| template_groups {itol.toolkit} | R Documentation |
template groups
Description
Templates were clustered into 5 groups by parameter similarity.
Usage
template_groups
Format
template_groups
A data frame with template group clustering reslut:
- template
All the 23 template types of iTOL
- group
5 clustring gourps: Tree structure: This group only controls the topology of tree branch merging, filtering, and spacing. There are no style and rich annotation data, even though most of the annotation data only include single-column id information and do not contain any dataset base information, sample information, or common and specific style information. It is a particularly simple type of template. Theme style: This does not change any topology or add any text information but only changes the color scheme, line type and width, and font style and size of existing information. This is an extremely comprehensive and diverse type of annotation information. Text: This group contains any templates with added text information. With super flexible and convenient annotation methods, users can modify even a single character's style in HTML. Users can also modify the text annotation style of nodes and branches in batch based on matching conditions in itol.hub objects, which require regular expression replacement and precise data filtering. This high-frequency data processing is difficult to achieve and retain the workflow in the EXCEL-based editor. Basic plot: This group contains basic visualization methods. From a functional point of view, this is the most feature-rich class of templates. The similarity of the parameters within this part is very high.The structured and uniform organization of these templates can greatly reduce code redundancy and the user workload of data organizing. Moreover, boxplot, which is not a regular enough data annotation template, can be automatedly manipulated in R. The lack of template data structure makes using frequency unbalanced among research. Hence, the frequency of using these low-frequency templates can be increased. Advanced plot: Compared with the basic visualization methods, these visualization methods contain more comprehensive data types and often require third-party tools for input data processing. But they are the most extensible type of visualization methods for iTOL.
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