This task can be invoke from count matrix data node or clustering task report, it performs t-Tests on selected attribute, comparing every group vs other for each subgroup and select the up-regulated genes as biomarker.

Computer biomarker dialog

In the dialog, select attribute. The available attributes are categorical attributes can be seen on the data node which includes project level attributes and data node local annotation, e.g. graph-based cluster 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.

Figure 1. Compute biomarker dialog: selecting attribute

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

Figure 2. Configure the biomarker filter criteria based on fold change

By default, the result outputs the top 10 features that are up-regulated 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 result is displayed in a table with each column is a subgroup name, each row is a feature. An example is shown in Figure 3. 

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.

Your Rating:

Thanks for voting!

Please Wait

Results:

2

rates