doi:10.1371/journal.pcbi.1009829> 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.">

TreeDimensionTest: Trajectory Presence and Heterogeneity in Multivariate Data (original) (raw)

Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) <doi:10.1371/journal.pcbi.1009829> 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.

Version: 0.0.2
Depends: mlpack
Imports: fitdistrplus, igraph, nFactors, Rcpp (≥ 1.0.2), RColorBrewer, Rdpack
LinkingTo: Rcpp
Suggests: knitr, rmarkdown, testthat
Published: 2022-03-12
DOI: 10.32614/CRAN.package.TreeDimensionTest
Author: Lovemore Tenha ORCID iD [aut], Joe Song ORCID iD [aut, cre]
Maintainer: Joe Song
License: LGPL (≥ 3)
NeedsCompilation: yes
Citation: TreeDimensionTest citation info
Materials: README, NEWS
CRAN checks: TreeDimensionTest results

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