Trajectory Presence and Heterogeneity in Multivariate Data

Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity.

title: The 'TreeDimensionTest' R package bibliography: inst/REFERENCES.bib

Overview

The package offers tests for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha and Song, 2022) are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity.

To download and install the package

install.packages("DimensionTreeTest")

Examples

See the package vignette.

Citing the package

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 (URL: https://doi.org/10.1371/journal.pcbi.1009829).