Importing binary character data
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.
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 shows how to import binary data (= 0 and 1 states) in a BioNumerics database and link the data to a character type experiment.
Is it possible to set different weights for each individual character in a character type experiment and calculate a similarity matrix using a weighted categorical coefficient?
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.
In BioNumerics, character values can be mapped to categorical names according to predefined criteria (see this tutorial for more information about the use of mappings in BioNumerics). When character mappings are present, it becomes possible to define a custom mappings similarity matrix, which determines how similarities are calculated among the mappings. This can be useful when analyzing data sets like SNPs, VNTRs, SSRs, etc. In this tutorial the use of a custom mappings matrix is illustrated.
This tutorial illustrates how to create a dendrogram based on character data coming from different experiments, using a composite data set.
This tutorial illustrates how to calculate a dendrogram based on a binary data set.
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 create a Minimum Spanning Tree (MST) based on MLVA repeat numbers. The same steps are also applicable for clustering of other categorical character data sets such as MLST.
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.