Comparative analysis of missing value imputation methods to improve clustering and interpretation of microarray experiments.

ABSTRACT:

Background

Microarray technologies produced large amount of data. In a previous study, we have shown the interest of k-Nearest Neighbour approach for restoring the missing gene expression values, and its positive impact of the gene clustering by hierarchical algorithm. Since, numerous replacement methods have been proposed to impute missing values (MVs) for microarray data. In this study, we have evaluated twelve different usable methods, and their influence on the quality of gene clustering. Interestingly we have used several datasets, both kinetic and non kinetic experiments from yeast and human.

Results

We underline the excellent efficiency of approaches proposed and implemented by Bo and co-workers and especially one based on expected maximization (EM_array). These impro

SUBMITTER: Celton M 

PROVIDER: S-EPMC2827407 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature