Clustering a FAME data set
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.
A character is basically a name-value pair of which the value can be binary, multi-state or continuous. Because of this very broad definition, a wide variety of data can be analyzed as character types (= an array of characters). This includes morphological and biochemical features, commercial test panels (API®, Biolog®, Vitek®, etc.), antibiotics resistance profiles, fatty acid profiles, microarrays, SNP arrays, repeat numbers in MLVA, allelic profiles in MLST, etc.
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 dendrogram based on a binary data set.
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.
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.
Spoligotyping is widely used for differentation of Mycobacterium tuberculosis bacteria. In this tutorial the import of 43-digit binary spoligo codes and 15-digit octal codes is illustrated.
This tutorial shows how to import non-numerical data in a BIONUMERICS database and link the data to a character type experiment. It illustrates the use of character mappings in BIONUMERICS. Character mappings in BIONUMERICS are used to map categorical names (e.g. Present/Absent, Yes/No, Susceptible/Intermediate/Resistant, etc.) to character values (e.g. 0 and 1) or a range of values to according to predefined criteria.
This tutorial illustrates how to import MLVA repeat numbers and strain information from a text file. The same steps are also applicable for import of other character experiments.
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.
This tutorial shows how to import binary data (= 0 and 1 states) in a BIONUMERICS database and link the data to a character type experiment.
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.