Results
wolfemd
2020-Sep-21
Last updated: 2020-11-27
Checks: 7 0
Knit directory: IITA_2020GS/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 1f8cd99 | wolfemd | 2020-11-27 | Added plots of genetic gain for 4 traits. Initial analysis of GEBV vs. |
| html | d72a9ed | wolfemd | 2020-09-21 | Build site. |
| html | 9194239 | wolfemd | 2020-09-21 | Build site. |
| Rmd | 97778e7 | wolfemd | 2020-09-21 | Big update. Two types of pipeline to get BLUPs, GEBVs and GETGVs: |
Cross-validation accuracy
Conducted 5-fold x 5-reps of cross-validation (here). Three traits only, MCMDS, logFYLD, DM.
library(tidyverse)
library(magrittr)
cvresults <- readRDS(here::here("output", "cvresults_ModelA_chunk1.rds")) %>% bind_rows(readRDS(here::here("output",
"cvresults_ModelA_chunk2.rds"))) %>% bind_rows(readRDS(here::here("output", "cvresults_ModelA_chunk3.rds")))
cvresults %>% select(Trait, repeats, id, VersionOfBLUPs, accGEBV) %>% ggplot(., aes(x = Trait,
y = accGEBV, fill = VersionOfBLUPs)) + geom_boxplot() + theme_bw() + scale_fill_viridis_d() +
labs(title = "Compare 3-stage and 2-stage prediction pipelines", subtitle = "Model: Additive-only")
| Version | Author | Date |
|---|---|---|
| 9194239 | wolfemd | 2020-09-21 |
cvresults %>% select(Trait, repeats, id, VersionOfBLUPs, accGEBV) %>% spread(VersionOfBLUPs,
accGEBV) %>% mutate(diffAcc = blups2stage - blups3stage) %>% ggplot(., aes(x = Trait,
y = diffAcc, fill = Trait)) + geom_hline(yintercept = 0, color = "darkred") +
geom_boxplot() + theme_bw() + scale_fill_viridis_d() + labs(y = "Accuracy Difference (2-stage minus 3-stage)",
subtitle = "Model: Additive-only")
| Version | Author | Date |
|---|---|---|
| 9194239 | wolfemd | 2020-09-21 |
Genetic Gain
library(tidyverse)
library(magrittr)
iita_gebvs <- read.csv(here::here("output", "GEBV_IITA_ModelA_twostage_IITA_2020Sep21.csv"),
stringsAsFactors = F)
traits <- c("DM", "logFYLD", "logTOPYLD", "MCMDS")
iita_gebvs %>% select(GID, GeneticGroup, any_of(traits)) %>% pivot_longer(cols = any_of(traits),
names_to = "Trait", values_to = "GEBV") %>% group_by(Trait, GeneticGroup) %>%
summarize(meanGEBV = mean(GEBV), stdErr = sd(GEBV)/sqrt(n()), upperSE = meanGEBV +
stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = GeneticGroup,
y = meanGEBV, fill = Trait)) + geom_bar(stat = "identity", color = "gray60",
size = 1.25) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE), color = "gray60",
size = 1.25) + facet_wrap(~Trait, scales = "free_y", ncol = 1) + theme_bw() +
geom_hline(yintercept = 0, size = 1.15, color = "black") + theme(axis.text.x = element_text(face = "bold",
angle = 0, size = 12), axis.title.y = element_text(face = "bold", size = 14),
legend.position = "none", strip.background.x = element_blank(), strip.text = element_text(face = "bold",
size = 14)) + scale_fill_viridis_d() + labs(x = NULL, y = "Mean GEBVs")
Rate of gain
library(tidyverse)
library(magrittr)
iita_gebvs <- read.csv(here::here("output", "GEBV_IITA_ModelA_twostage_IITA_2020Sep21.csv"),
stringsAsFactors = F)
traits <- c("DM", "logFYLD", "logTOPYLD", "MCMDS")
ggcycletime <- readxl::read_xlsx(here::here("data", "pedigreeGeneticGainCycleTime.xlsx"))
table(ggcycletime$Accession %in% iita_gebvs$GID)
FALSE
807 Need germplasmName field from raw trial data to match GEBV and cycle time
library(tidyverse)
library(magrittr)
dbdata <- readRDS(here::here("output", "IITA_ExptDesignsDetected.rds"))
iita_gebvs %<>% left_join(dbdata %>% select(-MaxNOHAV) %>% unnest(TrialData) %>%
distinct(germplasmName, GID)) %>% group_by(GID) %>% slice(1) %>% ungroup()
table(ggcycletime$Accession %in% iita_gebvs$germplasmName)
FALSE TRUE
193 614 iita_gebvs %<>% left_join(., ggcycletime %>% rename(germplasmName = Accession))iita_gebvs %<>% mutate(Year_Accession = case_when(grepl("2013_|TMS13", germplasmName) ~
2013, grepl("TMS14", germplasmName) ~ 2014, grepl("TMS15", germplasmName) ~ 2015,
grepl("TMS18", germplasmName) ~ 2018, !grepl("2013_|TMS13|TMS14|TMS15|TMS18",
germplasmName) ~ Year_Accession))iita_gebvs %>%
select(germplasmName,GeneticGroup,Year_Accession,any_of(traits)) %>%
pivot_longer(cols=any_of(traits),names_to = "Trait",values_to = "GEBV") %>%
ggplot(.,aes(x=Year_Accession,y=GEBV,color=GeneticGroup)) +
geom_point() +
facet_wrap(~Trait,scales='free_y', ncol=1) +
theme_bw() +
# geom_hline(yintercept = 0, size=1.15, color='black') +
theme(axis.text.x = element_text(face = 'bold',angle = 0, size=12),
axis.title.y = element_text(face = 'bold',size=14),
#legend.position = 'none',
strip.background.x = element_blank(),
strip.text = element_text(face='bold',size=14)) +
scale_color_viridis_d()
# labs(x=NULL,y="Mean GEBVs")iita_gebvs %>% select(germplasmName, GeneticGroup, Year_Accession, any_of(traits)) %>%
mutate(GeneticGroup = ifelse(Year_Accession >= 2013, "GS", "PreGS")) %>% pivot_longer(cols = any_of(traits),
names_to = "Trait", values_to = "GEBV") %>% group_by(Trait, GeneticGroup, Year_Accession) %>%
summarize(meanGEBV = mean(GEBV), Nclones = n(), stdErr = sd(GEBV)/sqrt(n()),
upperSE = meanGEBV + stdErr, lowerSE = meanGEBV - stdErr) %>% ggplot(., aes(x = Year_Accession,
y = meanGEBV, color = GeneticGroup, size = Nclones)) + geom_point(size = 4) +
geom_smooth(method = lm, se = TRUE) + geom_linerange(aes(ymax = upperSE, ymin = lowerSE),
color = "gray40", size = 1) + facet_wrap(~Trait, scales = "free_y", ncol = 1) +
theme_bw() + theme(axis.text = element_text(face = "bold", angle = 0, size = 14),
axis.title = element_text(face = "bold", size = 16), strip.background.x = element_blank(),
strip.text = element_text(face = "bold", size = 18)) + scale_color_viridis_d()
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] Rcpp_1.0.5 lattice_0.20-41 lubridate_1.7.9.2 here_1.0.0
[5] assertthat_0.2.1 rprojroot_2.0.2 digest_0.6.27 R6_2.5.0
[9] cellranger_1.1.0 backports_1.2.0 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.2 pillar_1.4.6 rlang_0.4.8 readxl_1.3.1
[17] rstudioapi_0.13 whisker_0.4 Matrix_1.2-18 rmarkdown_2.5
[21] labeling_0.4.2 splines_4.0.2 munsell_0.5.0 broom_0.7.2
[25] compiler_4.0.2 httpuv_1.5.4 modelr_0.1.8 xfun_0.19
[29] pkgconfig_2.0.3 mgcv_1.8-33 htmltools_0.5.0 tidyselect_1.1.0
[33] fansi_0.4.1 viridisLite_0.3.0 crayon_1.3.4 dbplyr_2.0.0
[37] withr_2.3.0 later_1.1.0.1 grid_4.0.2 nlme_3.1-150
[41] jsonlite_1.7.1 gtable_0.3.0 lifecycle_0.2.0 DBI_1.1.0
[45] git2r_0.27.1 formatR_1.7 scales_1.1.1 cli_2.1.0
[49] stringi_1.5.3 farver_2.0.3 fs_1.5.0 promises_1.1.1
[53] xml2_1.3.2 ellipsis_0.3.1 generics_0.1.0 vctrs_0.3.5
[57] tools_4.0.2 glue_1.4.2 hms_0.5.3 yaml_2.2.1
[61] colorspace_2.0-0 rvest_0.3.6 knitr_1.30 haven_2.3.1