Visualization of results

Figure 2.24: Icon for the visualization module.

Figure 2.25: Components of the visualization module. The bottom window is used to create and define groups, while the top window allows to select result and visualization type, customize options and display the final chart.

The visualization component actually consists of two separate windows (2.25). The Group Window is located at the bottom and used to create, name and define groups used for data display. Sequencing runs can be added to individual groups using Drag and Drop. While any sequencing run can be present in several groups at the same time, it is not possible to add it to a group more than once. Groups may be assigned a name, their display color can be chosen by the user, and they may be temporarily excluded from display.
The main visualization window is shown at the top; once sequencing runs are added or changed, the type of analysis result to be shown can be chosen on the top right. Afterwards, the desired visualization type is selected and may be customized, as well.

Figure 2.26: Visualization example displaying a bar chart for two groups.

Figure 2.26 shows a simple visualization example. Two groups were defined containing the simLC and simMC metagenomes in the first and the simHC metagenome in the second group. The main visualization window shows a bar chart generated for these groups based on assigned EC numbers. To account for differences in dataset size, both groups are normalized to fractions.

Figure 2.27: In case a result is provided by more than one job, e.g. different taxonomic assignment methods, a dialog allows to select between jobs. In this example, taxonomic assignments are provided by MGX pipelines employing Kraken [Wood and Salzberg, 2014] as well as MetaCV [Liu et al., 2012].

Whenever a selected result type is provided by more than one analysis job, an interactive dialog allows the user to review possible jobs and select one (2.27). This scenario will occur when a tool is executed several times with different parameters, or when different taxonomic classifiers were used to analyse a dataset.