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.


Reference manual

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0.4.3 by Yann Abraham, a year ago


Report a bug at http://github.com/yannabraham/hilbertSimilarity/issues

Browse source code at https://github.com/cran/hilbertSimilarity

Authors: Yann Abraham [aut, cre] , Marilisa Neri [aut] , John Skilling [ctb]

Documentation:   PDF Manual  

CC BY-NC-SA 4.0 license

Imports Rcpp, entropy

Suggests knitr, rmarkdown, ggplot2, dplyr, tidyr, reshape2, bodenmiller, abind

Linking to Rcpp

See at CRAN