All data to reproduce figures can be accessed here :

Two python(3) scripts are given separately for splicing and expression.

In directory data, you will find the input files.

They are based on the following version of scikit-learn (0.21.2.)

NB: Imputer warnings when script start is not an error.

They call one R script to plot survival over the rounds of classification.

python  classification_cell2patient_splicing.py \
	 -c {absolutepath}/MatriceExonPSI_CellLines.csv \
	 -p {absolutepath}/MatriceExonPSI_Patients.csv \
	 -t 0.6 \
	 -n 1000 \ 

python  classification_cell2patient_expression.py \
	 -c {absolutepath}/MatriceGeneTPM_CellLines.csv \
	 -p {absolutepath}/MatriceGeneTPM_Patients.csv \
	 -t 0.6 \
	 -n 1000 

• t : Threshold for class probabilities.
• c : Path to a matrice with Expression/Splicing values for Cell Lines.
• p : Path to a matrice with Expression/Splicing values for Patients.
• n : Number of tree in the forest.

The final file annotated is splicing_TCGA_BASAL_HEADER_ADDED.tsv.
You can visualize using https://software.broadinstitute.org/morpheus.

The best features of interest are in outputBorutaPy.txt/.bed.