title: Latent variable and nICA modeling of pathway gene module composite.
abstract: In this paper, we report a new gene clustering approach, non-negative independent component analysis (nICA), for microarray data analysis. Due to positive nature of molecular expressions, nICA fits better to the reality of corresponding putative biological processes. In conjunction with nICA model, visual statistical data analyzer (VISDA) is applied to group genes into modules in the latent variable space. The experimental results show that significant enrichment of gene annotations within clusters can be obtained.
authors.affiliation: Dept of ECE, Virginia Polytech. Inst. & State Univ., Arlington, VA, USA.
authors.forename: Ting
authors.initials: T
authors.lastname: Gong
authors.forename: Yitan
authors.initials: Y
authors.lastname: Zhu
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Huai
authors.initials: H
authors.lastname: Li
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
authors.forename: Eric P
authors.initials: EP
authors.lastname: Hoffman
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D015233:Models, Statistical; D005810:Multigene Family; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D012441:Saccharomyces cerevisiae / Q000378:metabolism