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.
|Suggests:||knitr, rmarkdown, ggplot2, dplyr, tidyr, reshape2, bodenmiller, abind|
|Author:||Yann Abraham [aut, cre], Marilisa Neri [aut], John Skilling [ctb]|
|Maintainer:||Yann Abraham <yann.abraham at gmail.com>|
|License:||CC BY-NC-SA 4.0|
|CRAN checks:||hilbertSimilarity results|
Comparing Samples using hilbertSimilarity
Identifying Treatment effects using hilbertSimilarity
|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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