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This Package Discovers Directionality in Time and Pseudo-times Series of Gene Expression Patterns

Bioconductor version: Release (3.19)

Given a time series or pseudo-times series of gene expression data, we might wish to know: Do the changes in gene expression in these data exhibit directionality? Are there turning points in this directionality. Do different subsets of the data move in different directions? This package uses spherical geometry to probe these sorts of questions. In particular, if we are looking at (say) the first n dimensions of the PCA of gene expression, directionality can be detected as the clustering of points on the (n-1)-dimensional sphere.

Author: Michael Shapiro [aut, cre]

Maintainer: Michael Shapiro <michael.shapiro at>

Citation (from within R, enter citation("TrajectoryGeometry")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

SingleCellTrajectoryAnalysis HTML R Script
TrajectoryGeometry HTML R Script
Reference Manual PDF


biocViews BiologicalQuestion, GeneExpression, SingleCell, Software, StatisticalMethod
Version 1.12.0
In Bioconductor since BioC 3.13 (R-4.1) (3 years)
License MIT + file LICENSE
Depends R (>= 4.1)
Imports pracma, rgl, ggplot2, stats, methods
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Suggests dplyr, knitr, RColorBrewer, rmarkdown
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Follow Installation instructions to use this package in your R session.

Source Package TrajectoryGeometry_1.12.0.tar.gz
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