Abstract

BackgroundA typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates.ResultsWe performed a microarray experiment using a total of 24 Affymetrix GeneChip® arrays. The study included 4th mammary gland samples from eight 21-day-old Sprague Dawley CD female rats exposed to genistein (soy isoflavone). RNA samples from each rat were split to assess variation arising at labeling and hybridization steps. A general linear model was used to estimate variance components. Pearson correlations were computed to evaluate agreement between technical and biological replicates.ConclusionThe greatest source of variation was biological variation, followed by residual error, and finally variation due to labeling when *.cel files were processed with dChip and RMA image processing algorithms. When MAS 5.0 or GCRMA-EB were used, the greatest source of variation was residual error, followed by biology and labeling. Correlations between technical replicates were consistently higher than between biological replicates.

Highlights

  • A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes

  • Source *.cel data files from 24 GeneChip® arrays were subjected to image processing by four popular methods for probe-level data implemented in BioConductor [20]: DNA Chip Analyzer [21], MAS 5.0 [22], RMA [23], and GCRMA-EB [24]

  • Using Affymetrix GeneArray® chips, we examined the relative magnitudes of different sources of variation in microarray experiment

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Summary

Introduction

A typical microarray experiment has many sources of variation which can be attributed to biological and technical causes. Identifying sources of variation and assessing their magnitude, among other factors, are important for optimal experimental design. The objectives of this study were: (1) to estimate relative magnitudes of different sources of variation and (2) to evaluate agreement between biological and technical replicates. Microarray chips are a powerful technology capable of measuring expression levels of thousands of genes simultaneously. The number of experiments involving microarrays grows nearly exponentially each year [2]. Several platforms are currently available, including the commonly used short oligonucleotide-based Affymetrix GeneChip® arrays, which utilize multiple probes for each gene and (page number not for citation purposes). The scheme of hierarchical unbalanced design used in our experiment is shown. A total of 8 rats and 24 chips were used

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