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Evaluation of gene expression measurements from commercial microarray platforms

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TLDR
Correlations in gene expression levels and comparisons for significant gene expression changes in this subset were calculated, and showed considerable divergence across the different platforms, suggesting the need for establishing industrial manufacturing standards, and further independent and thorough validation of the technology.
Abstract
Multiple commercial microarrays for measuring genome-wide gene expression levels are currently available, including oligonucleotide and cDNA, single- and two-channel formats. This study reports on the results of gene expression measurements generated from identical RNA preparations that were obtained using three commercially available microarray platforms. RNA was collected from PANC-1 cells grown in serum-rich medium and at 24 h following the removal of serum. Three biological replicates were prepared for each condition, and three experimental replicates were produced for the first biological replicate. RNA was labeled and hybridized to microarrays from three major suppliers according to manufacturers’ protocols, and gene expression measurements were obtained using each platform’s standard software. For each platform, gene targets from a subset of 2009 common genes were compared. Correlations in gene expression levels and comparisons for significant gene expression changes in this subset were calculated, and showed considerable divergence across the different platforms, suggesting the need for establishing industrial manufacturing standards, and further independent and thorough validation of the technology.

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The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

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- 01 Sep 2006 - 
TL;DR: This study describes the experimental design and probe mapping efforts behind the MicroArray Quality Control project and shows intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed.
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DNA biosensors and microarrays.

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Multiple-laboratory comparison of microarray platforms

TL;DR: A consortium of ten laboratories from the Washington, DC–Baltimore, USA, area was formed to compare data obtained from three widely used platforms using identical RNA samples to demonstrate that there are relatively large differences in data obtained in labs using the same platform, but that the results from the best-performing labs agree rather well.
References
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Journal ArticleDOI

Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

TL;DR: The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data and provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-power gene expression and genomic hybridization experiments.
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Summaries of Affymetrix GeneChip probe level data

TL;DR: It is found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models.
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Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

TL;DR: This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments.
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Model-based analysis of oligonucleotide arrays: Expression index computation and outlier detection

TL;DR: A statistical model is proposed for the probe-level data, and model-based estimates for gene expression indexes are developed, which help to identify and handle cross-hybridizing probes and contaminating array regions.
Journal ArticleDOI

Identifying biological themes within lists of genes with EASE

TL;DR: EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data and is robust to varying methods of normalization, intensity calculation and statistical selection of genes.
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The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements

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