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Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization

BACKGROUND: Oligonucleotide arrays have become one of the most widely used high-throughput tools in biology. Due to their sensitivity to experimental conditions, normalization is a crucial step when comparing measurements from these arrays. Normalization is, however, far from a solved problem. Frequ...

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Autores principales: Hulsman, Marc, Mentink, Anouk, van Someren, Eugene P, Dechering, Koen J, de Boer, Jan, Reinders, Marcel JT
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2857856/
https://www.ncbi.nlm.nih.gov/pubmed/20346103
http://dx.doi.org/10.1186/1471-2105-11-156
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author Hulsman, Marc
Mentink, Anouk
van Someren, Eugene P
Dechering, Koen J
de Boer, Jan
Reinders, Marcel JT
author_facet Hulsman, Marc
Mentink, Anouk
van Someren, Eugene P
Dechering, Koen J
de Boer, Jan
Reinders, Marcel JT
author_sort Hulsman, Marc
collection PubMed
description BACKGROUND: Oligonucleotide arrays have become one of the most widely used high-throughput tools in biology. Due to their sensitivity to experimental conditions, normalization is a crucial step when comparing measurements from these arrays. Normalization is, however, far from a solved problem. Frequently, we encounter datasets with significant technical effects that currently available methods are not able to correct. RESULTS: We show that by a careful decomposition of probe specific amplification, hybridization and array location effects, a normalization can be performed that allows for a much improved analysis of these data. Identification of the technical sources of variation between arrays has allowed us to build statistical models that are used to estimate how the signal of individual probes is affected, based on their properties. This enables a model-based normalization that is probe-specific, in contrast with the signal intensity distribution normalization performed by many current methods. Next to this, we propose a novel way of handling background correction, enabling the use of background information to weight probes during summarization. Testing of the proposed method shows a much improved detection of differentially expressed genes over earlier proposed methods, even when tested on (experimentally tightly controlled and replicated) spike-in datasets. CONCLUSIONS: When a limited number of arrays are available, or when arrays are run in different batches, technical effects have a large influence on the measured expression of genes. We show that a detailed modelling and correction of these technical effects allows for an improved analysis in these situations.
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spelling pubmed-28578562010-04-22 Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization Hulsman, Marc Mentink, Anouk van Someren, Eugene P Dechering, Koen J de Boer, Jan Reinders, Marcel JT BMC Bioinformatics Methodology article BACKGROUND: Oligonucleotide arrays have become one of the most widely used high-throughput tools in biology. Due to their sensitivity to experimental conditions, normalization is a crucial step when comparing measurements from these arrays. Normalization is, however, far from a solved problem. Frequently, we encounter datasets with significant technical effects that currently available methods are not able to correct. RESULTS: We show that by a careful decomposition of probe specific amplification, hybridization and array location effects, a normalization can be performed that allows for a much improved analysis of these data. Identification of the technical sources of variation between arrays has allowed us to build statistical models that are used to estimate how the signal of individual probes is affected, based on their properties. This enables a model-based normalization that is probe-specific, in contrast with the signal intensity distribution normalization performed by many current methods. Next to this, we propose a novel way of handling background correction, enabling the use of background information to weight probes during summarization. Testing of the proposed method shows a much improved detection of differentially expressed genes over earlier proposed methods, even when tested on (experimentally tightly controlled and replicated) spike-in datasets. CONCLUSIONS: When a limited number of arrays are available, or when arrays are run in different batches, technical effects have a large influence on the measured expression of genes. We show that a detailed modelling and correction of these technical effects allows for an improved analysis in these situations. BioMed Central 2010-03-26 /pmc/articles/PMC2857856/ /pubmed/20346103 http://dx.doi.org/10.1186/1471-2105-11-156 Text en Copyright ©2010 Hulsman et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology article
Hulsman, Marc
Mentink, Anouk
van Someren, Eugene P
Dechering, Koen J
de Boer, Jan
Reinders, Marcel JT
Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization
title Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization
title_full Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization
title_fullStr Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization
title_full_unstemmed Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization
title_short Delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization
title_sort delineation of amplification, hybridization and location effects in microarray data yields better-quality normalization
topic Methodology article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2857856/
https://www.ncbi.nlm.nih.gov/pubmed/20346103
http://dx.doi.org/10.1186/1471-2105-11-156
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