Year: 2005
Type: article
Abstract: BACKGROUND: Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples. RESULTS: We propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data. CONCLUSIONS: The new normalization method is effective for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed.
Source: BMC Bioinformatics
Authors: Yingdong Zhao, Ming-Chung Li, Richard Simon
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,990
OpenAlex ID: W1577782730