| test.trajectory {TreeDimensionTest} | R Documentation |
Tree Dimension Test
Description
Computes the statistical significance for the presence of trajectory in multivariate data.
Usage
test.trajectory(
x,
perm = 100,
MST = c("boruvka", "exact"),
dim.reduction = c("pca", "none")
)
Arguments
x |
matrix of input data. Rows as observations and columns as features. |
perm |
number of simulations to compute null distribution parameters by maximum likelihood estimation. |
MST |
the MST algorithm to be used in test. There are two options: "exact" MST and "boruvka" which is approximate but faster for large samples. |
dim.reduction |
string parameter with value "pca" to perform dimensionality reduction or "none" to not perform dimensionality reduction before the test. |
Details
If the input data is already after dimension reduction, use
dim.reduction="none". The method is described in
(Tenha and Song 2022).
Value
A list with the following components:
tdt_measure The tree dimension value for the given input data
statistic The S statistic calculated on the input data. S statistic is derived from tree dimension
tdt_effect Effect size for tree dimension
leaves Number of leaf/degree1 vertices in the MST of the data
diameter The tree diameter of MST, where each edge is of unit length
p.value The pvalue for the S statistic. Pvalue measures presence of trajectory in input x.
original_dimension If "pca" is selected, the number of dimensions in the original dataset
pca_components If "pca" is selected, the number of pca components selected after dimensionality reduction
mst A vector of edges of the mst computed on x. Length of vector is always even.
References
Tenha L, Song M (2022). “Inference of trajectory presence by tree dimension and subset specificity by subtree cover.” PLOS Computational Biology, 18(2), e1009829. doi: 10.1371/journal.pcbi.1009829.