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https://github.com/nevrome/mobest
R Package - Spatiotemporal Ancestry Interpolation and Search
https://github.com/nevrome/mobest
adna ancient-dna archaeology r
Last synced: about 2 months ago
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R Package - Spatiotemporal Ancestry Interpolation and Search
- Host: GitHub
- URL: https://github.com/nevrome/mobest
- Owner: nevrome
- License: other
- Created: 2020-03-24T15:39:32.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2024-03-06T14:45:33.000Z (10 months ago)
- Last Synced: 2024-03-06T22:49:15.236Z (10 months ago)
- Topics: adna, ancient-dna, archaeology, r
- Language: R
- Homepage: https://nevrome.de/mobest
- Size: 6.62 MB
- Stars: 5
- Watchers: 4
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
![GitHub R package
version](https://img.shields.io/github/r-package/v/nevrome/mobest)
[![R-CMD-check](https://github.com/nevrome/mobest/actions/workflows/check-release.yaml/badge.svg)](https://github.com/nevrome/mobest/actions/workflows/check-release.yaml)# mobest
This R package provides types and functions for spatiotemporal interpolation of human genetic ancestry components, probabilistic similarity search and the calculation of a derived measure for **mob**ility **est**imation. The workflow in mobest version 1.0.0 was specifically developed to support [this](https://github.com/nevrome/mobest.analysis.2022) research compendium for [this](https://doi.org/10.1073/pnas.2218375120) paper.
**See the documentation at https://nevrome.de/mobest**
- `mobest` assumes you have a set of genetic samples with spatial (two coordinates in a projected reference system) and temporal positions (years BC/AD) for which you calculated a derived, numeric measure of genetic ancestry (e.g. coordinates in a PCA or MDS space).
- `mobest` provides a framework to perform spatiotemporal interpolation using Gaussian process regression (kriging) with the [`laGP`](https://CRAN.R-project.org/package=laGP) package to reconstruct an ancestry field based on the ancestry measure you provided.
- `mobest` finally allows to derive a similarity probability for samples of interest within the interpolated field, which – under certain circumstances – can be interpreted as an origin probability.