Infer linear trajectory through space

infer_trajectory infers a trajectory through samples in a given space in a four-step process:

1. Perform k-means clustering

2. Calculate distance matrix between cluster centers using a custom distance function

3. Find the shortest path connecting all cluster centers using the custom distance matrix

4. Iteratively fit a curve to the given data using principal curves

Usage

infer_trajectory(
  space,
  k = 4,
  thresh = 0.001,
  maxit = 10,
  stretch = 0,
  smoother = "smooth_spline",
  approx_points = 100
)

Arguments

space

A numeric matrix or a data frame containing the coordinates of samples.

k

The number of clusters to cluster the data into.

thresh

convergence threshold on shortest distances to the curve.

maxit

maximum number of iterations.

stretch

A stretch factor for the endpoints of the curve, allowing the curve to grow to avoid bunching at the end. Must be a numeric value between 0 and 2.

smoother

choice of smoother. The default is "smooth_spline", and other choices are "lowess" and "periodic_lowess". The latter allows one to fit closed curves. Beware, you may want to use iter = 0 with lowess().

approx_points

Approximate curve after smoothing to reduce computational time. If FALSE, no approximation of the curve occurs. Otherwise, approx_points must be equal to the number of points the curve gets approximated to; preferably about 100.

Value

A list containing several objects:

• path: the trajectory obtained by principal curves.

• time: the time point of each sample along the inferred trajectory.

See also

reduce_dimensionality, draw_trajectory_plot

Examples

## Generate an example dataset and visualise it
dataset <- generate_dataset(num_genes = 200, num_samples = 400, num_groups = 4)
space <- reduce_dimensionality(dataset$expression, ndim = 2)
draw_trajectory_plot(space, progression_group = dataset$sample_info$group_name)


## Infer a trajectory through this space
traj <- infer_trajectory(space)

## Visualise the trajectory
draw_trajectory_plot(space, path=traj$path, progression_group = dataset$sample_info$group_name)