CLEP: A Hybrid Data- and Knowledge- Driven Framework for Generating Patient Representations

Table of Contents

• Table of Contents
• General Info
• Installation
• Documentation
• Input Data Formats
  • Data
  • Design
  • Knowledge Graph
• Usage
• Issues
• Acknowledgements
  • Citation
  • Graphics
• Disclaimer

General Info

CLEP is a framework that contains novel methods for generating patient representations from any patient level data and its corresponding prior knowledge encoded in a knowledge graph. The framework is depicted in the graphic below

Installation

NOTE: The installation of CLEP requires R to be installed on your system along with limma package for R. R can be downloaded from CRAN. The limma package can be installed in R with the following command:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("limma")

The code can be installed from PyPI with:

$ pip install clep

The most recent code can be installed from the source on GitHub with:

$ pip install git+https://github.com/hybrid-kg/clep.git

For developers, the repository can be cloned from GitHub and installed in editable mode with:

$ git clone https://github.com/hybrid-kg/clep.git
$ cd clep
$ pip install -e .

Documentation

Read the official docs for more information.

Input Data Formats

Data

Note: The data must be in a tab separated file format.

Design

Note: The data must be in a tab separated file format. The name of the "Target" column must be "Target" and the samples in the "FileName" column should match the names of the columns in the data file

Knowledge Graph

The graph format CLEP can handle is a modified version of the Edge List Format. Which looks as follows:

Note: The data must be in a tab separated file format & if your knowledge graph does not have relations between the source and the target, just populate the relation column with "No Relation".

Usage

Note: These are very basic commands for clep, and the detailed options for each command can be found in the documentation

1. Radical Searching The following command finds the extreme samples with extreme feature values based on the control population.

$ clep sample-scoring radical-search --data <DATA_FILE> --design <DESIGN_FILE> --control Control --threshold 2.5 --control_based --ret_summary --out <OUTPUT_DIR>

2. Graph Generation The following command generates the patient-gene network based on the method chosen (Interaction_network).

$ clep embedding generate-network --data <SCORED_DATA_FILE> --method interaction_network --ret_summary --out <OUTPUT_DIR>

3. Knowledge Graph Embedding

The following command generates the embedding of the network passed to it.

$ clep embedding kge --data <NETWORK_FILE> --design <DESIGN_FILE> --model_config <MODEL_CONFIG.json> --train_size 0.8 --validation_size 0.1 --out <OUTPUT_DIR>

4. Classification

The following command carries out classification on the given data file for a chosen model (Elastic Net) with 100 hyper-parameter optimization trials.

$ clep classify --data <EMBEDDING_FILE> --model elastic_net --num-trials 100 --out <OUTPUT_DIR>

Issues

If you have difficulties using CLEP, please open an issue at our GitHub repository.

Acknowledgements

Citation

If you have found CLEP useful in your work, please consider citing:

CLEP: A Hybrid Data- and Knowledge- Driven Framework for Generating Patient Representations.
Bharadhwaj, V. S., Ali, M., Birkenbihl, C., Mubeen, S., Lehmann, J., Hofmann-Apitius, M., Hoyt, C. T., & Domingo-Fernandez, D. (2020).
Bioinformatics, btab340.

Graphics

The CLEP logo and framework graphic was designed by Carina Steinborn.

Disclaimer

CLEP is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or guarantee of maintenance, support, or back-up of data.