Characters

BioNumerics offers a generalized and well-documented implementation of ANOVA (Analysis of Variance) and MANOVA (Multivariate Analysis of Variance) with comprehensive statistical analysis and validation testing tools. These very useful statistical methods allow you to investigate the relation between groups of entries and characters, as well as the significance of such groups. In this tutorial the (M)ANOVA tool implemented in BioNumerics will be illustrated using a sample data set.

This tutorial illustrates how to import zone diameter interpretive standards and equivalent minimal inhibitory concentration (MIC) breakpoints in a BioNumerics database and how BioNumerics automatically converts your antibiotics data into Sensitive, Intermediate and Resistant (SIR) categories based on the defined standards.

This tutorial illustrates how to cluster character data in BioNumerics. A test assay that reveals the metabolic activity of bacteria on different compounds is used as an example.

This tutorial illustrates how to calculate a dendrogram based on character data in BioNumerics. A fatty acid methyl ester (FAME) data set is used as an example.

This tutorial illustrates how to calculate a Principal Components Analysis (PCA) and a Multi Dimensional Scaling (MDS) on a character data set and how to change the layout of the obtained plots.

This tutorial illustrates how to create a Minimum Spanning Tree (MST) based on MLST allele numbers. The same steps are also applicable for clustering of other categorical character data sets such as MLVA.

This tutorial illustrates how to import MLST allelic profiles and strain information from an Excel file. The same steps are also applicable for import of other character experiments.