hilbertSimilarity
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.
- Version0.4.3
- R versionunknown
- LicenseCC BY-NC-SA 4.0
- Needs compilation?Yes
- Last release11/11/2019
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Team
Yann Abraham
Marilisa Neri
Show author detailsRolesAuthorJohn Skilling
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