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 |
| Reference manual: | hilbertSimilarity.html , hilbertSimilarity.pdf |
| Vignettes: |
Comparing Samples using hilbertSimilarity (source, R code) Identifying Treatment effects using hilbertSimilarity (source, R code) |
| 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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