DOI: 10.18129/B9.bioc.TrajectoryGeometry
This is the development version of TrajectoryGeometry; for the stable release version, see
TrajectoryGeometry.
This Package Discovers Directionality in Time and Pseudo-times Series of Gene Expression Patterns
Bioconductor version: Development (3.16)
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 crick.ac.uk>
Citation (from within R,
enter citation("TrajectoryGeometry")
):
Installation
To install this package, start R (version
“4.2”) and enter:
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager") # The following initializes usage of Bioc devel BiocManager::install(version='devel') BiocManager::install("TrajectoryGeometry")
For older versions of R, please refer to the appropriate
Bioconductor release.
Documentation
To view documentation for the version of this package installed
in your system, start R and enter:
browseVignettes("TrajectoryGeometry")
Details
biocViews | BiologicalQuestion, GeneExpression, SingleCell, Software, StatisticalMethod |
Version | 1.5.0 |
In Bioconductor since | BioC 3.13 (R-4.1) (1 year) |
License | MIT + file LICENSE |
Depends | R (>= 4.1) |
Imports | pracma, rgl, ggplot2, stats, methods |
LinkingTo | |
Suggests | dplyr, knitr, RColorBrewer, rmarkdown |
SystemRequirements | |
Enhances | |
URL | |
Depends On Me | |
Imports Me | |
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