Last updated: 2020-12-23

Checks: 7 0

Knit directory: TARI_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.

R Markdown file: up-to-date

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.

Environment: empty

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.

Seed: set.seed(20201215)

The command set.seed(20201215) 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.

Session information: recorded

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Cache: none

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File paths: relative

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Repository version: 6a50ab2

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 6a50ab2. 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: 


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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/05-Results.Rmd) and HTML (docs/05-Results.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 6a50ab2 wolfemd 2020-12-23 Tweak one heading…
html abaf52a wolfemd 2020-12-23 Build site.
Rmd fae176a wolfemd 2020-12-23 Publish the first set of analyses and files for TARI 2020 GS.

Raw data

Summary of the number of unique plots, locations, years, etc. in the cleaned plot-basis data. See here for details..

library(tidyverse)
library(magrittr)
rawdata <- readRDS(file = here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))
rawdata %>% summarise(Nplots = nrow(.), across(c(locationName, studyYear, studyName, 
    TrialType, GID), ~length(unique(.)), .names = "N_{.col}")) %>% rmarkdown::paged_table()

So ~3700 unique clone names in the phenotype data, across >12K plots. This is not the same number of clones as are expected to be genotyped-and-phenotyped.

Break down the plots based on the trial design and TrialType (really a grouping of the population that is breeding program specific), captured by two logical variables, CompleteBlocks and IncompleteBlocks.

rawdata %>% count(TrialType, CompleteBlocks, IncompleteBlocks) %>% spread(TrialType, 
    n) %>% rmarkdown::paged_table()

Next, look at breakdown of plots by TrialType (rows) and locations (columns):

rawdata %>% count(locationName, TrialType) %>% spread(locationName, n) %>% rmarkdown::paged_table()
traits <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD", 
    "logFYLD", "logTOPYLD", "logRTNO")
rawdata %>% select(locationName, studyYear, studyName, TrialType, any_of(traits)) %>% 
    pivot_longer(cols = any_of(traits), values_to = "Value", names_to = "Trait") %>% 
    ggplot(., aes(x = Value, fill = Trait)) + geom_histogram() + facet_wrap(~Trait, 
    scales = "free") + theme_bw() + scale_fill_viridis_d() + labs(title = "Distribution of Raw Phenotypic Values")

Version Author Date
abaf52a wolfemd 2020-12-23

How many genotyped-and-phenotyped clones?

rawdata %>% select(locationName, studyYear, studyName, TrialType, germplasmName, 
    FullSampleName, GID, any_of(traits)) %>% pivot_longer(cols = any_of(traits), 
    values_to = "Value", names_to = "Trait") %>% filter(!is.na(Value), !is.na(FullSampleName)) %>% 
    distinct(germplasmName, FullSampleName, GID) %>% rmarkdown::paged_table()

There are 423 so far.

rawdata %>% select(locationName, studyYear, studyName, TrialType, germplasmName, 
    FullSampleName, GID, any_of(traits)) %>% # pivot_longer(cols = any_of(traits), values_to = 'Value', names_to = 'Trait')
# %>% filter(!is.na(Value)) %>%
distinct(germplasmName, FullSampleName, GID) %>% rmarkdown::paged_table()

Table of germplasmName-DNA-sample-name matches are here: output/germplasmName_to_DNAname_matches_TARI_2020Dec22.csv.

List of DNA-sample-names are here:

  1. RefPanel (containing TARI TP): output/rownames_DosageMatrix_ImputationReferencePanel_StageVI_91119.csv
  2. New samples (DCAs20-5629): output/rownames_DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.csv

BLUPs

These are the BLUPs combining data for each clone across trials/locations without genomic information, used as input for genomic prediction downstream.

library(tidyverse)
library(magrittr)
source(here::here("code", "gsFunctions.R"))
dbdata <- readRDS(here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))
traits <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD", 
    "logFYLD", "logTOPYLD", "logRTNO")
blups <- readRDS(file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.rds"))

blups %>% left_join(nestDesignsDetectedByTraits(dbdata, traits) %>% mutate(Nplots = map_dbl(MultiTrialTraitData, 
    nrow)) %>% select(Trait, Nplots)) %>% mutate(Nclones = map_dbl(blups, ~nrow(.)), 
    NoutliersRemoved = map2_dbl(outliers1, outliers2, ~length(.x) + length(.y))) %>% 
    # relocate(c(Nclones,NoutliersRemoved),.after = Trait) %>%
# select(-blups,-varcomp,-outliers1,-outliers2) %>%
select(Trait, Nplots, Nclones, NoutliersRemoved, Vg, Ve, H2) %>% mutate(across(is.numeric, 
    ~round(., 4))) %>% arrange(desc(H2)) %>% rmarkdown::paged_table()
blups %>% select(Trait, blups) %>% unnest(blups) %>% ggplot(., aes(x = drgBLUP, fill = Trait)) + 
    geom_histogram() + facet_wrap(~Trait, scales = "free") + theme_bw() + scale_fill_viridis_d() + 
    labs(title = "Distribution of de-regressed BLUP Values")

Version Author Date
abaf52a wolfemd 2020-12-23 
blups %>% select(Trait, blups) %>% unnest(blups) %>% ggplot(., aes(x = REL, fill = Trait)) + 
    geom_histogram() + facet_wrap(~Trait, scales = "free") + theme_bw() + scale_fill_viridis_d() + 
    labs(title = "Distribution of BLUP Reliabilities")

Version Author Date
abaf52a wolfemd 2020-12-23

Prediction accuracy

  1. Check prediction accuracy: Evaluate prediction accuracy with cross-validation.
    • Compare prediction accuracy with vs. without IITA’s training data to augment.
rm(list = ls())
gc()
          used (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
Ncells 1286042 68.7    3034042 162.1         NA  2202262 117.7
Vcells 2431465 18.6   10146329  77.5     102400 10142130  77.4
library(tidyverse)
library(magrittr)
cv <- readRDS(here::here("output", "cvresults_A_2020Dec21.rds")) %>% bind_rows(readRDS(here::here("output", 
    "cvresults_ADE_2020Dec21.rds"))) %>% unnest(CVresults) %>% select(-splits, -accuracy)
traits <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD", 
    "logFYLD", "logTOPYLD", "logRTNO")
cv %<>% mutate(Trait = factor(Trait, levels = traits), modelType = factor(modelType, 
    levels = c("A", "ADE")))

Table of mean accuracies

5-fold cross-validation, replicated 10 times.

Mean accuracy and upper/lower 95% interval.

Two prediction models: A (additive-only) and ADE (additive + dominance + additive-by-dominance epistasis).

cv %>% group_by(Trait, modelType) %>% # use accGETGV. For modelA we GETGV==GEBV. For modelADE we don't want GEBV, just
# GETGV.
summarize(meanAccuracy = mean(accGETGV, na.rm = T), lower5pct = quantile(accGETGV, 
    probs = c(0.05), na.rm = T), upper5pct = quantile(accGETGV, probs = c(0.95), 
    na.rm = T)) %>% mutate(across(is.numeric, ~round(., 2))) %>% arrange(modelType, 
    desc(meanAccuracy)) %>% rmarkdown::paged_table()

Boxplot of accuracies

5-fold cross-validation, replicated 10 times.

Two prediction models: A (additive-only) and ADE (additive + dominance + additive-by-dominance epistasis).

cv %>% 
  ggplot(.,aes(x=Trait,y=accGETGV,fill=modelType)) + 
  geom_boxplot(position = "dodge2",color='gray50',size=0.5) + 
  geom_hline(yintercept = 0, color='darkred') + 
#  facet_wrap(~GroupName,nrow=1,scales='free_x') + 
  theme_bw() + 
  theme(strip.text.x = element_text(face='bold', size=12),
        axis.text.y = element_text(face='bold', size=14, angle = 0),
        axis.text.x = element_text(face='bold', size=10, angle = 0),
        axis.title.y = element_text(face='bold', size=12),
        plot.title = element_text(face='bold'),
        legend.position = 'bottom') + 
  scale_fill_viridis_d() + # coord_flip() + 
  labs(title="Prediction Accuracies", y="GEBV or GETGV accuracy",x=NULL) +
  geom_hline(yintercept = 0, color='darkred')

Version Author Date
abaf52a wolfemd 2020-12-23 

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_1.5.0          httpuv_1.5.4     
[29] fansi_0.4.1       broom_0.7.2       Rcpp_1.0.5        promises_1.1.1   
[33] backports_1.2.1   scales_1.1.1      formatR_1.7       jsonlite_1.7.2   
[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_1.0.1       
[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_2.6    
[57] httr_1.4.2        R6_2.5.0          git2r_0.27.1      compiler_4.0.2