Results
2020-October-15
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/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 | 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")
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:
- RefPanel (containing TARI TP):
output/rownames_DosageMatrix_ImputationReferencePanel_StageVI_91119.csv - 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")
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")
# Prediction accuracy.
- 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 1285983 68.7 3051641 163.0 NA 2125557 113.6
Vcells 2430943 18.6 10146329 77.5 102400 10141707 77.4library(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
cv %>% group_by(Trait) %>% # 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(desc(meanAccuracy)) %>%
rmarkdown::paged_table()Boxplot of accuracies
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')
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