A weighted local least squares imputation method for missing value estimation in microarray gene expression data.
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.
Ching
Wai-Ki
WK
Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. wching@hkusua.hku.hk
Li
Limin
L
Tsing
Nam-Kiu
NK
Tai
Ching-Wan
CW
Ng
Tuen-Wai
TW
Wong
Alice S
AS
Cheng
Kwai-Wa
KW
D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D016018:Least-Squares Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*