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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=hilbertSimilarity to link to this page.
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