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.

DOI: 10.1504/IJDMB.2010.033524

Cite this paper

@article{Ching2010AWL, title={A weighted Local Least Squares Imputation method for missing value estimation in microarray gene expression data}, author={Wai-Ki Ching and Limin Li and Nam-Kiu Tsing and Ching-Wan Tai and Tuen-Wai Ng and Alice S. Wong and Kwai-Wa Cheng}, journal={International journal of data mining and bioinformatics}, year={2010}, volume={4 3}, pages={331-47} }