analyseSingleCellTrajectory {TrajectoryGeometry} | R Documentation

## Analyse a single cell trajectory.

### Description

This function analyses a single cell trajectory by sampling multiple paths and comparing each path to random paths. It takes vector of pseudotime values, and a matrix of attribute values (cell x attribute). It also optionally takes the number of pseudotime windows to sample a single cell from. This defaults to 10. The function returns a list of Answers for each comparison of a sampled path to a random path.

### Usage

    analyseSingleCellTrajectory(
    attributes,
    pseudotime,
    randomizationParams,
    statistic,
    nSamples = 1000,
    nWindows = 10,
    d = ncol(attributes),
    N = 1000
    )

### Arguments

`attributes` | - An n x d (cell x attribute) matrix of numeric attributes for single cell data. Rownames should be cell names.
`pseudotime` | - A named numeric vector of pseudotime values for cells.
`randomizationParams` | - A character vector which is used to control the production of randomized paths for comparison.
`statistic` | - Allowable values are 'median', 'mean' or 'max'.
`nSamples` | - The number of sampled paths to generate (default 1000).
`nWindows` | - The number of windows pseudotime should be split into to sample cells from (defaults to 10).
`d` | - The dimension under consideration. This defaults to ncol(attributes).
`N` | - The number of random paths to generated for statistical comparison to the given path (defaults to 1000).

### Value

This returns a list, where each entry is itself a list containing information comparing a sampled path to random paths. These entries consist of: pValue - the p-value for the path and statistic in question; sphericalData - a list containing the projections of the path to the sphere, the center of that sphere and the statistic for distance to that center; randomDistances - the corresponding distances for randomly chosen; paths; randomizationParams - the choice of randomization parameters

### Examples

    chol_answers = analyseSingleCellTrajectory(chol_attributes[,seq_len(3)],
                                            chol_pseudo_time_normalised,
                                            nSamples = 10,
                                            randomizationParams =
                                                        c('byPermutation',
                                                    'permuteWithinColumns'),
                                            statistic = "mean",
                                            N = 1)
    hep_answers = analyseSingleCellTrajectory(hep_attributes[,seq_len(3)],
                                            hep_pseudo_time_normalised,
                                            nSamples = 10,
                                            randomizationParams =
                                                    c('byPermutation',
                                                    'permuteWithinColumns'),
                                            statistic = "mean",
                                            N = 1)

[Package TrajectoryGeometry version 1.0.0 Index]