Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/20852
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMolania, Ramyar-
dc.contributor.authorGagnon-Bartsch, Johann A-
dc.contributor.authorDobrovic, Alexander-
dc.contributor.authorSpeed, Terence P-
dc.date2019-05-22-
dc.date.accessioned2019-06-05T01:28:39Z-
dc.date.available2019-06-05T01:28:39Z-
dc.date.issued2019-
dc.identifier.citationNucleic acids research 2019; 47(12): 6073-6083-
dc.identifier.urihttp://ahro.austin.org.au/austinjspui/handle/1/20852-
dc.description.abstractThe 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.-
dc.language.isoeng-
dc.titleA new normalization for Nanostring nCounter gene expression data.-
dc.typeJournal Article-
dc.identifier.journaltitleNucleic acids research-
dc.identifier.affiliationDepartment of Mathematics and Statistics, University of Melbourne, Victoria 3010, Australiaen
dc.identifier.affiliationDepartment of Statistics, University of Michigan, Ann Arbor, Michigan, MI 48109, USA-
dc.identifier.affiliationDepartment of Clinical Pathology, The University of Melbourne, Parkville, Victoria 3010, Australiaen
dc.identifier.affiliationDepartment of Surgery, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australiaen
dc.identifier.affiliationTranslational Genomics and Epigenomics Laboratory, Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australiaen
dc.identifier.affiliationBioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria 3052, Australiaen
dc.identifier.affiliationDepartment of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australiaen
dc.identifier.affiliationSchool of Cancer Medicine and Molecular Cancer Prevention Program, La Trobe University, Bundoora, Victoria 3086, Australiaen
dc.identifier.doi10.1093/nar/gkz433-
dc.identifier.pubmedid31114909-
dc.type.austinJournal Article-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairetypeJournal Article-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
crisitem.author.deptOlivia Newton-John Cancer Research Institute-
crisitem.author.deptSurgery (University of Melbourne)-
Appears in Collections:Journal articles
Show simple item record

Page view(s)

2
checked on Jan 29, 2023

Google ScholarTM

Check


Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.