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organoid_biomarker_detection

Network-based biomarker detection from organoid models to predict drug response in cancer patients

Source code to reproduce the paper "Network-based machine-learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients", Kong et al

Requirements

  • python (v 2.7.13)
  • pandas (v 0.24.2)
  • matplotlib (v 2.0.0)
  • numpy (v 1.16.6)
  • scipy (v 1.2.2)
  • sklearn (v 0.20.2)
  • lifelines (v 0.19.5)
  • gseapy

Installation instruction

  • All python packages can be installed via pip (https://pypi.org/project/pip/).
  • e.g. pip install [package name].
  • Installations would take few minutes for each python package.

Code (for python)

  • "run_ssGSEA.py" to generate pathway level expression profiles using single sample GSEA (ssGSEA) tool (gseapy)
  • "single_pathway_prediction.py" to predict drug response in cancer patients using a single pathway
  • "multiple_pathway_prediction.py" to predict drug response in cancer patients using multiple pathways

Demo

  • Code for drug response prediction of 5fluorouracil-treated colorectal cancer patients using colorectal cancer organoids
  • Expected results are provided under "./python/results/" folder.
  • The majority of the code runs within several minutes, with the execption of "run_ssGSEA.py", which may take several hours depending on the size of a sample cohort.

Network proximity was calculated using codes from

  • 'Uncovering disease-disease relationships through the incomplete interactome' Menche et al, Science, 2015
  • 'Network-based in silico drug efficacy screening' Emre et al, Nature Communications, 2016
  • https://github.com/emreg00/toolbox

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