Welcome to DKOsimR Documentation!

DKOsimR is an R package for running Double-CRISPR Knockout Simulation (DKOsim). DKOsim is a simulation framework designed to simulate growth-based dual knockout CRISPR screens. It allows users and investigators to efficiently reproduce synthetic data where both the single gene fitness effect and the interaction of gene pairs can be pre-specified.

Quick Start

This page introduces DKOsimR package for generating synthetic CRISPR double knockout data. See Tutorial for a quickstart on installation and run simulations. A vignettes file of DKOsimR is available in the R package, and a pre-built output preview (PDF) can be downloaded here.

For more details to tune and customize your CRISPR simulation, check Running Simulation and Laboratory Data Approximation.

Applying GI Detection Methods on Simulated Data provides a quick guide and example on how to apply GI detection method on simulated output data, and how to visualize the results.

Finally, we supply the Appendix for columns description of the simulated dataset.

Paper References

Gu, Y., Hart, T. Leon-Novelo, L., & Shen, J.P. (2026). Double-CRISPR Knockout Simulation (DKOsim): A Monte-Carlo Randomization System to Model Cell Growth Behavior and Infer the Optimal Library Design for Growth-Based Double Knockout Screens. PLoS Computational Biology. 22(4): e1013510. https://doi.org/10.1371/journal.pcbi.1013510

Shen, J., Zhao, D., Sasik, R. et al (2017). Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nature Methods 14, 573-576 (2017). https://doi.org/10.1038/nmeth.4225

Note

The simulation and documentations are under active development.

Contents

• Tutorial
  • Introduction
  • Installation
  • Graphical Overview of Study Design
  • List of Tunable Parameters
  • Quick Start
  • Customized Simulation
  • Simulation Approximating Laboratory Data
  • Summary
• Running Simulation
  • Default Values of Tunable Parameters
  • Run Simulation by Default
  • Run Customized Simulation
• Laboratory Data Pattern Approximation
  • Default Lab Mode
  • Customized Lab Mode
  • References
• Applying GI Detection Methods on Simulated Data
  • Delta Log Fold Change (dLFC) Application
  • Visualizations
  • References
• Summary Guidance in Picking Suitable Parameters
  • How should I choose parameters?
  • Suggestions on laboratory data approximation
  • References
• Appendix
  • Column descriptions of Simulated Datasets