Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/20852
Title: A new normalization for Nanostring nCounter gene expression data.
Austin Authors: Molania, Ramyar;Gagnon-Bartsch, Johann A;Dobrovic, Alexander ;Speed, Terence P
Affiliation: Department of Mathematics and Statistics, University of Melbourne, Victoria 3010, Australia
Department of Statistics, University of Michigan, Ann Arbor, Michigan, MI 48109, USA
Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010, Australia
Department of Surgery, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Translational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia
Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
School of Cancer Medicine and Molecular Cancer Prevention Program, La Trobe University, Bundoora, Victoria 3086, Australia
Issue Date: 2019
metadata.dc.date: 2019-05-22
Publication information: Nucleic acids research 2019; 47(12): 6073-6083
Abstract: 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.
URI: http://ahro.austin.org.au/austinjspui/handle/1/20852
DOI: 10.1093/nar/gkz433
PubMed URL: 31114909
Type: Journal Article
Appears in Collections:Journal articles

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