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README.md
In dyngen: A Multi-Modal Simulator for Spearheading Single-Cell Omics Analyses
dyngen 
dyngen is a novel, multi-modal simulation engine for studying dynamic
cellular processes at single-cell resolution. dyngen is more flexible
than current single-cell simulation engines, and allows better method
development and benchmarking, thereby stimulating development and
testing of novel computational methods.
dyngen is now published (CC-BY,
doi:10.1038/s41467-021-24152-2).
Run citation("dyngen") to obtain the corresponding citation
information. All source code for reproducing the results in this
manuscript are available on
GitHub.
Installation
dyngen should work straight out of the CRAN box by running
install.packages("dyngen"). Having said that, you should definitely
configure a few system variables for optimal speed. Check the
installation
guide for more
information!
Getting started
Check out this
guide on how
to get started with dyngen. You can find more guides by clicking any of
the links below:
- Getting
started
- Installation
instructions
- Showcase different
backbones
- Advanced: Comparison to reference
dataset
- Advanced: Constructing a custom
backbone
- Advanced: Running dyngen from a docker
container
- Advanced: On scalability and
runtime
- Advanced: Simulating batch
effects
- Advanced: Simulating a knockout
experiment
- Advanced: Tweaking
parameters
Latest changes
A full list of changes is available on our
changelog.
Try the dyngen package in your browser
Any scripts or data that you put into this service are public.
dyngen documentation built on Oct. 12, 2022, 5:06 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
dyngen
dyngen is a novel, multi-modal simulation engine for studying dynamic cellular processes at single-cell resolution. dyngen is more flexible than current single-cell simulation engines, and allows better method development and benchmarking, thereby stimulating development and testing of novel computational methods.
dyngen is now published (CC-BY,
doi:10.1038/s41467-021-24152-2).
Run citation("dyngen") to obtain the corresponding citation
information. All source code for reproducing the results in this
manuscript are available on
GitHub.
Installation
dyngen should work straight out of the CRAN box by running
install.packages("dyngen"). Having said that, you should definitely
configure a few system variables for optimal speed. Check the
installation
guide for more
information!
Getting started
Check out this guide on how to get started with dyngen. You can find more guides by clicking any of the links below:
- Getting started
- Installation instructions
- Showcase different backbones
- Advanced: Comparison to reference dataset
- Advanced: Constructing a custom backbone
- Advanced: Running dyngen from a docker container
- Advanced: On scalability and runtime
- Advanced: Simulating batch effects
- Advanced: Simulating a knockout experiment
- Advanced: Tweaking parameters
Latest changes
A full list of changes is available on our changelog.
Try the dyngen package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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