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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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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. |
format | Online Article Text |
id | pubmed-4143561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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