HilbertSimilarity estimating sample similarity in single cell high dimensional datasets
| Main Authors: | Abraham, Yann, Neri, Marilisa |
|---|---|
| Format: | info software Journal |
| Bahasa: | eng |
| Terbitan: |
, 2019
|
| Online Access: |
https://zenodo.org/record/3557362 |
Daftar Isi:
- 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.