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 |

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.pdf |

Vignettes: |
Comparing Samples using hilbertSimilarity Identifying Treatment effects using hilbertSimilarity |

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 |

OS X binaries: | r-release: hilbertSimilarity_0.4.3.tgz, r-oldrel: hilbertSimilarity_0.4.3.tgz |

Old sources: | hilbertSimilarity archive |

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