dyngen 0.4.0 (2020-07-15)

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)

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:

• Fix module naming of backbones derived from backbone_branching().

• Allow to plot labels in plot_simulation_expression().

• Improve backbone_disconnected() and backbone_converging().

• Rename required columns in backbone() input data.

• Use backbone_linear() to make backbone_cyclic() randomised.

• Added a decay rate for pre-mRNAs as well.

• Kinetics: redefine the decay rates in terms of the half-life of these molecules.

• Only compute dimred if desired.

• Allow computing the propensity ratios as ground-truth for rna velocity.

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)

• 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)

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)

• 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)

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