The theoretic framework of local weighted approximation for microarray missing value estimation

摘要

Microarray data are used in many biomedical experiments. They often contain missing values which significantly affect statistical algorithms. Although a number of imputation algorithms have been proposed, they have various limitations to exploit local and global information effectively for estimation. It is necessary to develop more effective techniques to solve the data imputation problem. In this paper, we propose a theoretic framework of local weighted approximation for missing value estimation, based on the Taylor series approximation. Besides revealing that k-nearest neighbor imputation (KNNimpute) is a special case of the framework, we focus on the study of its linear case—local weighted linear approximation imputation (LWLAimpute) from theory to experiment. Experimental results show that LWLAimpute and its iterative version can achieve better performance than some existing imputation methods, the superiority becomes more significant with increasing level of missing values.