A new approach for filtering noise from high-density oligonucleotide microarray datasets.

ABSTRACT: Although DNA microarrays are powerful tools for profiling gene expression, the dynamic range and the sheer number of signals produced require efficient procedures for distinguishing false positive results (noise) from changes in expression that are 'real' (independently reproducible). We have developed an approach to filter noise from datasets generated when high density oligonucleotide-based microarrays are used to compare two distinct RNA populations. First, we performed comparisons between chips hybridized with cRNAs prepared from an identical starting RNA population; an 'Increase' or 'Decrease' call in such a comparison was defined as a false positive. Plotting the average distribution of these false positive signal intensities across 18 such comparisons of nine independent RNA prepara

SUBMITTER: Mills JC 

PROVIDER: S-EPMC55837 | biostudies-literature | 2001 Aug

REPOSITORIES: biostudies-literature