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This task can be invoked from count matrix data node or clustering task report (Differential analysis > Statistics > Compute biomarkers). It performs Student's t-tests on the selected attribute, comparing one subgroup at a time vs all the others combined. The By default, the up-regulated genes as are reported as biomarkers.
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Compute biomarker dialog
In the set-up dialog, select the attribute from the drop down list. The available attributes are categorical attributes which can be seen on the Data tab (i.e. project-level attributes) as well as and data node-specific annotation, e.g. graph-based 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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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 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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