Tree-dependent components of gene expression data for clustering
SCIE
SCOPUS
- Title
- Tree-dependent components of gene expression data for clustering
- Authors
- Kim, JK; Choi, SJ
- Date Issued
- 2006-01
- Publisher
- SPRINGER-VERLAG BERLIN
- Abstract
- Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metabolic shift data, and yeast sporulation data, shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.
- Keywords
- YEAST
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/23754
- DOI
- 10.1007/11840930_87
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 4132, page. 837 - 846, 2006-01
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.