Last updated: 2021-06-18
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Knit directory: implementGMSinCassava/
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/05-CrossValidation.Rmd) and HTML (docs/05-CrossValidation.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 | fa20501 | wolfemd | 2021-06-18 | Initial results are ready to publish and share with colleagues for |
| Rmd | 12cc368 | wolfemd | 2021-06-18 | runParentWiseCrossVal for 1 full rep, 5 folds. Found issue with CBSU R compilation but NOT with my code! |
| html | e66bdad | wolfemd | 2021-06-10 | Build site. |
| Rmd | a8452ba | wolfemd | 2021-06-10 | Initial build of the entire page upon completion of all |
| Rmd | 6a5ef32 | wolfemd | 2021-06-09 | meanPredAccuracy() now also included with function moved to “parentWiseCrossVal.R”. NOTE on previous commit: cross-validation functions are NOT in “predCrossVar.R”. |
| Rmd | 63067f7 | wolfemd | 2021-06-07 | Function varPredAccuracy() debugged / tested and moved to predCrossVar.R |
| Rmd | 66c0bde | wolfemd | 2021-06-07 | Remove old and unused code. STILL IN PROGRESS at the computeVarPredAccuracy step. |
| Rmd | 3c085ee | wolfemd | 2021-06-07 | Cross-validation code IN PROGRESS. Currently working on computeVarPredAccuracy. |
In the manuscript, the cross-validation is documented many pages and scripts, documented here.
For ongoing GS, I have a function runCrossVal() that manages all inputs and outputs easy to work with pre-computed accuracies.
Goal here is to make a function: runParentWiseCrossVal(), or at least make progress towards developing one.
However, for computational reasons, I imagine it might still be best to separate the task into a few functions.
My goal is to simplify and integrate into the pipeline used for NextGen Cassava. In the paper, used multi-trait Bayesian ridge-regression (MtBRR) to obtain marker effects, and also stored posterior matrices on disk to later compute posterior mean variances. This was computationally expensive and different from my standard univariate REML approach. I think MtBRR and PMV are probably the least biased way to go… but…
For the sake of testing a simple integration into the in-use pipeline, I want to try univariate REML to get the marker effects, which I’ll subsequently use for the cross-validation.
Revised the functions in package:predCrossVar to increase the computational efficiency. Not yet included into the actual R package but instead sourced from code/predCrossVar.R. Additional speed increases were achieved by extra testing to optimize balance of OMP_NUM_THREADS setting (multi-core BLAS) and parallel processing of the crosses-being-predicted. Improvements will benefit users predicting with REML / Bayesian-VPM, but probably worse for Bayesian-PMV.
Fully-tested runParentWiseCrossVal() and component functions are in the code/parentWiseCrossVal.R script. Below, source it and use it for a full cross-validation run.
cd /home/jj332_cas/marnin/implementGMSinCassava/;
export PATH=/programs/R-4.0.5clean-p/bin:$PATH;
# for a 112 core machine. Use ncores=20 below
export OMP_NUM_THREADS=5;
screen;
R # initiate R sessionrequire(tidyverse); require(magrittr);
# SOURCE CORE FUNCTIONS
source(here::here("code","parentWiseCrossVal.R"))
source(here::here("code","predCrossVar.R"))
# PEDIGREE
ped<-read.table(here::here("output","verified_ped.txt"),
header = T, stringsAsFactors = F) %>%
rename(GID=FullSampleName,
damID=DamID,
sireID=SireID) %>%
dplyr::select(GID,sireID,damID)
# Keep only families with _at least_ 2 offspring
ped %<>%
semi_join(ped %>% count(sireID,damID) %>% filter(n>1) %>% ungroup())
# BLUPs
blups<-readRDS(file=here::here("data","blups_forCrossVal.rds")) %>%
dplyr::select(-varcomp)
# GENOMIC RELATIONSHIP MATRICES (GRMS)
grms<-list(A=readRDS(file=here::here("output","kinship_A_IITA_2021May13.rds")),
D=readRDS(file=here::here("output","kinship_D_IITA_2021May13.rds")))
# DOSAGE MATRIX
dosages<-readRDS(file=here::here("data",
"dosages_IITA_filtered_2021May13.rds"))
# RECOMBINATION FREQUENCY MATRIX
recombFreqMat<-readRDS(file=here::here("data",
"recombFreqMat_1minus2c_2021May13.rds"))
# HAPLOTYPE MATRIX
## keep only haplos for parents-in-the-pedigree
## those which will be used in prediction, saves memory
haploMat<-readRDS(file=here::here("data","haps_IITA_filtered_2021May13.rds"))
parents<-union(ped$sireID,ped$damID)
parenthaps<-sort(c(paste0(parents,"_HapA"),
paste0(parents,"_HapB")))
haploMat<-haploMat[parents,colnames(recombFreqMat)]
# SELECTION INDEX WEIGHTS
## from IYR+IK
## note that not ALL predicted traits are on index
SIwts<-c(logFYLD=20,
HI=10,
DM=15,
MCMDS=-10,
logRTNO=12,
logDYLD=20,
logTOPYLD=15,
PLTHT=10) cvAD_1rep<-runParentWiseCrossVal(nrepeats=1,nfolds=5,seed=53,modelType="AD",
ncores=20,outName="output/cvAD_1rep",
ped=ped,gid="GID",blups=blups,
dosages=dosages,haploMat=haploMat,
grms=grms,recombFreqMat = recombFreqMat,
selInd = TRUE, SIwts = SIwts)
saveRDS(cvAD_1rep,here::here("output","cvAD_1rep_predAccuracy.rds"))cvAD_5rep<-runParentWiseCrossVal(nrepeats=5,nfolds=5,seed=0407,modelType="AD",
ncores=20,outName="output/cvAD_1rep",
ped=ped,gid="GID",blups=blups,
dosages=dosages,haploMat=haploMat,
grms=grms,recombFreqMat = recombFreqMat,
selInd = TRUE, SIwts = SIwts)
saveRDS(cvAD_5rep,here::here("output","cvAD_5rep_predAccuracy.rds"))