Function reference • dyno

Choosing the optimal trajectory inference method dynguidelines
`answer_questions()`	Provide answers to various questions
`dynguidelines`	Dynguidelines packages
`get_answers_code()`	Produces the code necessary to reproduce a particular set of guidelines
`get_questions()` `get_question()`	Load in the questions
`get_renderers()`	Get all renderers
`guidelines()` `guidelines_shiny()`	Select the top methods, optionally based on a given dataset
`is_guidelines()`	Check whether object is guidelines
`label_capitalise()` `label_split()`	Labelling
`methods`	Metadata on the different TI methods
`shiny_server()`	The shiny server
`shiny_ui()`	Shiny user interface
Running trajectory inference methods dynmethods
`dynmethods`	Wrappers for trajectory inference methods
`methods`	Metadata on the different TI methods
`ti_angle()`	Inferring a trajectory inference using Angle
`ti_calista()`	Inferring a trajectory inference using CALISTA
`ti_cellrouter()`	Inferring a trajectory inference using CellRouter
`ti_celltrails()`	Inferring a trajectory inference using CellTrails
`ti_celltree_gibbs()`	Inferring a trajectory inference using cellTree with gibbs
`ti_celltree_maptpx()`	Inferring a trajectory inference using cellTree with maptpx
`ti_celltree_vem()`	Inferring a trajectory inference using cellTree with vem
`ti_comp1()`	Inferring trajectories with Component 1
`ti_dpt()`	Inferring a trajectory inference using DPT
`ti_elpicycle()`	Inferring a trajectory inference using ElPiGraph cycle
`ti_elpigraph()`	Inferring a trajectory inference using ElPiGraph
`ti_elpilinear()`	Inferring a trajectory inference using ElPiGraph linear
`ti_embeddr()`	Inferring a trajectory inference using Embeddr
`ti_error()`	Inferring trajectories with Control: error
`ti_fateid()`	Inferring a trajectory inference using FateID
`ti_forks()`	Inferring a trajectory inference using FORKS
`ti_gpfates()`	Inferring a trajectory inference using GPfates
`ti_grandprix()`	Inferring a trajectory inference using GrandPrix
`ti_identity()`	Inferring trajectories with Control: identity
`ti_matcher()`	Inferring a trajectory inference using MATCHER
`ti_merlot()`	Inferring a trajectory inference using MERLoT
`ti_mfa()`	Inferring a trajectory inference using mfa
`ti_monocle_ddrtree()`	Inferring a trajectory inference using Monocle DDRTree
`ti_monocle_ica()`	Inferring a trajectory inference using Monocle ICA
`ti_mpath()`	Inferring a trajectory inference using Mpath
`ti_ouija()`	Inferring a trajectory inference using ouija
`ti_ouijaflow()`	Inferring a trajectory inference using ouijaflow
`ti_paga()`	Inferring a trajectory inference using PAGA
`ti_pcreode()`	Inferring a trajectory inference using pCreode
`ti_periodpc()`	Inferring a trajectory inference using Periodic PrinCurve
`ti_phenopath()`	Inferring a trajectory inference using PhenoPath
`ti_projected_dpt()`	Inferring a trajectory inference using Projected DPT
`ti_projected_gng()`	Inferring a trajectory inference using Projected GNG
`ti_projected_monocle()`	Inferring a trajectory inference using Projected Monocle
`ti_projected_paga()`	Inferring a trajectory inference using Projected PAGA
`ti_projected_slingshot()`	Inferring a trajectory inference using Projected Slingshot
`ti_projected_tscan()`	Inferring a trajectory inference using Projected TSCAN
`ti_pseudogp()`	Inferring a trajectory inference using pseudogp
`ti_raceid_stemid()`	Inferring a trajectory inference using RaceID / StemID
`ti_random()`	Inferring trajectories with Control: random
`ti_recat()`	Inferring a trajectory inference using reCAT
`ti_scimitar()`	Inferring a trajectory inference using SCIMITAR
`ti_scorpius()`	Inferring a trajectory inference using SCORPIUS
`ti_scoup()`	Inferring a trajectory inference using SCOUP
`ti_scuba()`	Inferring a trajectory inference using SCUBA
`ti_shuffle()`	Inferring trajectories with Control: shuffle
`ti_sincell()`	Inferring a trajectory inference using Sincell
`ti_slice()`	Inferring a trajectory inference using SLICE
`ti_slicer()`	Inferring a trajectory inference using SLICER
`ti_slingshot()`	Inferring a trajectory inference using Slingshot
`ti_stemnet()`	Inferring a trajectory inference using STEMNET
`ti_topslam()`	Inferring a trajectory inference using topslam
`ti_tscan()`	Inferring a trajectory inference using TSCAN
`ti_urd()`	Inferring a trajectory inference using URD
`ti_wanderlust()`	Inferring a trajectory inference using Wanderlust
`ti_waterfall()`	Inferring a trajectory inference using Waterfall
`ti_wishbone()`	Inferring a trajectory inference using Wishbone
Toolbox to transform trajectory models dynwrap
`add_branch_trajectory()`	Define a trajectory model given its branch network and the pseudotime of the cells on one of the branches
`add_cell_graph()`	Constructs a trajectory using a graph between cells, by mapping cells onto a set of backbone cells.
`add_cell_waypoints()`	Add cell waypoints to a wrapped object with trajectory
`add_cluster_graph()`	Constructs a trajectory using a cell grouping and a network between groups. Will use an existing grouping if it is present in the model.
`add_cyclic_trajectory()`	Constructs a circular trajectory using the pseudotime values of each cell.
`add_dimred()` `is_wrapper_with_dimred()` `get_dimred()`	Add or create a dimensionality reduction
`add_dimred_projection()`	Constructs a trajectory by projecting cells within a dimensionality reduction onto a backbone formed by a milestone network. Optionally, a cell grouping can be given which will restrict the edges on which a cell can be projected.
`add_end_state_probabilities()`	Multifurcating trajectory with end state probabilities
`add_expression()` `is_wrapper_with_expression()` `get_expression()`	Add count and normalised expression values to a model
`add_grouping()` `is_wrapper_with_grouping()` `get_grouping()`	Add a cell grouping to a data wrapper
`add_linear_trajectory()`	Constructs a linear trajectory using the pseudotime values of each cell.
`add_prior_information()`	Add prior information to a data wrapper
`calculate_pseudotime()` `add_pseudotime()`	Add or calculate pseudotime as distance from the root
`add_root()`	Root the trajectory
`add_root_using_expression()`	Add root cell to wrapper using expression of features
`add_timing_checkpoint()`	Helper function for storing timings information.
`add_timings()`	Add count and normalised expression values to a model
`add_trajectory()`	Define a trajectory model given its milestone network and milestone percentages or progressions
`add_waypoints()`	Add or create waypoints to a trajectory
`allowed_inputs`	All allowed inputs
`allowed_outputs`	All allowed outputs
`calculate_average_by_group()`	Calculate mean values per cell group
`calculate_average_by_milestone_percentages()`	Calculate mean values by milestone percentages
`classify_milestone_network()`	Classify a milestone network
`compute_tented_geodesic_distances()` `compute_tented_geodesic_distances_()`	Calculate geodesic distances between cells in a trajectory, taking into account tents
`convert_milestone_percentages_to_progressions()`	Convert milestone percentages to progressions
`convert_progressions_to_milestone_percentages()`	Convert progressions to milestone percentages
`create_container_ti_method()`	Creating a TI method from a docker repository
`create_docker_ti_method()` `extract_definition_from_docker_image()` `pull_docker_ti_method()`	Create a TI method from a docker image
`create_singularity_ti_method()` `extract_definition_from_singularity_image()` `pull_singularity_ti_method()`	Create a TI method from a singularity image
`create_ti_method()`	Create a TI method wrapper
`determine_cell_trajectory_positions()`	Determine the positions of all cells in the trajectory
`dimred_trajectory()` `check_or_perform_dimred()`	Perform dimensionality reduction on a trajectory and the respective samples in order to plot it
`dynwrap`	This R package contains the code for a common model of single-cell trajectories.
`execute_method_internal()`	Internal method for executing a method
`execute_method_on_dataset()`	Run a method on a dataset with a set of parameters
`gather_cells_at_milestones()`	"Gather" cells to their closest milestones
`generate_prior_information()`	Extract the prior information from the milestone network
`get_cell_grouping()`	Group cells to their highest milestone
`get_default_parameters()`	Get the default parameters of a method
`get_ti_methods()`	Return all TI ti_methods
`group_onto_nearest_milestones()`	Grouping the cells onto the closest milestones
`group_onto_trajectory_edges()`	Grouping the cells onto their edges
`infer_trajectories()` `infer_trajectory()`	Infer trajectories
`is_data_wrapper()`	Test whether an object is a data_wrapper
`is_prediction()`	Tests whether an object is a trajectory created by a TI method.
`is_ti_method()`	Tests whether an object is a TI method description
`is_wrapper_with_prior_information()`	Test whether an object is a dataset and contains prior information
`is_wrapper_with_timings()`	Test whether an object is a model and has timings information
`is_wrapper_with_trajectory()`	Test whether an object is a model and has a trajectory
`is_wrapper_with_waypoint_cells()`	Test whether an object is a data_wrapper and cell waypoints
`is_wrapper_with_waypoints()`	Test whether an trajectory is a data_wrapper and waypoints
`label_milestones()` `label_milestones_markers()` `is_wrapper_with_milestone_labelling()` `get_milestone_labelling()`	Label milestones either manually (`label_milestones`) or using marker genes (`label_milestones_markers`)
`parse_parameter_definition()`	Parse a parameter definition
`prior_usages`	Metadata on prior usages
`priors`	Metadata on priors
`select_waypoint_cells()`	Select the waypoint cells
`select_waypoints()`	Select the waypoints
`simplify_igraph_network()`	Simplify an igraph network such that consecutive linear edges are removed
`simplify_trajectory()`	Simplify a trajectory
`simplify_trajectory_type()`	Convert directed trajectory type to simplified versions
`test_docker_installation()`	Tests whether docker is correctly installed and available
`ti_comp1()`	Inferring trajectories with Component 1
`ti_error()`	Inferring trajectories with Control: error
`ti_identity()`	Inferring trajectories with Control: identity
`ti_random()`	Inferring trajectories with Control: random
`ti_shuffle()`	Inferring trajectories with Control: shuffle
`trajectory_type_dag`	A DAG of trajectory types
`trajectory_types`	Metadata on the trajectory types
`trajectory_types_simplified`	Metadata on simplified trajectory types
`wrap_data()`	A data wrapper for datasets and trajectories
`wrap_expression()`	Create a wrapper object with expression and counts
`wrap_output()` `wrap_rds()` `wrap_text()` `wrap_feather()`	Wrap the output of a TI method
`wrap_prediction_model()`	An abstract data wrapper for TI predictions
Visualising trajectories dynplot
`add_cell_coloring()`	Add coloring
`add_density_coloring()`	Color cells using a background density
`add_milestone_coloring()`	Add milestone coloring
`dynplot`	Plot all the trajectories
`empty_plot()`	Create an empty plot for spacing
`get_milestone_palette_names()`	Get the names of valid color palettes
`linearise_cells()`	Linearise a trajectory
`milestone_palette()`	Wrapper for various palettes
`plot_dendro()`	Plot a tree trajectory as a dendrogram
`plot_dimred()`	Plot the trajectory on dimensionality reduction
`plot_edge_flips()`	Plotting edge flips
`plot_features()`	Plotting a set of features in a line plot
`plot_graph()`	Plot a dimensionality reduced trajectory as a 2D graph
`plot_heatmap()`	Plot the traj as a heatmap
`plot_linearised_comparison()`	Plot strip onedim
`plot_onedim()`	Plot onedim
`plot_strip()`	Plot strip
`plot_topology()`	Plotting the topology of a trajectory
`process_dynplot()`	Default theme for TI plots
`project_waypoints()`	Project the waypoints
`theme_clean()`	We like our plots clean
`theme_graph()`	We like our plots clean
Extracting relevant features from a trajectory libraries/dynfeature
`calculate_milestone_feature_importance()` `calculate_overall_feature_importance()` `calculate_waypoint_feature_importance()` `calculate_cell_feature_importance()` `calculate_branch_feature_importance()` `calculate_branching_point_feature_importance()`	Calculating feature importances across trajectories
`dynfeature`	Dynfeature feature importance

Reference

Choosing the optimal trajectory inference method

Running trajectory inference methods

Toolbox to transform trajectory models

Visualising trajectories

Extracting relevant features from a trajectory

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