Global rank-invariant set normalization (GRSN) to reduce systematic distortions in microarray data.

ABSTRACT:

Background

Microarray technology has become very popular for globally evaluating gene expression in biological samples. However, non-linear variation associated with the technology can make data interpretation unreliable. Therefore, methods to correct this kind of technical variation are critical. Here we consider a method to reduce this type of variation applied after three common procedures for processing microarray data: MAS 5.0, RMA, and dChip.

Results

We commonly observe intensity-dependent technical variation between samples in a single microarray experiment. This is most common when MAS 5.0 is used to process probe level data, but we also see this type of technical variation with RMA and dChip processed data. Datasets with unbalanced numbers of up and down regulated g

SUBMITTER: Pelz CR 

PROVIDER: S-EPMC2644708 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature