Curate by trait-trial
wolfemd
2020-Sep-16
Last updated: 2020-12-03
Checks: 7 0
Knit directory: IITA_2020GS/
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Previous step
- Prepare a training dataset: Download data from DB, “Clean” and format DB data
Nest by trial
Start with cleaned data from previous step.
rm(list = ls())
library(tidyverse)
library(magrittr)
dbdata <- readRDS(here::here("output", "IITA_CleanedTrialData.rds"))All downstream analyses in this step will by on a per-trial (location-year-studyName combination).
The nestByTrials() function converts a data.frame where each row is a plot to one where each row is a trial, with a list-type column TrialData containing the corresponding trial’s plot-data.
source(here::here("code", "gsFunctions.R"))
dbdata <- nestByTrials(dbdata)dbdata %>% head %>% rmarkdown::paged_table()dbdata$TrialData[[1]] %>% slice(1:20) %>% rmarkdown::paged_table()Detect experimental designs
The next step is to check the experimental design of each trial. If you are absolutely certain of the usage of the design variables in your dataset, you might not need this step.
Examples of reasons to do the step below:
- Some trials appear to be complete blocked designs and the blockNumber is used instead of replicate, which is what most use.
- Some complete block designs have nested, incomplete sub-blocks, others simply copy the “replicate” variable into the “blockNumber variable”
- Some trials have only incomplete blocks but the incomplete block info might be in the replicate and/or the blockNumber column
One reason it might be important to get this right is that the variance among complete blocks might not be the same among incomplete blocks. If we treat a mixture of complete and incomplete blocks as part of the same random-effect (replicated-within-trial), we assume they have the same variance.
Also error variances might be heterogeneous among different trial-types (blocking scheme available) and/or plot sizes (maxNOHAV).
Detect designs
dbdata <- detectExptDesigns(dbdata)dbdata %>% count(programName, CompleteBlocks, IncompleteBlocks) %>% rmarkdown::paged_table()Output file
saveRDS(dbdata, file = here::here("output", "IITA_ExptDesignsDetected.rds"))Model by trait-trial
NOTICE: Doing the next step on a server, too many traits and trials for laptop.
The next step fits models to each trial (for each trait)
rm(list = ls())
library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
dbdata <- readRDS(here::here("output", "IITA_ExptDesignsDetected.rds"))
traits <- c("MCMDS", "DM", "PLTHT", "BRNHT1", "BRLVLS", "HI", "logFYLD", "logTOPYLD",
"logRTNO", "TCHART", "LCHROMO", "ACHROMO", "BCHROMO")
# Nest by trait-trial. This next function will structure input trial data by
# trait. This will facilitate looping downstream analyses over each trait for
# each trial.
dbdata <- nestTrialsByTrait(dbdata, traits)dbdata %>% head %>% rmarkdown::paged_table()dbdata$TraitByTrialData[[1]] %>% head %>% rmarkdown::paged_table()Fit models
dbdata <- curateTrialsByTrait(dbdata, traits, ncores = 20)Output file
saveRDS(dbdata, file = here::here("output", "IITA_CuratedTrials.rds"))Plot Results
library(tidyverse); library(magrittr); #library(plotly)
dbdata<-readRDS(file=here::here("output","IITA_CuratedTrials.rds"))
traits<-c("MCMDS","DM","PLTHT","BRNHT1","BRLVLS","HI","logFYLD","logTOPYLD","logRTNO","TCHART","LCHROMO","ACHROMO","BCHROMO")
dbdata %<>%
mutate(Trait=factor(Trait,levels=traits),
TrialType=factor(TrialType,levels=c("CrossingBlock","GeneticGain","CET","ExpCET","PYT","AYT","UYT","NCRP"))) Heritabilities overall
dbdata %>% ggplot(., aes(x = Trait, y = H2, fill = Trait)) + geom_boxplot(color = "darkgray") +
theme_bw() + scale_fill_viridis_d(option = "magma") + theme(axis.text.x = element_text(face = "bold",
angle = 90), axis.title = element_text(face = "bold", size = 12), plot.title = element_text(face = "bold",
size = 14), legend.position = "none") + labs(x = NULL, y = expression("H"^"2"),
title = "Broad-sense Heritabilities across trials")
Residual variances, by TrialType and Trait
dbdata %>% select(studyYear:VarComps) %>% unnest(VarComps) %>% ggplot(., aes(x = TrialType,
y = Residual, fill = TrialType)) + geom_boxplot(color = "darkgray") + theme_bw() +
facet_wrap(~Trait, scales = "free", nrow = 3) + scale_fill_viridis_d(option = "inferno") +
theme(axis.text.x = element_text(angle = 90, face = "bold"), legend.position = "none")
H2 by trait and trialtype.
dbdata %>% select(studyYear:VarComps) %>% unnest(VarComps) %>% ggplot(., aes(x = TrialType,
y = H2, fill = TrialType)) + geom_boxplot(color = "darkgray") + theme_bw() +
facet_wrap(~Trait, scales = "free", nrow = 2) + scale_fill_viridis_d(option = "inferno") +
theme(axis.text.x = element_text(angle = 90, face = "bold"), legend.position = "none")
Number of outliers detected and removed by trait-trialType.
dbdata %>% ggplot(., aes(x = TrialType, y = Noutliers, fill = TrialType)) + geom_boxplot(color = "darkgray") +
theme_bw() + facet_wrap(~Trait, scales = "free", nrow = 4) + scale_fill_viridis_d(option = "inferno") +
theme(axis.text.x = element_text(angle = 90, face = "bold"), legend.position = "none")
Missingness
dbdata %>% ggplot(., aes(x = TrialType, y = propMiss, fill = TrialType)) + geom_boxplot(color = "darkgray") +
theme_bw() + facet_wrap(~Trait, scales = "free", nrow = 3) + scale_fill_viridis_d(option = "inferno") +
theme(axis.text.x = element_text(angle = 90, face = "bold"), legend.position = "none")
Next step
- Get BLUPs combining all trial data: Combine data from all trait-trials to get BLUPs for downstream genomic
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 viridisLite_0.3.0 htmltools_0.5.0
[9] yaml_2.2.1 rlang_0.4.9 later_1.1.0.1 pillar_1.4.7
[13] withr_2.3.0 glue_1.4.2 DBI_1.1.0 dbplyr_2.0.0
[17] modelr_0.1.8 readxl_1.3.1 lifecycle_0.2.0 munsell_0.5.0
[21] gtable_0.3.0 cellranger_1.1.0 rvest_0.3.6 evaluate_0.14
[25] labeling_0.4.2 knitr_1.30 ps_1.4.0 httpuv_1.5.4
[29] fansi_0.4.1 broom_0.7.2 Rcpp_1.0.5 promises_1.1.1
[33] backports_1.2.0 scales_1.1.1 formatR_1.7 jsonlite_1.7.1
[37] farver_2.0.3 fs_1.5.0 hms_0.5.3 digest_0.6.27
[41] stringi_1.5.3 rprojroot_2.0.2 grid_4.0.2 here_1.0.0
[45] cli_2.2.0 tools_4.0.2 crayon_1.3.4 whisker_0.4
[49] pkgconfig_2.0.3 ellipsis_0.3.1 xml2_1.3.2 reprex_0.3.0
[53] lubridate_1.7.9.2 rstudioapi_0.13 assertthat_0.2.1 rmarkdown_2.5
[57] httr_1.4.2 R6_2.5.0 git2r_0.27.1 compiler_4.0.2