Setting Up the Environment

Prerequisit: You must have installed python 3.x. All scripts were tested on Ubuntu 18.04.3 LTS

1. Install DeepLinkPrdiction package (for use of DLP model and manipulation of interaction networks):

   cd DeepLinkPrediction
   python setup.py install
   

2. Clone and Install OpenNE (For methods GraRep, DeepWalk, node2vec and LINE):

   cd OpenNE/src
   python setup.py install
   

3. Install forked EvalNE repository (for baseline methods and benchmarking framework). Clone the repository, then:

   cd EvalNE
   python setup.py install 
   

Demo of the DLP model

As a demonstration of how the DLP model can be called outside of the EvalNE framework, you will find a script called DLP_demo.py inside the demo folder. All necessary data is in this demo folder and the script can be run in its entirety in ~298 seconds on a server (hardware: 48-core CPU and 189GB RAM) and ~336 seconds locally (hardware: 4-core CPU and 16GB RAM).

Workflow for a single cancer type

1. Construct heterogeneous network by integrating the LOF screening data of a certain cancer type (--disease) with a functional interaction scaffold (--ppi_scaffold) using scripts/construct_train_test_edges_separate_args.py.

2. Construct train/test/validation edges using construct_train_test_edges_separately.py.

3. Run EvalNE API's

   • Run EvalNE_General_Performance/EvalNE_General_Performance_API.py to calculate the general performance (on gene-gene and gene-cell line interactions) of every method.
   • Run EvalNE_CellLine_specific_Performance/EvalNE_CellLine_specific_performance_API.py to calculate the dependency specific (gene-cell line interactions only) performance of every method.
   • Run EvalNE_CellLine_specific_total_predictions/EvalNE_CellLine_Specific_totalprediction_API.py to predict a probability for each possible gene-cell line combination

4. Run DLP models with the pretrained embeddings of DeepWalk.

   • Run DLP_baseline_initializer_Performance/DLP_baseline_initializer_performance_API.py twice to calculate the general and dependency specific performance, respectively.
   • Run DLP_baseline_initializer_Target_prediction/DLP_baseline_initializer_targetprediction_API.py to predict a probability for each possible gene-cell line combination

5. Run scripts/construct_combined_prediction_df.py to construct a combined pandas dataframe which averages the probabilities of each interaction over the three runs.

These 5 steps are automated in run_single_cancertype.sh for a single cancer type. To iterate over several cancer types use run_several_cancertypes.sh

Useful notes

• The parameter k-step of the method GraRep needs to be a multiple of the embedding dimension
• The parameter order of the method AROPE needs to be equal to the dimension of the weights parameter
• EvalNA API scripts are put in separate directories for the following reason: The version of EvalNE used in this work does not allow for the evaluation of different test sets on the same trained model. Hence, if we want to predict on a separate test and and also on a separate prediction set (eg all possible combinations of genes - cell line), 2 separate EvalNE runs are required. Additionally, EvalNE constructs tmp files on each run making it impossible to run EvalNE on 2 separate test sets at the same time impossible, unless each API call to EvalNE is sitauted in its own separate directory.