Cargando…

Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression

BACKGROUND: High-quality expression data are required to investigate the biological effects of microRNAs (miRNAs). The goal of this study was, first, to assess the quality of miRNA expression data based on microarray technologies and, second, to consolidate it by applying a novel normalization metho...

Descripción completa

Detalles Bibliográficos
Autores principales: Sewer, Alain, Gubian, Sylvain, Kogel, Ulrike, Veljkovic, Emilija, Han, Wanjiang, Hengstermann, Arnd, Peitsch, Manuel C, Hoeng, Julia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077261/
https://www.ncbi.nlm.nih.gov/pubmed/24886675
http://dx.doi.org/10.1186/1756-0500-7-302
_version_ 1782323579016183808
author Sewer, Alain
Gubian, Sylvain
Kogel, Ulrike
Veljkovic, Emilija
Han, Wanjiang
Hengstermann, Arnd
Peitsch, Manuel C
Hoeng, Julia
author_facet Sewer, Alain
Gubian, Sylvain
Kogel, Ulrike
Veljkovic, Emilija
Han, Wanjiang
Hengstermann, Arnd
Peitsch, Manuel C
Hoeng, Julia
author_sort Sewer, Alain
collection PubMed
description BACKGROUND: High-quality expression data are required to investigate the biological effects of microRNAs (miRNAs). The goal of this study was, first, to assess the quality of miRNA expression data based on microarray technologies and, second, to consolidate it by applying a novel normalization method. Indeed, because of significant differences in platform designs, miRNA raw data cannot be normalized blindly with standard methods developed for gene expression. This fundamental observation motivated the development of a novel multi-array normalization method based on controllable assumptions, which uses the spike-in control probes to adjust the measured intensities across arrays. RESULTS: Raw expression data were obtained with the Exiqon dual-channel miRCURY LNA™ platform in the “common reference design” and processed as “pseudo-single-channel”. They were used to apply several quality metrics based on the coefficient of variation and to test the novel spike-in controls based normalization method. Most of the considerations presented here could be applied to raw data obtained with other platforms. To assess the normalization method, it was compared with 13 other available approaches from both data quality and biological outcome perspectives. The results showed that the novel multi-array normalization method reduced the data variability in the most consistent way. Further, the reliability of the obtained differential expression values was confirmed based on a quantitative reverse transcription–polymerase chain reaction experiment performed for a subset of miRNAs. The results reported here support the applicability of the novel normalization method, in particular to datasets that display global decreases in miRNA expression similarly to the cigarette smoke-exposed mouse lung dataset considered in this study. CONCLUSIONS: Quality metrics to assess between-array variability were used to confirm that the novel spike-in controls based normalization method provided high-quality miRNA expression data suitable for reliable downstream analysis. The multi-array miRNA raw data normalization method was implemented in an R software package called ExiMiR and deposited in the Bioconductor repository.
format Online
Article
Text
id pubmed-4077261
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-40772612014-07-02 Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression Sewer, Alain Gubian, Sylvain Kogel, Ulrike Veljkovic, Emilija Han, Wanjiang Hengstermann, Arnd Peitsch, Manuel C Hoeng, Julia BMC Res Notes Research Article BACKGROUND: High-quality expression data are required to investigate the biological effects of microRNAs (miRNAs). The goal of this study was, first, to assess the quality of miRNA expression data based on microarray technologies and, second, to consolidate it by applying a novel normalization method. Indeed, because of significant differences in platform designs, miRNA raw data cannot be normalized blindly with standard methods developed for gene expression. This fundamental observation motivated the development of a novel multi-array normalization method based on controllable assumptions, which uses the spike-in control probes to adjust the measured intensities across arrays. RESULTS: Raw expression data were obtained with the Exiqon dual-channel miRCURY LNA™ platform in the “common reference design” and processed as “pseudo-single-channel”. They were used to apply several quality metrics based on the coefficient of variation and to test the novel spike-in controls based normalization method. Most of the considerations presented here could be applied to raw data obtained with other platforms. To assess the normalization method, it was compared with 13 other available approaches from both data quality and biological outcome perspectives. The results showed that the novel multi-array normalization method reduced the data variability in the most consistent way. Further, the reliability of the obtained differential expression values was confirmed based on a quantitative reverse transcription–polymerase chain reaction experiment performed for a subset of miRNAs. The results reported here support the applicability of the novel normalization method, in particular to datasets that display global decreases in miRNA expression similarly to the cigarette smoke-exposed mouse lung dataset considered in this study. CONCLUSIONS: Quality metrics to assess between-array variability were used to confirm that the novel spike-in controls based normalization method provided high-quality miRNA expression data suitable for reliable downstream analysis. The multi-array miRNA raw data normalization method was implemented in an R software package called ExiMiR and deposited in the Bioconductor repository. BioMed Central 2014-05-17 /pmc/articles/PMC4077261/ /pubmed/24886675 http://dx.doi.org/10.1186/1756-0500-7-302 Text en Copyright © 2014 Sewer et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 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
Sewer, Alain
Gubian, Sylvain
Kogel, Ulrike
Veljkovic, Emilija
Han, Wanjiang
Hengstermann, Arnd
Peitsch, Manuel C
Hoeng, Julia
Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression
title Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression
title_full Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression
title_fullStr Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression
title_full_unstemmed Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression
title_short Assessment of a novel multi-array normalization method based on spike-in control probes suitable for microRNA datasets with global decreases in expression
title_sort assessment of a novel multi-array normalization method based on spike-in control probes suitable for microrna datasets with global decreases in expression
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077261/
https://www.ncbi.nlm.nih.gov/pubmed/24886675
http://dx.doi.org/10.1186/1756-0500-7-302
work_keys_str_mv AT seweralain assessmentofanovelmultiarraynormalizationmethodbasedonspikeincontrolprobessuitableformicrornadatasetswithglobaldecreasesinexpression
AT gubiansylvain assessmentofanovelmultiarraynormalizationmethodbasedonspikeincontrolprobessuitableformicrornadatasetswithglobaldecreasesinexpression
AT kogelulrike assessmentofanovelmultiarraynormalizationmethodbasedonspikeincontrolprobessuitableformicrornadatasetswithglobaldecreasesinexpression
AT veljkovicemilija assessmentofanovelmultiarraynormalizationmethodbasedonspikeincontrolprobessuitableformicrornadatasetswithglobaldecreasesinexpression
AT hanwanjiang assessmentofanovelmultiarraynormalizationmethodbasedonspikeincontrolprobessuitableformicrornadatasetswithglobaldecreasesinexpression
AT hengstermannarnd assessmentofanovelmultiarraynormalizationmethodbasedonspikeincontrolprobessuitableformicrornadatasetswithglobaldecreasesinexpression
AT peitschmanuelc assessmentofanovelmultiarraynormalizationmethodbasedonspikeincontrolprobessuitableformicrornadatasetswithglobaldecreasesinexpression
AT hoengjulia assessmentofanovelmultiarraynormalizationmethodbasedonspikeincontrolprobessuitableformicrornadatasetswithglobaldecreasesinexpression