API

API#

Import GOT as:

import pygot

Preprocessing (pp)#

Dimension reduction

preprocessing.GS_VAE([device])

Dimension reduction model

Tools (tl)#

Trajectory Inference (traj)#

Velocity model training (awared with time label)

tools.traj.fit_velocity_model(adata, ...[, ...])

Estimates velocities and fit trajectories in latent space.

tools.traj.velocity(adata, odefunc[, ...])

Velocity inference using trained model.

tools.traj.latent_velocity(adata, odefunc[, ...])

Latent velocity inference using trained model.

tools.traj.latent2gene_velocity(adata, ...)

Transform latent velocity into gene velocity

tools.traj.simulate_trajectory(adata, ...[, ...])

Simulate trajecotry using trained NeuralODE model.

Source searching and time labeling (most for snapshot data)

tools.traj.fit_velocity_model_without_time(...)

Estimates velocities and fit trajectories in latent space WITHOUT time label.

tools.traj.determine_source_state(adata, ...)

Determine souce cell for snapshot data

Downstream Analysis (analysis)#

Density and pseudotime estimation

tools.analysis.ProbabilityModel([device])

Probability model for pseudotime estimation

Cell fate prediction

tools.analysis.CellFate()

Cell fate prediction based on the markov chain.

tools.analysis.TimeSeriesRoadmap(adata, ...)

Developmental tree inference based on the velocity graph.

Gene regulatory network (GRN) inference

tools.analysis.GRN()

Gene regulatory network infered by velocity linear regression

tools.analysis.GRNData(G_hat, beta_hat, ...)

Gene regulatory network data structure

Plotting (pl)#

Velocity visualization

plotting.plot_trajectory(adata, traj[, ...])

Root visualization

plotting.plot_root_cell(adata[, color, basis])

Datasets#

datasets.synthetic([file_path])

Sythetic data
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