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 | 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. |
Summary of the number of unique plots, locations, years, etc. in the cleaned plot-basis data. See here for details..
library(tidyverse)
library(magrittr)
<- readRDS(file = here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))
rawdata %>% summarise(Nplots = nrow(.), across(c(locationName, studyYear, studyName,
rawdata ~length(unique(.)), .names = "N_{.col}")) %>% rmarkdown::paged_table() TrialType, GID),
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.
%>% count(TrialType, CompleteBlocks, IncompleteBlocks) %>% spread(TrialType,
rawdata %>% rmarkdown::paged_table() n)
Next, look at breakdown of plots by TrialType (rows) and locations (columns):
%>% count(locationName, TrialType) %>% spread(locationName, n) %>% rmarkdown::paged_table() rawdata
<- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD",
traits "logFYLD", "logTOPYLD", "logRTNO")
%>% select(locationName, studyYear, studyName, TrialType, any_of(traits)) %>%
rawdata 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?
%>% select(locationName, studyYear, studyName, TrialType, germplasmName,
rawdata any_of(traits)) %>% pivot_longer(cols = any_of(traits),
FullSampleName, GID, 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.
%>% select(locationName, studyYear, studyName, TrialType, germplasmName,
rawdata any_of(traits)) %>% # pivot_longer(cols = any_of(traits), values_to = 'Value', names_to = 'Trait')
FullSampleName, GID, # %>% 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:
output/rownames_DosageMatrix_ImputationReferencePanel_StageVI_91119.csv
output/rownames_DosageMatrix_DCas20_5629_EA_REFimputedAndFiltered.csv
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"))
<- readRDS(here::here("output", "TARI_ExptDesignsDetected_2020Dec18.rds"))
dbdata <- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD",
traits "logFYLD", "logTOPYLD", "logRTNO")
<- readRDS(file = here::here("output", "tari_blupsForModelTraining_twostage_asreml_2020Dec20.rds"))
blups
%>% left_join(nestDesignsDetectedByTraits(dbdata, traits) %>% mutate(Nplots = map_dbl(MultiTrialTraitData,
blups %>% select(Trait, Nplots)) %>% mutate(Nclones = map_dbl(blups, ~nrow(.)),
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()
%>% select(Trait, blups) %>% unnest(blups) %>% ggplot(., aes(x = drgBLUP, fill = Trait)) +
blups 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 |
%>% select(Trait, blups) %>% unnest(blups) %>% ggplot(., aes(x = REL, fill = Trait)) +
blups 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 |
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)
<- readRDS(here::here("output", "cvresults_A_2020Dec21.rds")) %>% bind_rows(readRDS(here::here("output",
cv "cvresults_ADE_2020Dec21.rds"))) %>% unnest(CVresults) %>% select(-splits, -accuracy)
<- c("MCMDS", "MCBSDS", "CBSDRS", "CGMS1", "CGMS2", "DM", "PLTHT", "HI", "logDYLD",
traits "logFYLD", "logTOPYLD", "logRTNO")
%<>% mutate(Trait = factor(Trait, levels = traits), modelType = factor(modelType,
cv levels = c("A", "ADE")))
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).
%>% group_by(Trait, modelType) %>% # use accGETGV. For modelA we GETGV==GEBV. For modelADE we don't want GEBV, just
cv # 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()
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