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
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

Downloads:

Reference manual: hilbertSimilarity.pdf
Vignettes: Comparing Samples using hilbertSimilarity
Identifying Treatment effects using hilbertSimilarity
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
OS X binaries: r-release: hilbertSimilarity_0.4.3.tgz, r-oldrel: hilbertSimilarity_0.4.3.tgz
Old sources: hilbertSimilarity archive

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