https://github.com/pblischak/inbreeding-sfs
SFS-Based Demographic Inference with Inbreeding
https://github.com/pblischak/inbreeding-sfs
Last synced: about 1 month ago
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SFS-Based Demographic Inference with Inbreeding
- Host: GitHub
- URL: https://github.com/pblischak/inbreeding-sfs
- Owner: pblischak
- Created: 2019-10-02T18:06:02.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-12-22T23:51:53.000Z (over 5 years ago)
- Last Synced: 2025-02-17T07:44:48.067Z (4 months ago)
- Language: Forth
- Size: 2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# SFS-Based Demographic Inference with Inbreeding
This repository contains code, data, and results for all simulations and empirical
data analyses conducted in the manuscript *Inferring the Demographic History of
Inbred Species From Genome-Wide SNP Frequency Data*.
**Preprint**:
Blischak, P. D., M. S. Barker, and R. N. Gutenkunst. 2019. Inferring the Demographic History of
Inbred Species From Genome-Wide SNP Frequency Data.
bioRxiv [10.1101/2019.12.20.881474](https://doi.org/10.1101/2019.12.20.881474).
### `sims/`
The `sims/` folder contains code for performing the four main simulation experiments that
were conducted in the paper. Results for these simulations are provided as well.
### `data/`
The `data/` folder contains two example data sets (cabbage and puma), as well as
scripts for fitting demographic models, estimating parameter uncertainties, and plotting
comparisons between the observed and expected SFS.
### `bbc-shiny/`
The `bbc-shiny/` folder contains R code for a small Shiny application to demonstrate the
properties of the beta-binomial convolution that is used to derive the expected SFS with
inbreeding.
#### Setup
These analyses can be recreated using the latest version of ∂a∂i (v2.0.3).
```bash
# 0. Add the Bioconda channel if you don't already have it
conda config --append channels bioconda
# 1. Create a new Python 3 environment for dadi
conda create -n dadi-env python=3
# 2. Activate the new environment
conda activate dadi-env
# 3. Install dadi (and dependencies)
conda install dadi
```
Figures were created either using Python and the built-in plotting functions in ∂a∂i or
using `ggplot2` within the `tidyverse` package in R v3.6.