High-throughput processing and normalization of one-color microarrays for transcriptional meta-analyses.

ABSTRACT:

Background

Microarray experiments are becoming increasingly common in biomedical research, as is their deposition in publicly accessible repositories, such as Gene Expression Omnibus (GEO). As such, there has been a surge in interest to use this microarray data for meta-analytic approaches, whether to increase sample size for a more powerful analysis of a specific disease (e.g. lung cancer) or to re-examine experiments for reasons different than those examined in the initial, publishing study that generated them. For the average biomedical researcher, there are a number of practical barriers to conducting such meta-analyses such as manually aggregating, filtering and formatting the data. Methods to automatically process large repositories of microarray data into a standardized, dir

SUBMITTER: Dozmorov MG 

PROVIDER: S-EPMC3236842 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature