hilbertSimilarity: Hilbert Similarity Index for High Dimensional Data

Quantifying similarity between high-dimensional single cell samples is challenging, and usually requires some simplifying hypothesis to be made. By transforming the high dimensional space into a high dimensional grid, the number of cells in each sub-space of the grid is characteristic of a given sample. Using a Hilbert curve each sample can be visualized as a simple density plot, and the distance between samples can be calculated from the distribution of cells using the Jensen-Shannon distance. Bins that correspond to significant differences between samples can identified using a simple bootstrap procedure.

Version:	0.4.3
Imports:	Rcpp, entropy
LinkingTo:	Rcpp
Suggests:	knitr, rmarkdown, ggplot2, dplyr, tidyr, reshape2, bodenmiller, abind
Published:	2019-11-11
DOI:	10.32614/CRAN.package.hilbertSimilarity
Author:	Yann Abraham [aut, cre], Marilisa Neri [aut], John Skilling [ctb]
Maintainer:	Yann Abraham <yann.abraham at gmail.com>
BugReports:	http://github.com/yannabraham/hilbertSimilarity/issues
License:	CC BY-NC-SA 4.0
URL:	http://github.com/yannabraham/hilbertSimilarity
NeedsCompilation:	yes
Materials:	README
CRAN checks:	hilbertSimilarity results

Documentation:

Reference manual:	hilbertSimilarity.pdf
Vignettes:	Comparing Samples using hilbertSimilarity Identifying Treatment effects using hilbertSimilarity

Downloads:

Package source:	hilbertSimilarity_0.4.3.tar.gz
Windows binaries:	r-devel: hilbertSimilarity_0.4.3.zip, r-release: hilbertSimilarity_0.4.3.zip, r-oldrel: hilbertSimilarity_0.4.3.zip
macOS binaries:	r-release (arm64): hilbertSimilarity_0.4.3.tgz, r-oldrel (arm64): hilbertSimilarity_0.4.3.tgz, r-release (x86_64): hilbertSimilarity_0.4.3.tgz, r-oldrel (x86_64): hilbertSimilarity_0.4.3.tgz
Old sources:	hilbertSimilarity archive

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