In this page we provide a walkthrough and a brief discussion of the Prediction tool. This comprises the training of a model and its testing with a different split of data.

1. Log into HiPathia. For further information on this step visit Logging in.

2. Selection of test data. We will work with a Breast Cancer dataset from the repository The Cancer Genome Atlas (TCGA) Link to dataset. More information on the original dataset is available here:

* https://www.nature.com/articles/nature11412

* https://pubmed.ncbi.nlm.nih.gov/23644459/

We have selected a subset of Breast Cancer samples from the dataset annotated as luminal A or luminal B (the molecular annotations come from this paper) that were not used in the training of the model that we want to test.

You can download the expression matrix we use to test the model from this link:

• Test expression matrix: brca_sub_class_exp_test.txt

3. Upload the test data to HiPathia in the data panel by clicking My data. For further information on this step visit Upload your data.

4. Click the Prediction button.

5. In the Type panel, select Test existing predictor. A window with all the existing models will appear. Select the model you want to use. The model information will appear on the right panel. You can follow the steps in Worked example Prediction - Train to train your own model with your data. We will test the model we have trained in that guided example.

6. In the Input data panel select Expression matrix. Click the File browser in the Expression matrix file section and select the desired file: brca_genes_vals_bn_test.txt.

7. In the Job information panel, click the File browser button and select the desired output folder. In this case, we will use analysis_BRCA. Give a name to the study, for example, “BRCA test model”.

8. Click the Run analysis button. A study will be created and listed in the studies panel. You can access this panel by clicking on the My studies button.

This section provides a walkthrough of the report page generated when testing a previously trained model with another split of data.

Here appears the information about the selected study.

• Name: the study name.
• Description: the description of the current study.
• Tool: the name of the used tool (in this case, is Hipathia).
• Date: study's launching date (MM/DD/AAAA, HH:MM:SS AM/PM format)

Here appear the parameters with which the current study was launched.

• Expression file: The name of the expression file that has been used in the current study.
• Species: The species of this experiment; Human (Homo sapiens), Mouse (Mus musculus), or Rat (Rattus norvegicus).

The matrix of circuit activity values can be downloaded by clicking circuit values. This matrix file indicates for each “effector circuit” the level of activation calculated using the HiPathia method for each sample.

This is the most important result of our predictor, which is a matrix with three columns:

• Sample name: all the 125 samples in the used expression matrix file.
• Prediction: the predicted group LumB (Luminal B) or LumA (Luminal A)
• Probability LumB: this is the probability of being lumB, if it is 1 that means the predictor is 100% sure that the given result will be LumB.

You can download the matrix of predicted experimental design by clicking on Prediction results.