Assessing the variability in GeneChip data.

Autor: Huang S; Genomic Informatics, Eli Lilly & Company, Indianapolis, Indiana 46285, USA. huang_shuguang@lilly.com, Qian HR, Geringer C, Love C, Gelbert L, Bemis K
Jazyk: angličtina
Zdroj: American journal of pharmacogenomics : genomics-related research in drug development and clinical practice [Am J Pharmacogenomics] 2003; Vol. 3 (4), pp. 279-90.
DOI: 10.2165/00129785-200303040-00005
Abstrakt: Introduction: Oligonucleotide and cDNA microarray experiments are now common practice in biological science research. The goal of these experiments is generally to gain clues about the functions of genes by measuring how their expression levels rise and fall in response to changing experimental conditions. Measures of gene expression are affected, however, by a variety of factors. This paper introduces statistical methods to assess the variability of Affymetrix GeneChip data due to randomness.
Methods: The variation of Affymetrix's GeneChip signal data are quantified at both chip level and individual gene level, respectively, by the agreement study method and variance components method. Three agreement measurement methods are introduced to assess the variability among chips. Variation sources for gene expression data are decomposed into four categories: systematic experiment variation, treatment effect, biological variation, and chip variation. The focus of this paper is on evaluating and comparing the last two kinds of variations.
Results: Measurement of agreement and variance components methods were applied to an experimental data, and the calculation and interpretation were exemplified. The variability between biological samples were shown to exist and were assessed at both the chip level and individual gene level. Using the variance components method, it was found that the biological and chip variation are roughly comparable. The Statistical Analysis System (SAS) program for doing the agreement studies can be obtained from the correspondence author.
Databáze: MEDLINE
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