Latent Variable and nICA Modeling of Pathway Gene Module Composite

Author

Gong, Ting ; Zhu, Yitan ; Xuan, Jianhua ; Li, Huai ; Clarke, Robert ; Hoffman, Eric P. ; Wang, Yue

Author_Institution

Dept of ECE, Virginia Polytech. Inst. & State Univ., Arlington, VA

fYear

2006

Firstpage

5872

Lastpage

5875

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

Keywords

arrays; biology computing; genetics; independent component analysis; molecular biophysics; pattern clustering; biological processes; gene clustering approach; gene module composite; latent variable space; microarray data analysis; molecular expressions; nonnegative ICA; nonnegative independent component analysis; visual statistical data analyzer; Bioinformatics; Biological processes; Biological system modeling; Cities and towns; Clustering algorithms; Data analysis; Gene expression; Independent component analysis; Principal component analysis; USA Councils; gene clustering; latent variable model; microarray data analysis; module discovery; non-negative ICA;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE

ISSN

1557-170X

Print_ISBN

1-4244-0032-5

Electronic_ISBN

1557-170X

Type

conf

DOI

10.1109/IEMBS.2006.260697

Filename

4463143