This repository contains code to reproduce the empirical evaluations of Gerard et al. (2018). The new methods can be found in the updog package on CRAN.
If you are having trouble reproducing these results, it might be that you need to update some of your R packages. These are the versions that I used:
sessionInfo()## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## 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] gridExtra_2.3 ggthemes_4.2.4 rmutil_1.1.9 updog_2.1.3
## [5] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4
## [9] readr_2.1.2 tidyr_1.2.0 tibble_3.1.7 ggplot2_3.3.6
## [13] tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.8.3 lubridate_1.8.0 listenv_0.8.0
## [4] assertthat_0.2.1 digest_0.6.29 foreach_1.5.2
## [7] utf8_1.2.2 parallelly_1.31.1 R6_2.5.1
## [10] cellranger_1.1.0 backports_1.4.1 reprex_2.0.1
## [13] evaluate_0.15 httr_1.4.3 pillar_1.7.0
## [16] rlang_1.0.2 readxl_1.4.0 rstudioapi_0.13
## [19] rmarkdown_2.14 munsell_0.5.0 broom_0.8.0
## [22] compiler_4.2.0 modelr_0.1.8 xfun_0.31
## [25] pkgconfig_2.0.3 globals_0.15.0 htmltools_0.5.2
## [28] tidyselect_1.1.2 codetools_0.2-18 doFuture_0.12.2
## [31] fansi_1.0.3 future_1.26.1 crayon_1.5.1
## [34] tzdb_0.3.0 dbplyr_2.1.1 withr_2.5.0
## [37] grid_4.2.0 jsonlite_1.8.0 gtable_0.3.0
## [40] lifecycle_1.0.1 DBI_1.1.2 magrittr_2.0.3
## [43] scales_1.2.0 cli_3.3.0 stringi_1.7.6
## [46] doRNG_1.8.2 RcppArmadillo_0.11.1.1.0 fs_1.5.2
## [49] xml2_1.3.3 ellipsis_0.3.2 generics_0.1.2
## [52] vctrs_0.4.1 iterators_1.0.14 tools_4.2.0
## [55] glue_1.6.2 rngtools_1.5.2 hms_1.1.1
## [58] fastmap_1.1.0 yaml_2.3.5 colorspace_2.0-3
## [61] rvest_1.0.2 knitr_1.39 haven_2.5.0
I’ve also only tried this out on Ubuntu.
If you find a bug, please create an issue.
To reproduce the results in Gerard et al. (2018), you need to (1)
download and install the appropriate packages, (2) obtain the
appropriate data, (3) run make, and (4) get coffee.
To install the needed R packages, run the following in R
install.packages(c("updog", "tidyverse", "rmutil", "snow", "parallel",
"ggthemes", "gridExtra", "devtools", "fitPoly"))
devtools::install_github("dcgerard/updogAlpha")If the most recent CRAN version of updog doesn’t seem to work, you can
retry with the version that I last used to reproduce these results:
devtools::install_github("dcgerard/updog",
ref = "76c72eb717e18061576fc20b3c07f9da71b67263")Please follow the directions
here
to install ebg.
Place KDRIsweetpotatoXushu18S1LG2017.vcf.gz in the Data folder. The direct url is: https://github.com/dcgerard/KDRIsweetpotatoXushu18S1LG2017/raw/main/KDRIsweetpotatoXushu18S1LG2017.vcf.gz
To reproduce all of the results in Gerard et al. (2018), simply run
make from the terminal. To reproduce the real-data analysis, run
make sweet_potatoTo reproduce the simulations, run
make simulationsThe simulations should take a few hours. You should get some coffee. Here is a list of some of my favorite places:
- Washington, D.C.
- Chicago
- Seattle
- Columbus
The source is available for SuperMASSA, but it isn’t too well documented, so I used the web application version during the empirical data analysis. As such, I’ve committed the inputs and fits in the Output folder. If you want to generate these fits, you’ll have to do it manually.
Gerard, David, Luís Felipe Ventorim Ferrão, Antonio Augusto Franco Garcia, and Matthew Stephens. 2018. “Genotyping Polyploids from Messy Sequencing Data.” Genetics 210 (3): 789–807. https://doi.org/10.1534/genetics.118.301468.
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