Last updated: 2020-12-23
Checks: 7 0
Knit directory: TARI_2020GS/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/04-GetGBLUPs.Rmd) and HTML (docs/04-GetGBLUPs.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | fae176a | wolfemd | 2020-12-23 | Publish the first set of analyses and files for TARI 2020 GS. |
Current Step
cd /home/jj332_cas/marnin/TARI_2020GS/;
export OMP_NUM_THREADS=56 # activate multithread OpenBLAS library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
A <- readRDS(file = here::here("output", "Kinship_A_TARI_2020Dec21.rds"))
D <- readRDS(file = here::here("output", "Kinship_D_TARI_2020Dec21.rds"))
AD <- readRDS(file = here::here("output", "Kinship_AD_TARI_2020Dec21.rds"))
# BLUPs from the 2 stage procedure (stage 1 of 2)
blups <- readRDS(file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.rds")) %>%
select(Trait, blups) %>% unnest(blups) %>% select(-`std error`) %>% filter(GID %in%
rownames(A)) %>% nest(TrainingData = -Trait)runGenomicPredictions()
cbsurobbins (112 cores; 512GB)
Model A
options(future.globals.maxSize = 1500 * 1024^2)
predModelA <- runGenomicPredictions(blups, modelType = "A", grms = list(A = A), gid = "GID",
ncores = 14)
saveRDS(predModelA, file = here::here("output", "genomicPredictions_ModelA_twostage_TARI_2020Dec21.rds"))Model ADE
options(future.globals.maxSize = 3000 * 1024^2)
predModelADE <- runGenomicPredictions(blups, modelType = "ADE", grms = list(A = A,
D = D, AD = AD), gid = "GID", ncores = 14)
saveRDS(predModelADE, file = here::here("output", "genomicPredictions_ModelADE_twostage_TARI_2020Dec21.rds"))rm(list = ls())
gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 696057 37.2 1239058 66.2 NA 1239058 66.2
Vcells 1291799 9.9 8388608 64.0 102400 2631872 20.1library(tidyverse)
library(magrittr)
predModelA <- readRDS(file = here::here("output", "genomicPredictions_ModelA_twostage_TARI_2020Dec21.rds"))
predModelADE <- readRDS(file = here::here("output", "genomicPredictions_ModelADE_twostage_TARI_2020Dec21.rds"))
traits <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD",
"logFYLD", "logTOPYLD", "logRTNO")# unique_gids<-predModelA %>% dplyr::select(genomicPredOut) %>%
# unnest(genomicPredOut) %>% select(-varcomps) %>% unnest(gblups) %$% GID %>%
# unique c1a<-unique_gids %>% grep('c1a',.,value = T,ignore.case = T) %>%
# union(.,unique_gids %>% grep('^F',.,value = T,ignore.case = T) %>%
# grep('c1b',.,value = T,ignore.case = T,invert = T)) c1b<-unique_gids%>%
# grep('c1b',.,value = T,ignore.case = T) c2a<-unique_gids %>% grep('C2a',.,value
# = T,ignore.case = T) %>% grep('NR17',.,value = T,ignore.case = T)
# c2b<-unique_gids %>% grep('C2b',.,value = T,ignore.case = T) %>% .[!. %in%
# c(c1a,c1b,c2a)] c3a<-unique_gids %>% grep('C3a',.,value = T,ignore.case = T)
# %>% .[!. %in% c(c1a,c1b,c2a,c2b)]
# nrTP<-setdiff(unique_gids,unique(c(c1a,c1b,c2a,c2b,c3a)))## Format and write GEBV
predModelA %>% select(Trait, genomicPredOut) %>% unnest(genomicPredOut) %>% select(-varcomps) %>%
unnest(gblups) %>% select(-GETGV, -contains("PEV")) %>% spread(Trait, GEBV) %>%
mutate(Group = ifelse(grepl("[...]", GID), "DCas20_5629", "TARI_TP")) %>% select(Group,
GID, any_of(traits)) %>% arrange(desc(Group)) %>% write.csv(., file = here::here("output",
"GEBV_TARI_ModelA_2020Dec21.csv"), row.names = F)
## Format and write GETGV
predModelADE %>% select(Trait, genomicPredOut) %>% unnest(genomicPredOut) %>% select(-varcomps) %>%
unnest(gblups) %>% select(GID, Trait, GETGV) %>% spread(Trait, GETGV) %>% mutate(Group = ifelse(grepl("[...]",
GID), "DCas20_5629", "TARI_TP")) %>% select(Group, GID, any_of(traits)) %>% arrange(desc(Group)) %>%
write.csv(., file = here::here("output", "GETGV_TARI_ModelADE_2020Dec21.csv"),
row.names = F)# ### Make a unified 'tidy' long-form: predModelA %>%
# select(Trait,Dataset,genomicPredOut) %>% unnest(genomicPredOut) %>%
# select(-varcomps) %>% unnest(gblups) %>% select(-GETGV) %>%
# full_join(predModelADE %>% select(Trait,Dataset,genomicPredOut) %>%
# unnest(genomicPredOut) %>% select(-varcomps) %>% unnest(gblups) %>%
# rename(GEBV_modelADE=GEBV, PEV_modelADE=PEVa) %>% select(-genomicPredOut)) %>%
# mutate(Group=case_when(GID %in% nrTP ~ 'nrTP', GID %in% c1a ~ 'C1a', GID %in%
# c1b ~ 'C1b', GID %in% c2a ~ 'C2a', GID %in% c2b ~ 'C2b', GID %in% c3a ~ 'C3a'))
# %>% relocate(Group,.before = GID) %>% write.csv(., file =
# here::here('output','genomicPredictions_TARI_2020Oct15.csv'), row.names = F)See Results: Home for plots and summary tables.
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_2.0.1 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4
[9] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.19 haven_2.3.1 colorspace_2.0-0
[5] vctrs_0.3.5 generics_0.1.0 htmltools_0.5.0 yaml_2.2.1
[9] rlang_0.4.9 later_1.1.0.1 pillar_1.4.7 withr_2.3.0
[13] glue_1.4.2 DBI_1.1.0 dbplyr_2.0.0 modelr_0.1.8
[17] readxl_1.3.1 lifecycle_0.2.0 munsell_0.5.0 gtable_0.3.0
[21] cellranger_1.1.0 rvest_0.3.6 evaluate_0.14 knitr_1.30
[25] ps_1.5.0 httpuv_1.5.4 fansi_0.4.1 broom_0.7.2
[29] Rcpp_1.0.5 promises_1.1.1 backports_1.2.1 scales_1.1.1
[33] formatR_1.7 jsonlite_1.7.2 fs_1.5.0 hms_0.5.3
[37] digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2 grid_4.0.2
[41] here_1.0.1 cli_2.2.0 tools_4.0.2 crayon_1.3.4
[45] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1 xml2_1.3.2
[49] reprex_0.3.0 lubridate_1.7.9.2 rstudioapi_0.13 assertthat_0.2.1
[53] rmarkdown_2.6 httr_1.4.2 R6_2.5.0 git2r_0.27.1
[57] compiler_4.0.2