View source: R/TrajectoryGeometry.R
analyseSingleCellTrajectory | R Documentation |
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.
analyseSingleCellTrajectory(
attributes,
pseudotime,
randomizationParams,
statistic,
nSamples = 1000,
nWindows = 10,
d = ncol(attributes),
N = 1000
)
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). |
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
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)
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