generate_dataset(): Fix subplot title.
plot_summary(): Create a dedicated function for plotting a dyngen summary.
DOCUMENTATION: Update citation to NCOMMS publication.
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!
Added functions for converting the dyngen output to various data formats:
as_anndata() for anndata,
as_sce() for SingleCellExperiment,
as_seurat() for Seurat,
as_dyno() for dyno,
as_list() for a simple list object.
The default number of cores used can be set by adding
options(Ncpus = ...) to your Rprofile.
The default cache folder for dyngen can be set by adding
options(dyngen_download_cache_dir = ...) to your Rprofile.
Combine similar models with different outputs using the
Store the timings throughout the dyngen execution. Extract the timings from a model using
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.
Added and extended vignettes:
Created a website at https://dyngen.dynverse.org using pkgdown.
Shortened examples to reduce r cmd check time.
$counts now contains counts of both spliced and unspliced reads, whereas
$counts_spliced contains separated counts.
Added a docker container containing the necessary code to run a dyngen simulation.
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.
Fix module naming of backbones derived from
Allow to plot labels in
Rename required columns in
backbone() input data.
backbone_linear() to make
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.
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.
Complete rewrite from
dyngen from the bottom up.
OPTIMISATION: All aspects of the pipeline have been optimised towards execution time and end-user usability.
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.
INITIAL RELEASE: a package for generating synthetic single-cell data from regulatory networks. Key features are:
gillespie0.1.0, a clone of
GillespieSSAthat is much less error-prone and more efficient than