The Linked Selection Signature of Rapid Adaptation in Temporal Genomic Data

This file contains the code, manuscript, and some of the smaller intermediate files necessary to reproduce the manuscript Buffalo and Coop (2019), The Linked Selection Signature of Rapid Adaptation in Temporal Genomic Data.

Required Software

msprime

We use msprime to generate gametes at mutation-drift-recombination equilibrium. Note that mspms must be in the $PATH of the server the simulations are running on. The following version was used:

$ mspms --version
mspms 0.4.0

Required R Packages

msr
tidyverse
gsl
parallel
rmarkdown
knitr

msr is my MS data parser for R.

Simulation Data on FigShare

For reproducibility purposes, we've uploaded the simulation data to FigShare. FigShare link and DOI: 10.6084/m9.figshare.7709930.

Reproducing the Results

1. The following files were created by the simulation routines in r_sims/ on the server,

   expfit-fluct-covs.Rdata
   expfit-varyl-varyn-covs.Rdata
   ld-res.Rdata
   neutral-covs.Rdata
   sl-covs.Rdata
   

   each by running Rscript --vanilla scriptname.r for the corresponding script.

2. On the server, run make server in analyses/, which generates some processed data from the main simulations, filtering out the N=1000 case, and then summarizing the complete (varying N) dataset on the server (too computationally intensive to run on my laptop). This generates:

   tempautocov/data/pd5f_varyn.rda
   tempautocov/data/predf_varyn.rda
   tempautocov/simdata/expfit-varyl-covs.Rdata
   

3. (If using separate machine for analysis): Copy the files above, and all files in simdata/ over to analysis machine. touch each file to update timestamp so make does not try to regenerate them.

4. Run make laptop in analyses/; this renders all Rmarkdown files into HTML. It also has the side effect of creating all processed data file used for graphics stored in data/.

5. Finally, in analyses/figures, run make. This runs analyses/figures/ms-plot.r (which sources analyses/figures/fluct-plot.r) for publication-quality figures. The final figures are then copied by the Makefile over to manuscript/images.

Session Info

Below is the session info for the server the simulations were run on:

R version 3.5.1 (2018-07-02)
Platform: x86_64-redhat-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods
[8] base

other attached packages:
 [1] TempSigLinkedSel_0.0.0.9000 reshape2_1.4.3
 [3] forcats_0.3.0               stringr_1.3.1
 [5] dplyr_0.7.7                 purrr_0.2.5
 [7] readr_1.1.1                 tidyr_0.8.1
 [9] tibble_1.4.2                ggplot2_3.0.0
[11] tidyverse_1.2.1             msr_0.1.1
[13] devtools_1.13.6

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.18     cellranger_1.1.0 plyr_1.8.4       pillar_1.3.0
 [5] compiler_3.5.1   bindr_0.1.1      tools_3.5.1      digest_0.6.17
 [9] lubridate_1.7.4  jsonlite_1.5     memoise_1.1.0    nlme_3.1-137
[13] gtable_0.2.0     lattice_0.20-35  pkgconfig_2.0.2  rlang_0.2.2
[17] cli_1.0.0        rstudioapi_0.7   commonmark_1.5   haven_1.1.2
[21] bindrcpp_0.2.2   httr_1.3.1       withr_2.1.2      roxygen2_6.1.0
[25] xml2_1.2.0       hms_0.4.2        grid_3.5.1       tidyselect_0.2.5
[29] glue_1.3.0       R6_2.2.2         readxl_1.1.0     modelr_0.1.2
[33] magrittr_1.5     backports_1.1.2  scales_1.0.0     rvest_0.3.2
[37] assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4    gsl_1.9-10.3
[41] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.0      crayon_1.3.4