Biologically valid linear factor models of gene expression
Bioinformatics
Topographic Independent Component Analysis
Neural Computation
Tree-Dependent components of gene expression data for clustering
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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Topographic independent component analysis (TICA) is an interesting extension of the conventional ICA, which aims at finding a linear decomposition into approximately independent components with the dependence between two components is approximated by their proximity in the topographic representation. In this paper we apply the topographic ICA to gene expression time series data and compare it with the conventional ICA as well as the independent subspace analysis (ISA). Empirical study with yeast cell cycle-related data and yeast sporulation data, shows that TICA is more suitable for gene clustering.