This task can be invoke invoked from count matrix data node or clustering task report , it performs (Differential analysis > Compute biomarkers). It performs Student's t-Tests tests on the selected attribute, comparing every group vs other for each subgroup and select the one subgroup at a time vs all the others combined. The up-regulated genes as biomarkerreported as biomarkers.

Computer biomarker dialog

In the set-up dialog, select the attribute from the drop down list. The available attributes are categorical attributes can be seen on the data node which includes project (i.e. project-level attributes) as well as and data node local -specific annotation, e.g. graph-based cluster clustering result (Figure 1). If the task is run on graph-based clustering output data node, the calculation is using upstream data node which contains feature counts – typically the input data node of PCA.

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Click on the Configure of  under Advanced options to change the criteria on for the output features (Figure 2).

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By default, the result outputs the top 10 features that are up-regulated by at least 1.5 fold change (in linear scale) for each subgroup comparing to others (ranked by the ascending p-values within each category)the others. The result is displayed in a table with each column is a subgroup name, each row is a feature. Features are ranked by the ascending p-values within each sub-category. An example is shown in Figure 3.  If a subgroup has fewer biomarkers than the others, the "extra" fields for that subgroup will be left blank.

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Furthermore, the Download link (lower right corner of the table report; Figure 3) downloads a .txt file to the local computer (default file name: Biomarkers.txt), which contains the full report: all the genes with fold change > 1.5, with corresponding fold change and p-values. If a subgroup has fewer biomarkers than the others, the "extra" table fields for that subgroup will be left blank. 

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