Last updated: 2020-12-03

Checks: 7 0

Knit directory: IITA_2020GS/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20200915) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 3cd0f44. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.DS_Store
    Ignored:    data/.DS_Store
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  data/GEBV_IITA_OutliersRemovedTRUE_73119.csv
    Untracked:  data/PedigreeGeneticGainCycleTime_aafolabi_01122020.csv
    Untracked:  data/iita_blupsForCrossVal_outliersRemoved_73019.rds
    Untracked:  output/DosageMatrix_IITA_2020Sep16.rds
    Untracked:  output/IITA_CleanedTrialData_2020Dec03.rds
    Untracked:  output/IITA_ExptDesignsDetected_2020Dec03.rds
    Untracked:  output/Kinship_AA_IITA_2020Sep16.rds
    Untracked:  output/Kinship_AD_IITA_2020Sep16.rds
    Untracked:  output/Kinship_A_IITA_2020Sep16.rds
    Untracked:  output/Kinship_DD_IITA2020Sep16.rds
    Untracked:  output/Kinship_D_IITA_2020Sep16.rds
    Untracked:  output/cvresults_ModelADE_chunk1.rds
    Untracked:  output/cvresults_ModelADE_chunk2.rds
    Untracked:  output/cvresults_ModelADE_chunk3.rds
    Untracked:  output/genomicPredictions_ModelADE_threestage_IITA_2020Sep21.rds
    Untracked:  output/genomicPredictions_ModelADE_twostage_IITA_2020Dec03.rds
    Untracked:  output/genomicPredictions_ModelA_threestage_IITA_2020Sep21.rds
    Untracked:  output/iita_blupsForModelTraining_twostage_asreml_2020Dec03.rds
    Untracked:  workflowr_log.R

Unstaged changes:
    Modified:   output/IITA_ExptDesignsDetected.rds
    Modified:   output/iita_blupsForModelTraining.rds

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/02-curateByTrial.Rmd) and HTML (docs/02-curateByTrial.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
html b9bb6f8 wolfemd 2020-12-03 Build site.
html d72a9ed wolfemd 2020-09-21 Build site.
html d6d72f8 wolfemd 2020-09-17 Build site.
html 7e156dd wolfemd 2020-09-17 Build site.
Rmd 7ea8b80 wolfemd 2020-09-17 All steps including genomic predicting (excluding cross-validation),

Previous step

  1. 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")

Version Author Date
b9bb6f8 wolfemd 2020-12-03
7e156dd wolfemd 2020-09-17

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")

Version Author Date
b9bb6f8 wolfemd 2020-12-03
7e156dd wolfemd 2020-09-17

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")

Version Author Date
b9bb6f8 wolfemd 2020-12-03
7e156dd wolfemd 2020-09-17

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")

Version Author Date
b9bb6f8 wolfemd 2020-12-03
7e156dd wolfemd 2020-09-17

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")

Version Author Date
b9bb6f8 wolfemd 2020-12-03
7e156dd wolfemd 2020-09-17

Next step

  1. 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