 𝜏 2 between 0.5 and 0.7 (Karim et al, 2019;Siswantining et al, 2021), this study utilized the specified parameter settings. Furthermore, the layer parameter represents the maximum number of layers that can be generated in the process of discovering biclusters.…”

“…The selection of model type determines the most fit model for data analysis, while the number of layers affects the complexity and granularity of the biclustering process. Additionally, the row and column release thresholds value for determining which variables and observations should be included in the biclusters in the biclusters (Siswantining et al, 2021).…”