dyngen 1.0.1 2021-04-12

  • MINOR CHANGE .download_cacheable_file(): Check the return value of utils::download.file(), since it is possible that the download will fail with a non-zero status but not an R error.

  • MINOR CHANGE: kinetics_random_distributions(): Add function for providing randomised distributions.

dyngen 1.0.0 2021-02-23

This version mostly upgrades dyngen’s ease-of-use, such as better vignettes, conversion functions for working with dyngen datasets in other packages, and more useful ways of specifying platform-specific parameters (i.e. number of cores and cache location). Perhaps more excitingly, the dyngen documentation is more readable online at https://dyngen.dynverse.org!

BREAKING CHANGES

NEW FEATURES

MAJOR CHANGES

  • generate_experiment(): Map count density of reference dataset to simulation expression before sampling molecules. Parameters are available for toggling off or on the mapping of the reference library size & CPM distribution.

MINOR CHANGES

BUG FIX

DOCUMENTATION

  • Added and extended vignettes:

    • Advanced: Simulating batch effects
    • Advanced: Simulating a knockout experiment
    • Advanced: Running dyngen from a docker container
    • Advanced: Constructing a custom backbone
    • Advanced: Tweaking parameters
    • Advanced: Comparison of characteristic features between dyngen and reference datasets
  • Created a website at https://dyngen.dynverse.org using pkgdown.

  • Shortened examples to reduce r cmd check time.

dyngen 0.4.0 2020-09-10

MAJOR CHANGES

  • wrap_dataset(): Outputted $counts now contains counts of both spliced and unspliced reads, whereas $counts_unspliced and $counts_spliced contains separated counts.

  • Added a docker container containing the necessary code to run a dyngen simulation.

MINOR CHANGES

  • Added logo to package.

  • Clean up internal code, mostly to satisfy R CMD check.

DOCUMENTATION

  • Added two vignettes.

  • Expanded the README.

dyngen 0.3.0 (2020-04-06) Unreleased

NEW FEATURES

  • Implement knockdown / knockouts / overexpression experiments.

  • Implement better single-cell regulatory activity by determining the effect on propensity values after knocking out a transcription factor.

  • Implement adding noise to the kinetic params of individual simulations.

  • Kinetics (transcription rate, translation rate, decay rate, …) are based on Schwannhausser et al. 2011.

  • Changed many parameter names to better explain its purpose.

MINOR CHANGES

BUG FIXES

  • Implement fix for double positives in bblego backbones.

  • Fix graph plotting mixup of interaction effects (up/down).

  • Made a fix to the computation of feature_info$max_protein.

dyngen 0.2.1 (2019-07-17) Unreleased

  • MAJOR CHANGES: Custom backbones can be defined using backbone lego pieces. See ?bblego for more information.

  • MAJOR CHANGES: Splicing reactions have been reworked to better reflect biology.

dyngen 0.2.0 (2019-07-12) Unreleased

Complete rewrite from dyngen from the bottom up.

  • OPTIMISATION: All aspects of the pipeline have been optimised towards execution time and end-user usability.

  • OPTIMISATION: dyngen 0.2.0 uses gillespie 0.2.0, which has also been rewritten entirely in Rcpp, thereby improving the speed significantly.

  • OPTIMISATION: The transcription factor propensity functions have been refactored to make it much more computationally efficient.

  • OPTIMISATION: Mapping a simulation to the gold standard is more automised and less error-prone.

  • FEATURE: A splicing step has been added to the chain of reaction events.

dyngen 0.1.0 (2017-04-27) Unreleased

  • INITIAL RELEASE: a package for generating synthetic single-cell data from regulatory networks. Key features are:

    • The cells undergo a dynamic process throughout the simulation.
    • Many different trajectory types are supported.
    • dyngen 0.1.0 uses gillespie 0.1.0, a clone of GillespieSSA that is much less error-prone and more efficient than GillespieSSA.

dyngen 0.0.1 (2016-04-04) Unreleased

  • Just a bunch of scripts on a repository, which creates random networks using igraph and generates simple single-cell expression data using GillespieSSA.