hilbertSimilarity (0.4.3)

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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.

Maintainer: Yann Abraham
Author(s): Yann Abraham [aut, cre], Marilisa Neri [aut], John Skilling [ctb]

License: CC BY-NC-SA 4.0

Uses: entropy, Rcpp, abind, ggplot2, reshape2, knitr, dplyr, tidyr, rmarkdown, bodenmiller

Released 4 months ago.

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