Biological impact of missing-value imputation on downstream analyses of gene expression profiles.

ABSTRACT:

Motivation

Microarray experiments frequently produce multiple missing values (MVs) due to flaws such as dust, scratches, insufficient resolution or hybridization errors on the chips. Unfortunately, many downstream algorithms require a complete data matrix. The motivation of this work is to determine the impact of MV imputation on downstream analysis, and whether ranking of imputation methods by imputation accuracy correlates well with the biological impact of the imputation.

Methods

Using eight datasets for differential expression (DE) and classification analysis and eight datasets for gene clustering, we demonstrate the biological impact of missing-value imputation on statistical downstream analyses, including three commonly employed DE methods, four classifiers and three g

SUBMITTER: Oh S 

PROVIDER: S-EPMC3008641 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature