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A new normalization for Nanostring nCounter gene expression data

The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most use...

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Autores principales: Molania, Ramyar, Gagnon-Bartsch, Johann A, Dobrovic, Alexander, Speed, Terence P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614807/
https://www.ncbi.nlm.nih.gov/pubmed/31114909
http://dx.doi.org/10.1093/nar/gkz433
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author Molania, Ramyar
Gagnon-Bartsch, Johann A
Dobrovic, Alexander
Speed, Terence P
author_facet Molania, Ramyar
Gagnon-Bartsch, Johann A
Dobrovic, Alexander
Speed, Terence P
author_sort Molania, Ramyar
collection PubMed
description The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different datasets. We also offer suggestions on the design and analysis of studies involving this technology.
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spelling pubmed-66148072019-07-12 A new normalization for Nanostring nCounter gene expression data Molania, Ramyar Gagnon-Bartsch, Johann A Dobrovic, Alexander Speed, Terence P Nucleic Acids Res Computational Biology The Nanostring nCounter gene expression assay uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. These counts need to be normalized to adjust for the amount of sample, variations in assay efficiency and other factors. Most users adopt the normalization approach described in the nSolver analysis software, which involves background correction based on the observed values of negative control probes, a within-sample normalization using the observed values of positive control probes and normalization across samples using reference (housekeeping) genes. Here we present a new normalization method, Removing Unwanted Variation-III (RUV-III), which makes vital use of technical replicates and suitable control genes. We also propose an approach using pseudo-replicates when technical replicates are not available. The effectiveness of RUV-III is illustrated on four different datasets. We also offer suggestions on the design and analysis of studies involving this technology. Oxford University Press 2019-07-09 2019-05-22 /pmc/articles/PMC6614807/ /pubmed/31114909 http://dx.doi.org/10.1093/nar/gkz433 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research. 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 reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Molania, Ramyar
Gagnon-Bartsch, Johann A
Dobrovic, Alexander
Speed, Terence P
A new normalization for Nanostring nCounter gene expression data
title A new normalization for Nanostring nCounter gene expression data
title_full A new normalization for Nanostring nCounter gene expression data
title_fullStr A new normalization for Nanostring nCounter gene expression data
title_full_unstemmed A new normalization for Nanostring nCounter gene expression data
title_short A new normalization for Nanostring nCounter gene expression data
title_sort new normalization for nanostring ncounter gene expression data
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6614807/
https://www.ncbi.nlm.nih.gov/pubmed/31114909
http://dx.doi.org/10.1093/nar/gkz433
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