Schmitt, Armin

[["dc.bibliographiccitation.artnumber","85"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Virology Journal"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Lange, Thomas M."],["dc.contributor.author","Rotärmel, Maria"],["dc.contributor.author","Müller, Dominik"],["dc.contributor.author","Mahone, Gregory S."],["dc.contributor.author","Kopisch-Obuch, Friedrich"],["dc.contributor.author","Keunecke, Harald"],["dc.contributor.author","Schmitt, Armin O."],["dc.date.accessioned","2022-06-01T09:39:42Z"],["dc.date.available","2022-06-01T09:39:42Z"],["dc.date.issued","2022"],["dc.date.updated","2022-07-29T12:17:39Z"],["dc.description.abstract","Background In research questions such as in resistance breeding against the Beet necrotic yellow vein virus it is of interest to compare the virus concentrations of samples from different groups. The enzyme-linked immunosorbent assay (ELISA) counts as the standard tool to measure virus concentrations. Simple methods for data analysis such as analysis of variance (ANOVA), however, are impaired due to non-normality of the resulting optical density (OD) values as well as unequal variances in different groups. Methods To understand the relationship between the OD values from an ELISA test and the virus concentration per sample, we used a large serial dilution and modelled its non-linear form using a five parameter logistic regression model. Furthermore, we examined if the quality of the model can be increased if one or several of the model parameters are defined beforehand. Subsequently, we used the inverse of the best model to estimate the virus concentration for every measured OD value. Results We show that the transformed data are essentially normally distributed but provide unequal variances per group. Thus, we propose a generalised least squares model which allows for unequal variances of the groups to analyse the transformed data. Conclusions ANOVA requires normally distributed data as well as equal variances. Both requirements are not met with raw OD values from an ELISA test. A transformation with an inverse logistic function, however, gives the possibility to use linear models for data analysis of virus concentrations. We conclude that this method can be applied in every trial where virus concentrations of samples from different groups are to be compared via OD values from an ELISA test. To encourage researchers to use this method in their studies, we provide an R script for data transformation as well as the data from our trial."],["dc.identifier.citation","Virology Journal. 2022 May 18;19(1):85"],["dc.identifier.doi","10.1186/s12985-022-01804-3"],["dc.identifier.pii","1804"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/108541"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-572"],["dc.publisher","BioMed Central"],["dc.relation.eissn","1743-422X"],["dc.rights.holder","The Author(s)"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject","Data analysis"],["dc.subject","Virus concentration"],["dc.subject","Serial dilution"],["dc.subject","Logistic regression"],["dc.subject","Generalised least squares model"],["dc.subject","Beet necrotic yellow vein virus"],["dc.subject","BNYVV"],["dc.title","Non-linear transformation of enzyme-linked immunosorbent assay (ELISA) measurements allows usage of linear models for data analysis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","956"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","Agriculture"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Lange, Thomas M."],["dc.contributor.author","Wutke, Martin"],["dc.contributor.author","Bertram, Lisa"],["dc.contributor.author","Keunecke, Harald"],["dc.contributor.author","Kopisch-Obuch, Friedrich"],["dc.contributor.author","Schmitt, Armin O."],["dc.date.accessioned","2021-12-01T09:22:44Z"],["dc.date.available","2021-12-01T09:22:44Z"],["dc.date.issued","2021"],["dc.description.abstract","The Beet necrotic yellow vein virus (BNYVV) causes rhizomania in sugar beet (Beta vulgaris L.), which is one of the most destructive diseases in sugar beet worldwide. In breeding projects towards resistance against BNYVV, the enzyme-linked immunosorbent assay (ELISA) is used to determine the virus concentration in plant roots and, thus, the resistance levels of genotypes. Here, we present a simulation study to generate 10,000 small samples from the estimated density functions of ELISA values from susceptible and resistant sugar beet genotypes. We apply receiver operating characteristic (ROC) analysis to these samples to optimise the cutoff values for sample sizes from two to eight and determine the false positive rates (FPR), true positive rates (TPR), and area under the curve (AUC). We present, furthermore, an alternative approach based upon Bayes factors to improve the decision procedure. The Bayesian approach has proven to be superior to the simple cutoff approach. The presented results could help evaluate or improve existing breeding programs and help design future selection procedures based upon ELISA. An R-script for the classification of sample data based upon Bayes factors is provided."],["dc.description.abstract","The Beet necrotic yellow vein virus (BNYVV) causes rhizomania in sugar beet (Beta vulgaris L.), which is one of the most destructive diseases in sugar beet worldwide. In breeding projects towards resistance against BNYVV, the enzyme-linked immunosorbent assay (ELISA) is used to determine the virus concentration in plant roots and, thus, the resistance levels of genotypes. Here, we present a simulation study to generate 10,000 small samples from the estimated density functions of ELISA values from susceptible and resistant sugar beet genotypes. We apply receiver operating characteristic (ROC) analysis to these samples to optimise the cutoff values for sample sizes from two to eight and determine the false positive rates (FPR), true positive rates (TPR), and area under the curve (AUC). We present, furthermore, an alternative approach based upon Bayes factors to improve the decision procedure. The Bayesian approach has proven to be superior to the simple cutoff approach. The presented results could help evaluate or improve existing breeding programs and help design future selection procedures based upon ELISA. An R-script for the classification of sample data based upon Bayes factors is provided."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/agriculture11100956"],["dc.identifier.pii","agriculture11100956"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94473"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation.eissn","2077-0472"],["dc.relation.orgunit","Abteilung Züchtungsinformatik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Decision Strategies for Absorbance Readings from an Enzyme-Linked Immunosorbent Assay—A Case Study about Testing Genotypes of Sugar Beet (Beta vulgaris L.) for Resistance against Beet Necrotic Yellow Vein Virus (BNYVV)"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]