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Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

BACKGROUND: Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biologi...

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Autores principales: Richard, Arianne C, Lyons, Paul A, Peters, James E, Biasci, Daniele, Flint, Shaun M, Lee, James C, McKinney, Eoin F, Siegel, Richard M, Smith, Kenneth GC
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143561/
https://www.ncbi.nlm.nih.gov/pubmed/25091430
http://dx.doi.org/10.1186/1471-2164-15-649
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author Richard, Arianne C
Lyons, Paul A
Peters, James E
Biasci, Daniele
Flint, Shaun M
Lee, James C
McKinney, Eoin F
Siegel, Richard M
Smith, Kenneth GC
author_facet Richard, Arianne C
Lyons, Paul A
Peters, James E
Biasci, Daniele
Flint, Shaun M
Lee, James C
McKinney, Eoin F
Siegel, Richard M
Smith, Kenneth GC
author_sort Richard, Arianne C
collection PubMed
description BACKGROUND: Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. RESULTS: Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this “gold-standard” comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. CONCLUSIONS: Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-649) contains supplementary material, which is available to authorized users.
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spelling pubmed-41435612014-09-02 Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation Richard, Arianne C Lyons, Paul A Peters, James E Biasci, Daniele Flint, Shaun M Lee, James C McKinney, Eoin F Siegel, Richard M Smith, Kenneth GC BMC Genomics Research Article BACKGROUND: Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. RESULTS: Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this “gold-standard” comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. CONCLUSIONS: Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-649) contains supplementary material, which is available to authorized users. BioMed Central 2014-08-04 /pmc/articles/PMC4143561/ /pubmed/25091430 http://dx.doi.org/10.1186/1471-2164-15-649 Text en © Richard et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Richard, Arianne C
Lyons, Paul A
Peters, James E
Biasci, Daniele
Flint, Shaun M
Lee, James C
McKinney, Eoin F
Siegel, Richard M
Smith, Kenneth GC
Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation
title Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation
title_full Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation
title_fullStr Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation
title_full_unstemmed Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation
title_short Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation
title_sort comparison of gene expression microarray data with count-based rna measurements informs microarray interpretation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4143561/
https://www.ncbi.nlm.nih.gov/pubmed/25091430
http://dx.doi.org/10.1186/1471-2164-15-649
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