A weighted Local Least Squares Imputation method for missing value estimation in microarray gene expression data

Authors:
Wai-Ki Ching;Limin Li;Nam-Kiu Tsing;Ching-Wan Tai;Tuen-Wai Ng;Alice S. Wong;Kwai-Wa Cheng
Affiliations:
Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong.;Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong.;Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong.;Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong.;Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong.;School of Biological Sciences, The University of Hong Kong, Pokfulam Road, Hong Kong.;MD Anderson Cancer Center, Department of Molecular Therapeutics, University of Texas, 1515 Holcombe Boulevard, Houston, TX, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2010

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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.