title: A weighted local least squares imputation method for missing value estimation in microarray gene expression data.
abstract: Many clustering techniques and classification methods for analysing microarray data require a complete dataset. However, very often gene expression datasets contain missing values due to various reasons. In this paper, we first propose to use vector angle as a measurement for the similarity between genes. We then propose the Weighted Local Least Square Imputation (WLLSI) method for missing values estimation. Numerical results on both synthetic data and real microarray data indicate that WLLSI method is more robust. The imputation methods are then applied to a breast cancer dataset and interesting results are obtained.
authors.affiliation: Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. wching@hkusua.hku.hk
authors.forename: Wai-Ki
authors.initials: WK
authors.lastname: Ching
authors.forename: Limin
authors.initials: L
authors.lastname: Li
authors.forename: Nam-Kiu
authors.initials: NK
authors.lastname: Tsing
authors.forename: Ching-Wan
authors.initials: CW
authors.lastname: Tai
authors.forename: Tuen-Wai
authors.initials: TW
authors.lastname: Ng
authors.forename: Alice S
authors.initials: AS
authors.lastname: Wong
authors.forename: Kwai-Wa
authors.initials: KW
authors.lastname: Cheng
mesh_terms: D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D016018:Least-Squares Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*