Biclustering models under collinearity in simulated biological experiments

Abstract

Biclustering models allow simultaneous detection of group observations that are related to variables in a data matrix. Such methods have been applied in biological data for classification. Collinearity is a common feature in biological data as there exist interactions between genes and proteins in their respective pathways. These relationships could seriously reduce the efficiency of biclustering models. In this study, synthetic data are generated to investigate the effect of collinearity on the performance of biclustering models. Specifically, the data are generated and induced with varying degrees of collinearity using Cholesky decomposition, and are implanted with biclusters to produce different sets of synthetic data. The effectiveness of three models namely Biclustering by Cheng tecting three types of biclusters in the generated data matrix were compared. The results show that all the models investigated are sensitive to changes in the level of collinearity. At low collinearity, all biclustering models detected the implanted biclusters in the data correctly. However, as the level of collinearity in the data increased, the proportion of detected biclusters captured by the models reduced. In particular at high collinearity, BCCC outperformed the other two models with Jaccard coefficients as high as 0.75 and 0.873 for one and two implanted biclusters respectively.