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/03-CrossValidation.Rmd) and HTML (docs/03-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 | fae176a | wolfemd | 2020-12-23 | Publish the first set of analyses and files for TARI 2020 GS. |
5-fold cross-validation. Replicate 5-times.
2 genomic models:
The data for the next step can be found on the cassavabase FTP server here.
Can be loaded directly to R from FTP.
NOTICE: You need enough RAM and a stable network connection. I do the next steps, including cross-validation on a server with plenty of RAM and a good, stable network connection, rather than on my personal computer (a laptop with 16 GB RAM).
The outputs (kinship matrices and filtered snp dosages) of the steps below, which are too large for GitHub, can be found on the cassavabase FTP server here.
# activate multithread OpenBLAS for fast compute of SigmaM (genotypic var-covar matrix)
export OMP_NUM_THREADS=56library(tidyverse)
library(magrittr)
snps_refpanel <- readRDS(here::here("output", "DosageMatrix_ImputationReferencePanel_StageVI_91119.rds"))
snps5629 <- readRDS(here::here("output", "DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.rds"))
snps2keep <- colnames(snps_refpanel) %>% .[. %in% colnames(snps5629)]
snps <- rbind(snps_refpanel[, snps2keep], snps5629[, snps2keep])
gc()
dim(snps) # [1] 18163 37136
# rm(list=(ls() %>% grep('snps',.,value = T, invert = T)))blups <- readRDS(file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.rds"))
blups %<>% select(Trait, blups) %>% unnest(blups) %>% select(-`std error`) %>% filter(GID %in%
rownames(snps))
table(unique(blups$GID) %in% rownames(snps)) # 422samples2Keep <- unique(blups$GID) %>% union(., rownames(snps5629))
length(samples2Keep) # [1] 3570
snps <- snps[samples2Keep, ]
gc()source(here::here("code", "gsFunctions.R"))
snps %<>% maf_filter(., 0.01)
dim(snps) # [1] 3570 36984Going to use my own kinship function.
Make the kinships.
Below e.g. A*A makes a matrix that approximates additive-by-additive epistasis relationships.
A <- kinship(snps, type = "add")
D <- kinship(snps, type = "dom")
AD <- A * D
saveRDS(snps, file = here::here("output", "DosageMatrix_TARI_2020Dec21.rds"))
saveRDS(A, file = here::here("output", "Kinship_A_TARI_2020Dec21.rds"))
saveRDS(D, file = here::here("output", "Kinship_D_TARI_2020Dec21.rds"))
saveRDS(AD, file = here::here("output", "Kinship_AD_TARI_2020Dec21.rds"))
# rm(snps); gc()NOTICE: The outputs (kinship matrices and filtered snp dosages) of the steps below, which are too large for GitHub, can be found on the cassavabase FTP server here.
cd /home/jj332_cas/marnin/TARI_2020GS/;
export OMP_NUM_THREADS=56 # activate multithread OpenBLAS rm(list = ls())
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
blups <- readRDS(file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.rds"))
A <- readRDS(file = here::here("output", "Kinship_A_TARI_2020Dec21.rds"))
blups %<>% select(Trait, blups) %>% unnest(blups) %>% select(-`std error`) %>% filter(GID %in%
rownames(A))
cv2do <- blups %>% nest(TrainTestData = -Trait)
cv2do %>% rmarkdown::paged_table()# # A tibble: 12 x 2 Trait TrainTestData <chr> <list> 1 MCMDS <tibble [422 x 6]>
# 2 MCBSDS <tibble [422 x 6]> 3 CBSDRS <tibble [421 x 6]> 4 CGMS1 <tibble [416 x
# 6]> 5 CGMS2 <tibble [416 x 6]> 6 DM <tibble [421 x 6]> 7 logTOPYLD <tibble [405
# x 6]> 8 logRTNO <tibble [402 x 6]> 9 HI <tibble [26 x 6]> 10 logDYLD <tibble
# [26 x 6]> 11 logFYLD <tibble [26 x 6]> 12 PLTHT <tibble [21 x 6]>cv2do$TrainTestData[[6]] %>% head %>% rmarkdown::paged_table()# GID BLUP PEV REL drgBLUP WT 1 ALBERT:CA8RLANXX:7:526312 0.19175750 0.3671294
# 0.7876987 0.2434402 16.28405 2 CH05_203:250442976 -1.06620285 0.3408428
# 0.8028995 -1.3279406 17.41691 3 COLICANANA:250442941 -1.42969014 0.3672643
# 0.7876207 -1.8152014 16.27851 4 EYOPE:250442952 0.89600706 0.3487736 0.7983134
# 1.1223751 17.06352 5 F10_30_R2:250442916 1.90423411 0.3406459 0.8030134
# 2.3713604 17.42582 6 I000388:250300404 -0.07756348 1.3379835 0.2262792
# -0.3427778 1.70979The function below runCrossVal() function implements nfold cross-validation. Specifically, for each of nrepeats it splits the data into nfolds sets according to gid. So if nfolds=5 then the the clones will be divided into 5 groups and 5 predictions will be made. In each prediction, 4/5 of the clones will be used to predict the remaining 1/5. Accuracy of the model is measured as the correlation between the BLUPs (adj. mean for each CLONE) in the test set and the GEBV (the prediction made of each clone when it was in the test set).
Below, 4 chunks of “5 reps x 5-fold” cross-validation are run on 1 large memory Cornell CBSU machine each (e.g. cbsulm16; 112 cores, 512 GB RAM).
starttime <- proc.time()[3]
cv_A <- cv2do %>% mutate(CVresults = map(TrainTestData, ~runCrossVal(TrainTestData = .,
modelType = "A", grms = list(A = A), byGroup = FALSE, augmentTP = NULL, nrepeats = 10,
nfolds = 5, ncores = 25, gid = "GID")))
runtime <- proc.time()[3] - starttime
runtime
cv_A %<>% mutate(modelType = "A") %>% dplyr::select(-TrainTestData)
saveRDS(cv_A, file = here::here("output", "cvresults_A_2020Dec21.rds"))options(future.globals.maxSize = 3000 * 1024^2)
D <- readRDS(file = here::here("output", "Kinship_D_TARI_2020Dec21.rds"))
AD <- readRDS(file = here::here("output", "Kinship_AD_TARI_2020Dec21.rds"))
starttime <- proc.time()[3]
cv_ADE <- cv2do %>% mutate(CVresults = map(TrainTestData, ~runCrossVal(TrainTestData = .,
modelType = "ADE", grms = list(A = A, D = D, AD = AD), byGroup = FALSE, augmentTP = NULL,
nrepeats = 5, nfolds = 5, ncores = 25, gid = "GID")))
cv_ADE %<>% mutate(modelType = "ADE") %>% dplyr::select(-TrainTestData)
saveRDS(cv_ADE, file = here::here("output", "cvresults_ADE_2020Dec21.rds"))
runtime <- proc.time()[3] - starttime
runtimeSee 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