Genomic Prediction Analysis - Stage II of II: Cross-validation Round 2
wolfemd
2019-7-29
Last updated: 2019-11-21
Checks: 7 0
Knit directory: IITA_2019GS/
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| Rmd | bfffb51 | wolfemd | 2019-11-21 | Publish the first set of analyses and files for IITA 2019 GS, |
Objective
This time with the outliers-removed BLUPs. Based on results in round 1, did not continue with some of the traits.
Set-up training data
rm(list=ls()); gc()
library(tidyverse); library(magrittr);
blups<-readRDS(file="data/iita_blupsForCrossVal_outliersRemoved_73019.rds")
K<-readRDS(file=paste0("/workdir/IITA_2019GS/Kinship_IITA_TrainingPop_72619.rds"))
blups %<>%
rename(trainingData=blups) %>%
mutate(trainingData=map(trainingData,~filter(.,GID %in% rownames(K))),)
tms13f<-rownames(K) %>% grep("TMS13F|2013_",.,value = T); length(tms13f) # 2395
tms14f<-rownames(K) %>% grep("TMS14F",.,value = T); length(tms14f) # 2171
tms15f<-rownames(K) %>% grep("TMS15F",.,value = T); length(tms15f) # 835
gg<-setdiff(rownames(K),c(tms13f,tms14f,tms15f)); length(gg) # 1228 (not strictly gg)
blups %<>%
mutate(seed_of_seeds=1:n(),
seeds=map(seed_of_seeds,function(seed_of_seeds,reps=5){
set.seed(seed_of_seeds);
outSeeds<-sample(1:1000,size = reps,replace = F);
return(outSeeds) }))
blups %<>%
select(-varcomp); gc()Cross-validation function
# trainingData<-blups$trainingData[[1]]; seeds<-blups$seeds[[1]]; nfolds<-5; reps<-5;
crossValidateFunc<-function(Trait,trainingData,seeds,nfolds=5,reps=5,ncores=50,...){
trntstdata<-trainingData %>%
filter(GID %in% rownames(K))
K1<-K[rownames(K) %in% trntstdata$GID,
rownames(K) %in% trntstdata$GID]
# rm(K,trainingData); gc()
# seed<-seeds[[1]]
# Nfolds=nfolds
makeFolds<-function(Nfolds=nfolds,seed){
genotypes<-rownames(K1)
set.seed(seed)
seed_per_group<-sample(1:10000,size = 4,replace = FALSE)
set.seed(seed_per_group[1])
FoldsThisRep_tms15<-tibble(CLONE=genotypes[genotypes %in% tms15f],
Group="TMS15F") %>%
mutate(Folds=sample(1:Nfolds,nrow(.),replace=T)) %>%
arrange(Folds) %>%
group_by(Group,Folds) %>%
nest(.key = Test)
set.seed(seed_per_group[2])
FoldsThisRep_tms14<-tibble(CLONE=genotypes[genotypes %in% tms14f],
Group="TMS14F") %>%
mutate(Folds=sample(1:Nfolds,nrow(.),replace=T)) %>%
arrange(Folds) %>%
group_by(Group,Folds) %>%
nest(.key = Test)
set.seed(seed_per_group[3])
FoldsThisRep_tms13<-tibble(CLONE=genotypes[genotypes %in% tms13f],
Group="TMS13F") %>%
mutate(Folds=sample(1:Nfolds,nrow(.),replace=T)) %>%
arrange(Folds) %>%
group_by(Group,Folds) %>%
nest(.key = Test)
set.seed(seed_per_group[4])
FoldsThisRep_gg<-tibble(CLONE=genotypes[genotypes %in% gg],
Group="GGetc") %>%
mutate(Folds=sample(1:Nfolds,nrow(.),replace=T)) %>%
arrange(Folds) %>%
group_by(Group,Folds) %>%
nest(.key = Test)
FoldsThisRep<-bind_rows(FoldsThisRep_tms15,FoldsThisRep_tms14) %>%
bind_rows(FoldsThisRep_tms13) %>%
bind_rows(FoldsThisRep_gg) %>%
mutate(Test=map(Test,~.$CLONE),
Train=map(Test,~genotypes[!genotypes %in% .]))
return(FoldsThisRep) }
crossval<-tibble(Rep=1:reps,seed=unlist(seeds)[1:reps]) %>%
mutate(Folds=map2(Rep,seed,~makeFolds(Nfolds=nfolds,seed=.y))) %>%
unnest()
#Test<-crossval$Test[[1]]; Train<-crossval$Train[[1]]
crossValidate<-function(Test,Train){
train<-Train
test<-Test
trainingdata<-trntstdata %>%
filter(GID %in% train) %>%
mutate(GID=factor(GID,levels=rownames(K1)))
require(sommer)
proctime<-proc.time()
fit <- mmer(fixed = drgBLUP ~1,
random = ~vs(GID,Gu=K1),
weights = WT,
data=trainingdata)
proc.time()-proctime
x<-fit$U$`u:GID`$drgBLUP
gebvs<-tibble(GID=names(x),
GEBV=as.numeric(x))
accuracy<-gebvs %>%
filter(GID %in% test) %>%
left_join(
trntstdata %>%
dplyr::select(GID,BLUP) %>%
filter(GID %in% test)) %$%
cor(GEBV,BLUP, use='complete.obs')
return(accuracy)
}
require(furrr)
options(mc.cores=ncores)
plan(multiprocess)
crossval<-crossval %>%
mutate(accuracy=future_map2(Test,Train,~crossValidate(Test=.x,Train=.y)))
saveRDS(crossval,file=paste0("/workdir/IITA_2019GS/CrossVal_73019/",
"CrossVal_",Trait,"_OutliersRemoved_73019.rds"))
rm(list=ls()); gc()
}Run CV on two servers
cbsulm14 (112)
Results
used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
Ncells 605215 32.4 1241060 66.3 NA 919191 49.1
Vcells 1158418 8.9 8388608 64.0 102400 2033475 15.6library(tidyverse); library(magrittr); library(cowplot);
cvNoOutliers<-tibble(Files=list.files("output/CrossVal_73019/")) %>%
mutate(Trait=gsub("CrossVal_","",Files),
Trait=gsub("_OutliersRemoved_73019.rds","",Trait),
Dataset="OutliersRemoved") %>%
mutate(cvResults=map(Files,~readRDS(paste0("output/CrossVal_73019/",.)))) %>%
dplyr::select(-Files)
cvWithOutliers<-tibble(Files=list.files("output/CrossVal_72719/")) %>%
filter(grepl("HistoricalDataIncluded|BRNHT1|PLTHT",Files)) %>%
mutate(Trait=gsub("CrossVal_","",Files),
Trait=gsub("_2013toPresent_72719.rds","",Trait),
Trait=gsub("_HistoricalDataIncluded_72719.rds","",Trait),
Dataset="NoOutlierRemoval") %>%
filter(Trait %in% cvNoOutliers$Trait) %>%
mutate(cvResults=map(Files,~readRDS(paste0("output/CrossVal_72719/",.)))) %>%
dplyr::select(-Files)
cv<-bind_rows(cvNoOutliers,
cvWithOutliers)
cv %<>%
unnest(cols = cvResults) %>%
mutate(Ntrain=map_dbl(Train,length),
Ntest=map_dbl(Test,length)) %>%
select(-Test,-Train) %>%
unnest(cols = accuracy)Figure 1
I did an additional cross-validation, using BLUPs produced after two rounds of model-fitting, followed-by outlier removal. I defined outliers as observations with abs(studentized residuals)>3.3. Overall, the improvement is not consistent or large, but I’d probably trend towards using the data with outliers removed.
By genetic group
library(viridis)
cv %>%
ggplot(.,aes(x=Trait,y=accuracy,fill=Dataset)) +
geom_boxplot() +
facet_grid(.~Group,space='free_x',scale='free_x') +
geom_hline(yintercept = 0,color='darkred',size=1.25) +
theme_bw() +
theme(axis.text.x = element_text(angle=90,face='bold',size=14)) +
scale_fill_viridis_d()
| Version | Author | Date |
|---|---|---|
| a869b9e | wolfemd | 2019-11-21 |
Figure 2
overall
library(viridis)
cv %>%
ggplot(.,aes(x=Trait,y=accuracy,fill=Dataset)) +
geom_boxplot() +
geom_hline(yintercept = 0,color='darkred',size=1.25) +
theme_bw() +
theme(axis.text.x = element_text(angle=90,face='bold',size=14)) +
scale_fill_viridis_d()
| Version | Author | Date |
|---|---|---|
| a869b9e | wolfemd | 2019-11-21 |
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] viridis_0.5.1 viridisLite_0.3.0 cowplot_1.0.0 magrittr_1.5
[5] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.3
[9] readr_1.3.1 tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
[13] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.11 reshape2_1.4.3
[4] haven_2.2.0 lattice_0.20-38 colorspace_1.4-1
[7] vctrs_0.2.0 generics_0.0.2 htmltools_0.4.0
[10] yaml_2.2.0 rlang_0.4.1 later_1.0.0
[13] pillar_1.4.2 withr_2.1.2 glue_1.3.1
[16] modelr_0.1.5 readxl_1.3.1 plyr_1.8.4
[19] lifecycle_0.1.0 munsell_0.5.0 gtable_0.3.0
[22] workflowr_1.5.0.9000 cellranger_1.1.0 rvest_0.3.5
[25] evaluate_0.14 labeling_0.3 knitr_1.26
[28] httpuv_1.5.2 broom_0.5.2 Rcpp_1.0.3
[31] promises_1.1.0 backports_1.1.5 scales_1.1.0
[34] jsonlite_1.6 farver_2.0.1 fs_1.3.1
[37] gridExtra_2.3 hms_0.5.2 digest_0.6.22
[40] stringi_1.4.3 grid_3.6.1 rprojroot_1.3-2
[43] cli_1.1.0 tools_3.6.1 lazyeval_0.2.2
[46] crayon_1.3.4 whisker_0.4 pkgconfig_2.0.3
[49] zeallot_0.1.0 xml2_1.2.2 lubridate_1.7.4
[52] assertthat_0.2.1 rmarkdown_1.17 httr_1.4.1
[55] rstudioapi_0.10 R6_2.4.1 nlme_3.1-142
[58] git2r_0.26.1 compiler_3.6.1