Last updated: 2021-05-05
Checks: 7 0
Knit directory: NRCRI_2021GS/
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Untracked: output/genomicPredictions_ModelADE_twostage_NRCRI_2021May03.rds
Untracked: output/genomicPredictions_ModelA_twostage_NRCRI_2021May03.rds
Untracked: output/genomicPredictions_NRCRI_2021May03.csv
Untracked: output/maxNOHAV_byStudy.csv
Unstaged changes:
Modified: README.md
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 | 94ec811 | wolfemd | 2021-05-05 | Completed prediction. Build site and push to GitHub / Cassavabase FTP server. Share to NRCRI. |
Current Step
cd /home/jj332_cas/marnin/NRCRI_2021GS/;
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_NRCRI_2021May03.rds"))
# BLUPs from the 2 stage procedure
# (stage 1 of 2)
blups<-readRDS(file=here::here("output","NRCRI_blupsForModelTraining_twostage_asreml_2021May03.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_NRCRI_2021May03.rds"))Model ADE
D<-readRDS(file=here::here("output","Kinship_D_NRCRI_2021May03.rds"))
AD<-readRDS(file=here::here("output","Kinship_AD_NRCRI_2021May03.rds"))
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_NRCRI_2021May03.rds"))rm(list=ls()); gc() used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 728874 39.0 1331574 71.2 NA 1331574 71.2
Vcells 1338811 10.3 8388608 64.0 65536 2225076 17.0library(tidyverse); library(magrittr);
predModelA<-readRDS(file = here::here("output","genomicPredictions_ModelA_twostage_NRCRI_2021May03.rds"))
predModelADE<-readRDS(file = here::here("output","genomicPredictions_ModelADE_twostage_NRCRI_2021May03.rds"))
traits<-c("CGM","CGMS1","CGMS2","MCMDS",
"DM","DMsg","PLTHT","BRNHT1","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)]
c3b<-unique_gids %>%
grep("NR20C3",.,value = T,ignore.case = T) %>%
.[!. %in% c(c1a,c1b,c2a,c2b,c3a)]
nrTP<-setdiff(unique_gids,unique(c(c1a,c1b,c2a,c2b,c3a,c3b)))## Format and write GEBV
predModelA %>%
select(Trait,genomicPredOut) %>%
unnest(genomicPredOut) %>%
select(-varcomps) %>%
unnest(gblups) %>%
select(-GETGV,-contains("PEV")) %>%
spread(Trait,GEBV) %>%
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",
GID %in% c3b ~ "C3b")) %>%
select(Group,GID,any_of(traits)) %>%
arrange(desc(Group)) %>%
write.csv(., file = here::here("output","GEBV_NRCRI_ModelA_2021May03.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=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",
GID %in% c3b ~ "C3b")) %>%
select(Group,GID,any_of(traits)) %>%
arrange(desc(Group)) %>%
write.csv(., file = here::here("output","GETGV_NRCRI_ModelADE_2021May03.csv"), row.names = F)
### Make a unified "tidy" long-form:
predModelA %>%
select(Trait,genomicPredOut) %>%
unnest(genomicPredOut) %>%
select(-varcomps) %>%
unnest(gblups) %>%
select(-GETGV) %>%
full_join(predModelADE %>%
select(Trait,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",
GID %in% c3b ~ "C3b")) %>%
relocate(Group,.before = GID) %>%
write.csv(., file = here::here("output","genomicPredictions_NRCRI_2021May03.csv"), row.names = F)See Results: Home for plots and summary tables.
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
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_0.5.1 stringr_1.4.0 dplyr_1.0.5
[5] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.1
[9] ggplot2_3.3.3 tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.22 bslib_0.2.4 haven_2.4.0
[5] colorspace_2.0-0 vctrs_0.3.7 generics_0.1.0 htmltools_0.5.1.1
[9] yaml_2.2.1 utf8_1.2.1 rlang_0.4.10 jquerylib_0.1.3
[13] later_1.1.0.1 pillar_1.6.0 withr_2.4.2 glue_1.4.2
[17] DBI_1.1.1 dbplyr_2.1.1 readxl_1.3.1 modelr_0.1.8
[21] lifecycle_1.0.0 cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0
[25] rvest_1.0.0 evaluate_0.14 knitr_1.32 httpuv_1.5.5
[29] fansi_0.4.2 broom_0.7.6 Rcpp_1.0.6 promises_1.2.0.1
[33] backports_1.2.1 scales_1.1.1 jsonlite_1.7.2 fs_1.5.0
[37] hms_1.0.0 digest_0.6.27 stringi_1.5.3 rprojroot_2.0.2
[41] grid_4.0.3 here_1.0.1 cli_2.4.0 tools_4.0.3
[45] sass_0.3.1 crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3
[49] ellipsis_0.3.1 xml2_1.3.2 reprex_2.0.0 lubridate_1.7.10
[53] rstudioapi_0.13 assertthat_0.2.1 rmarkdown_2.7 httr_1.4.2
[57] R6_2.5.0 git2r_0.28.0 compiler_4.0.3