dyngen supports different trajectory topologies, such as bifurcating and cyclic. You can find a full list of backbones using ?list_backbones. This vignette will showcase each of them individually.
backbone <- backbone_linear()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
download_cache_dir = "~/.cache/dyngen/",
num_cores = 7
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |============= | 25% elapsed=00s, remaining~01s |========================= | 50% elapsed=01s, remaining~01s |====================================== | 75% elapsed=01s, remaining~00s |==================================================| 100% elapsed=02s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_bifurcating()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
download_cache_dir = "~/.cache/dyngen/",
num_cores = 7
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======== | 14% elapsed=01s, remaining~03s |=============== | 29% elapsed=01s, remaining~03s |====================== | 43% elapsed=01s, remaining~02s |============================= | 57% elapsed=02s, remaining~01s |==================================== | 71% elapsed=02s, remaining~01s |=========================================== | 86% elapsed=02s, remaining~00s |==================================================| 100% elapsed=03s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_bifurcating_converging()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
download_cache_dir = "~/.cache/dyngen/",
num_cores = 7
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======== | 14% elapsed=00s, remaining~01s |=============== | 29% elapsed=00s, remaining~01s |====================== | 43% elapsed=00s, remaining~01s |============================= | 57% elapsed=01s, remaining~00s |==================================== | 71% elapsed=01s, remaining~00s |=========================================== | 86% elapsed=01s, remaining~00s |==================================================| 100% elapsed=02s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_bifurcating_cycle()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
download_cache_dir = "~/.cache/dyngen/",
num_cores = 7
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======= | 12% elapsed=00s, remaining~01s |============= | 25% elapsed=00s, remaining~01s |=================== | 38% elapsed=01s, remaining~01s |========================= | 50% elapsed=01s, remaining~01s |================================ | 62% elapsed=01s, remaining~01s |====================================== | 75% elapsed=02s, remaining~01s |============================================ | 88% elapsed=02s, remaining~00s |==================================================| 100% elapsed=02s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_bifurcating_loop()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
download_cache_dir = "~/.cache/dyngen/",
num_cores = 7
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |======== | 14% elapsed=00s, remaining~01s |=============== | 29% elapsed=00s, remaining~01s |====================== | 43% elapsed=01s, remaining~01s |============================= | 57% elapsed=01s, remaining~01s |==================================== | 71% elapsed=01s, remaining~00s |=========================================== | 86% elapsed=01s, remaining~00s |==================================================| 100% elapsed=02s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_binary_tree(
num_modifications = 2
)
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
download_cache_dir = "~/.cache/dyngen/",
num_cores = 7
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |===== | 8 % elapsed=00s, remaining~04s |========= | 17% elapsed=01s, remaining~04s |============= | 25% elapsed=01s, remaining~04s |================= | 33% elapsed=01s, remaining~03s |===================== | 42% elapsed=02s, remaining~02s |========================= | 50% elapsed=02s, remaining~02s |============================== | 58% elapsed=02s, remaining~02s |================================== | 67% elapsed=02s, remaining~01s |====================================== | 75% elapsed=03s, remaining~01s |========================================== | 83% elapsed=03s, remaining~01s |============================================== | 92% elapsed=04s, remaining~00s |==================================================| 100% elapsed=04s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad
#> kinetics; choose a different seed and rerun.
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_branching(
num_modifications = 2,
min_degree = 3,
max_degree = 3
)
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
download_cache_dir = "~/.cache/dyngen/",
num_cores = 7
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |===== | 8 % elapsed=00s, remaining~05s |========= | 17% elapsed=01s, remaining~05s |============= | 25% elapsed=01s, remaining~04s |================= | 33% elapsed=02s, remaining~03s |===================== | 42% elapsed=02s, remaining~03s |========================= | 50% elapsed=02s, remaining~02s |============================== | 58% elapsed=02s, remaining~02s |================================== | 67% elapsed=03s, remaining~01s |====================================== | 75% elapsed=03s, remaining~01s |========================================== | 83% elapsed=04s, remaining~01s |============================================== | 92% elapsed=04s, remaining~00s |==================================================| 100% elapsed=04s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot
backbone <- backbone_consecutive_bifurcating()
init <- initialise_model(
backbone = backbone,
num_cells = 500,
num_tfs = 100,
num_targets = 50,
num_hks = 25,
simulation_params = simulation_default(census_interval = 10, ssa_algorithm = ssa_etl(tau = 300 / 3600)),
download_cache_dir = "~/.cache/dyngen/",
num_cores = 7
)
out <- generate_dataset(init, make_plots = TRUE)
#> Generating TF network
#> Sampling feature network from real network
#> Generating kinetics for 175 features
#> Generating formulae
#> Generating gold standard mod changes
#> Precompiling reactions for gold standard
#> Running gold simulations
#> | | 0 % elapsed=00s |===== | 8 % elapsed=00s, remaining~04s |========= | 17% elapsed=01s, remaining~04s |============= | 25% elapsed=01s, remaining~04s |================= | 33% elapsed=01s, remaining~03s |===================== | 42% elapsed=02s, remaining~02s |========================= | 50% elapsed=02s, remaining~02s |============================== | 58% elapsed=02s, remaining~02s |================================== | 67% elapsed=02s, remaining~01s |====================================== | 75% elapsed=03s, remaining~01s |========================================== | 83% elapsed=03s, remaining~01s |============================================== | 92% elapsed=04s, remaining~00s |==================================================| 100% elapsed=04s, remaining~00s
#> Precompiling reactions for simulations
#> Running 32 simulations
#> Mapping simulations to gold standard
#> Warning in .generate_cells_predict_state(model): Simulation does not contain all gold standard edges. This simulation likely suffers from bad
#> kinetics; choose a different seed and rerun.
#> Performing dimred
#> Simulating experiment
#> Wrapping dataset
#> Making plots
out$plot