knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "readme-figs/" )
This is an R-package that makes it easy to compute the BLUE for estimating allele frequency amongst the founders given the pedigree connecting a sample.
It also provides an algorithm for determining optimal sibling removal schemes and it makes it easy to compute effective sample sizes.
You can get the package thus:
devtools::install_github("eriqande/afblue")
Because devtools::install_github
might not recursively install dependencies
you might need or want to also do this:
install.packages(c("dplyr", "kinship2", "magrittr", "stringr", "tidyr"))
If you want to see how the package was used in the paper "Purging putative siblings from population genetic datasets: A cautionary view" by Robin S. Waples and Eric C. Anderson in Molecular Ecology then, after you have done the above line, you should get the whole repository which includes the scripts. Do it like this on your command line. ```{sh, eval=FALSE} git clone https://github.com/eriqande/afblue
Or just get the package from Dryad. Then open up the Rstudio project (`afblue.Rproj`) in that repository. ### Main paper simulations In order to rerun the simulations done in the paper you must run the code in the files `R-Robin/ESS.R` and `R-Robin/Sims.R`. The top part of these files, e.g., the lines: ```r NLoci = 100 Ne = 100 S = 40 NGens = 10 NReps = 200 MaxSib = seq(1:NReps) MaxFamily = 9 Familysize = seq(1:MaxFamily) ProbFamily = 0.5 ## Only used with Mixed mating model Sibcheck = 0 ## 0 removes FS+HS; 1 removes FS only Mating = 1 ## 1 = random; 2 = monogamy; 3 = mixed if (Mating==3) {Familysize = seq(2:MaxFamily)+1}
provide a place for the user to change the simulation parameters as needed or desired.
In order to rerun the analyses on the coho salmon populations you may need to install some more packages if you do not already have them:
install.packages(c("readr", "ggplot2", "grid", "gridExtra", "forcats"))
Then, armed with those packages, you must run the code in
./R-main/coho-afblue-analysis.R
with the working directory being the top level of the
repository/Rstudio project.
Running that script will produce a directory called outputs
and will fill it with the following
output files:
as-if-true-pedigrees.pdf coho_summ_table-related.csv coho_summ_table-unrelated.csv ess_fig_a.pdf ess_fig_b.pdf full_blue_results.csv unrelateds-with-spuriously-inferred-pedigrees.pdf
which are various plots and outputs that appear in the paper.
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