Last updated: 2023-06-29
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Knit directory: dgrp-starve/
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| /data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/genetic/multibayesC_f_sr.top3.Rds | snake/data/22_cov/genetic/multibayesC_f_sr.top3.Rds |
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| /data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/genetic/multigblup_m_sr.top3.Rds | snake/data/22_cov/genetic/multigblup_m_sr.top3.Rds |
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| /data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/residual/mr.mash_m_sr.top3.Rds | snake/data/22_cov/residual/mr.mash_m_sr.top3.Rds |
| /data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/residual/multigblup_m_sr.top3.Rds | snake/data/22_cov/residual/multigblup_m_sr.top3.Rds |
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Unstaged changes:
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 18a4e42 | nklimko | 2023-06-29 | wflow_publish(“analysis/covCor.Rmd”) |
| html | 2296e1a | nklimko | 2023-06-29 | Build site. |
| Rmd | 9d0b702 | nklimko | 2023-06-29 | wflow_publish(“analysis/covCor.Rmd”) |
| html | 43083ea | nklimko | 2023-06-29 | Build site. |
| Rmd | 85cf149 | nklimko | 2023-06-29 | wflow_publish(“analysis/covCor.Rmd”) |
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| Rmd | 36024af | nklimko | 2023-06-29 | wflow_publish(“analysis/covCor.Rmd”) |
The following methods are currently implemented:
Mr. Mash
GBLUP
BayesC
traits <- c('starvation','cafe','free.glycerol','free.glucose')
covMash <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/genetic/mr.mash_f_sr.top3.Rds")
covBlup <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/genetic/multigblup_f_sr.top3.Rds")
covBayes <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/genetic/multibayesC_f_sr.top3.Rds")
rownames(covMash) <- traits
colnames(covMash) <- traits
rownames(covBlup) <- traits
colnames(covBlup) <- traits
rownames(covBayes) <- traits
colnames(covBayes) <- traits
allCor <- list(mr.mash=covMash, gblup=covBlup, bayesC=covBayes)
print(allCor)$mr.mash
starvation cafe free.glycerol free.glucose
starvation 1.0000000 0.8744728 -0.8180919 -0.7278613
cafe 0.8744728 1.0000000 -0.6984191 -0.6169826
free.glycerol -0.8180919 -0.6984191 1.0000000 0.4966888
free.glucose -0.7278613 -0.6169826 0.4966888 1.0000000
$gblup
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.40194695 0.17856937 0.3449596
cafe -0.4019470 1.00000000 -0.05537373 -0.2061424
free.glycerol 0.1785694 -0.05537373 1.00000000 0.3477372
free.glucose 0.3449596 -0.20614242 0.34773718 1.0000000
$bayesC
starvation cafe free.glycerol free.glucose
starvation 1.00000000 -0.3642108 0.06414897 0.3351407
cafe -0.36421082 1.0000000 -0.10821256 -0.2337754
free.glycerol 0.06414897 -0.1082126 1.00000000 0.4240397
free.glucose 0.33514067 -0.2337754 0.42403969 1.0000000covMash <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/residual/mr.mash_f_sr.top3.Rds")
covBlup <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/residual/multigblup_f_sr.top3.Rds")
covBayes <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/residual/multibayesC_f_sr.top3.Rds")
rownames(covMash) <- traits
colnames(covMash) <- traits
rownames(covBlup) <- traits
colnames(covBlup) <- traits
rownames(covBayes) <- traits
colnames(covBayes) <- traits
allCor <- list(mr.mash=covMash, gblup=covBlup, bayesC=covBayes)
print(allCor)$mr.mash
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.310667600 0.259026024 0.2878386
cafe -0.3106676 1.000000000 -0.007593636 -0.1134700
free.glycerol 0.2590260 -0.007593636 1.000000000 0.3864606
free.glucose 0.2878386 -0.113470017 0.386460558 1.0000000
$gblup
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.20077482 0.32234601 0.21681177
cafe -0.2007748 1.00000000 0.02873947 -0.02419659
free.glycerol 0.3223460 0.02873947 1.00000000 0.40851429
free.glucose 0.2168118 -0.02419659 0.40851429 1.00000000
$bayesC
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.295690713 0.361438723 0.30855544
cafe -0.2956907 1.000000000 0.007877075 -0.08569471
free.glycerol 0.3614387 0.007877075 1.000000000 0.44594467
free.glucose 0.3085554 -0.085694713 0.445944668 1.00000000covMash <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/genetic/mr.mash_m_sr.top3.Rds")
covBlup <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/genetic/multigblup_m_sr.top3.Rds")
covBayes <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/genetic/multibayesC_m_sr.top3.Rds")
rownames(covMash) <- traits
colnames(covMash) <- traits
rownames(covBlup) <- traits
colnames(covBlup) <- traits
rownames(covBayes) <- traits
colnames(covBayes) <- traits
allCor <- list(mr.mash=covMash, gblup=covBlup, bayesC=covBayes)
print(allCor)$mr.mash
starvation cafe free.glycerol free.glucose
starvation 1.00000000 0.3958853 -0.62460824 -0.09489319
cafe 0.39588527 1.0000000 -0.26613636 0.43982589
free.glycerol -0.62460824 -0.2661364 1.00000000 0.09257539
free.glucose -0.09489319 0.4398259 0.09257539 1.00000000
$gblup
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.40489417 0.25579581 0.34307339
cafe -0.4048942 1.00000000 0.03441261 -0.02261244
free.glycerol 0.2557958 0.03441261 1.00000000 0.45703062
free.glucose 0.3430734 -0.02261244 0.45703062 1.00000000
$bayesC
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.302125003 0.29106739 0.294493791
cafe -0.3021250 1.000000000 -0.05066257 -0.007827465
free.glycerol 0.2910674 -0.050662570 1.00000000 0.426145604
free.glucose 0.2944938 -0.007827465 0.42614560 1.000000000covMash <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/residual/mr.mash_m_sr.top3.Rds")
covBlup <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/residual/multigblup_m_sr.top3.Rds")
covBayes <- readRDS("/data2/morgante_lab/nklimko/rep/dgrp-starve/snake/data/22_cov/residual/multibayesC_m_sr.top3.Rds")
rownames(covMash) <- traits
colnames(covMash) <- traits
rownames(covBlup) <- traits
colnames(covBlup) <- traits
rownames(covBayes) <- traits
colnames(covBayes) <- traits
allCor <- list(mr.mash=covMash, gblup=covBlup, bayesC=covBayes)
print(allCor)$mr.mash
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.30844847 0.3383513 0.29428243
cafe -0.3084485 1.00000000 -0.0397316 -0.04264402
free.glycerol 0.3383513 -0.03973160 1.0000000 0.41443304
free.glucose 0.2942824 -0.04264402 0.4144330 1.00000000
$gblup
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.19568123 0.3980355 0.21583822
cafe -0.1956812 1.00000000 -0.1043009 -0.06051565
free.glycerol 0.3980355 -0.10430095 1.0000000 0.34762876
free.glucose 0.2158382 -0.06051565 0.3476288 1.00000000
$bayesC
starvation cafe free.glycerol free.glucose
starvation 1.0000000 -0.31518688 0.4206801 0.31394922
cafe -0.3151869 1.00000000 -0.0675322 -0.04858729
free.glycerol 0.4206801 -0.06753220 1.0000000 0.44168462
free.glucose 0.3139492 -0.04858729 0.4416846 1.00000000###FEMALE
#setwd('/data2/morgante_lab/nklimko/rep/dgrp-starve/')
bayesC <- readRDS("snake/data/20_cor/bayesC_f_starvation.Rds")
gblup <- readRDS("snake/data/20_cor/gblup_f_starvation.Rds")
multigblupData <- readRDS('snake/data/20_cor/multigblup_f_starvation.Rds')
temp <- unlist(multigblupData)
multigblup <- temp[seq(1, length(temp), by=4)]
multibayesCData <- readRDS('snake/data/20_cor/multibayesC_f_starvation.Rds')
temp <- unlist(multibayesCData)
multibayesC <- temp[seq(1, length(temp), by=4)]
mr.mashData <- readRDS('snake/data/20_cor/mr.mash_f_starvation.Rds')
temp <- unlist(mr.mashData)
mr.mash <- temp[seq(1, length(temp), by=4)]
mlassoData <- readRDS('snake/data/20_cor/mlasso_f_starvation.Rds')
temp <- unlist(mlassoData)
mlasso <- temp[seq(1, length(temp), by=4)]
topmbc <- unlist(readRDS('snake/data/20_cor/multibayesC_f_sr.top3.Rds'))
top_multibayesC <- topmbc[seq(1, length(topmbc), by=16)]
topblup <- unlist(readRDS('snake/data/20_cor/multiblup_f_sr.top3.Rds'))
top_multiblup <- topblup[seq(1, length(topblup), by=16)]
temp <- c(gblup, bayesC, multigblup, multibayesC, mr.mash, mlasso, top_multiblup, top_multibayesC)
label <- c(rep("gblup", iter), rep("bayesC", iter), rep("multigblup", iter), rep("multibayesC", iter), rep("mr.mash", iter), rep('mlasso', iter), rep('top_multiblup', iter), rep("top_multibayesC", iter))
data <- data.table(cor=as.numeric(temp), method=label)
gg[[1]] <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y='Correlation between True and Predicted Phenotype',tag='F') +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
print(paste0(c('bayesC', 'gblup', 'multibayesC', 'multigblup', 'mr.mash', 'top_multibayesC', 'top_multiblup'),': ',c(mean(bayesC), mean(gblup), mean(multibayesC), mean(multigblup), mean(mr.mash), mean(top_multibayesC), mean(top_multiblup))))### MALE
#setwd('/data2/morgante_lab/nklimko/rep/dgrp-starve/')
bayesC <- readRDS("snake/data/20_cor/bayesC_m_starvation.Rds")
gblup <- readRDS("snake/data/20_cor/gblup_m_starvation.Rds")
multigblupData <- readRDS('snake/data/20_cor/multigblup_m_starvation.Rds')
temp <- unlist(multigblupData)
multigblup <- temp[seq(1, length(temp), by=4)]
multibayesCData <- readRDS('snake/data/20_cor/multibayesC_m_starvation.Rds')
temp <- unlist(multibayesCData)
multibayesC <- temp[seq(1, length(temp), by=4)]
mr.mashData <- readRDS('snake/data/20_cor/mr.mash_m_starvation.Rds')
temp <- unlist(mr.mashData)
mr.mash <- temp[seq(1, length(temp), by=4)]
mlassoData <- ('snake/data/20_cor/mlasso_m_starvation.Rds')
temp <- unlist(mlassoData)
mlasso <- temp[seq(1, length(temp), by=4)]
topmbc <- unlist(readRDS('snake/data/20_cor/multibayesC_m_sr.top3.Rds'))
top_multibayesC <- topmbc[seq(1, length(topmbc), by=16)]
topblup <- unlist(readRDS('snake/data/20_cor/multiblup_m_sr.top3.Rds'))
top_multiblup <- topblup[seq(1, length(topblup), by=16)]
temp <- c(gblup, bayesC, multigblup, multibayesC, mr.mash, mlasso, top_multiblup, top_multibayesC)
label <- c(rep("gblup", iter), rep("bayesC", iter), rep("multigblup", iter), rep("multibayesC", iter), rep("mr.mash", iter), rep('mlasso', iter), rep('top_multiblup', iter), rep("top_multibayesC", iter))
data <- data.table(cor=as.numeric(temp), method=label)
gg[[2]] <- ggplot(data, aes(x=method, y=cor, fill=method)) +
geom_violin(color = NA, width = 0.65) +
geom_boxplot(color='#440154FF', width = 0.15) +
theme_minimal() +
stat_summary(fun=mean, color='#440154FF', geom='point',
shape=18, size=3, show.legend=FALSE) +
labs(x=NULL,y='Correlation between True and Predicted Phenotype',tag='M') +
theme(legend.position='none',
axis.text.x = element_text(angle = -45, size=10),
text=element_text(size=10),
plot.tag = element_text(size=15)) +
scale_fill_viridis(begin = 0.4, end=0.9,discrete=TRUE)
print(paste0(c('bayesC', 'gblup', 'multibayesC', 'multigblup', 'mr.mash', 'top_multibayesC', 'top_multiblup'),': ',c(mean(bayesC), mean(gblup), mean(multibayesC), mean(multigblup), mean(mr.mash), mean(top_multibayesC), mean(top_multiblup))))plot_grid(gg[[1]],gg[[2]], ncol=2)
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.5 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /opt/ohpc/pub/libs/gnu9/openblas/0.3.7/lib/libopenblasp-r0.3.7.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 purrr_1.0.1
[5] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 tidyverse_2.0.0
[9] scales_1.2.1 viridis_0.6.2 viridisLite_0.4.2 doParallel_1.0.17
[13] iterators_1.0.14 foreach_1.5.2 qqman_0.1.8 cowplot_1.1.1
[17] ggplot2_3.4.2 data.table_1.14.8 dplyr_1.1.2 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.10 getPass_0.2-2 ps_1.7.2 rprojroot_2.0.3
[5] digest_0.6.31 utf8_1.2.3 R6_2.5.1 evaluate_0.20
[9] httr_1.4.5 highr_0.10 pillar_1.9.0 rlang_1.1.1
[13] rstudioapi_0.14 whisker_0.4.1 callr_3.7.3 jquerylib_0.1.4
[17] rmarkdown_2.20 munsell_0.5.0 compiler_4.1.2 httpuv_1.6.9
[21] xfun_0.37 pkgconfig_2.0.3 htmltools_0.5.4 tidyselect_1.2.0
[25] gridExtra_2.3 codetools_0.2-19 fansi_1.0.4 calibrate_1.7.7
[29] tzdb_0.3.0 withr_2.5.0 later_1.3.0 MASS_7.3-58.3
[33] grid_4.1.2 jsonlite_1.8.4 gtable_0.3.3 lifecycle_1.0.3
[37] git2r_0.31.0 magrittr_2.0.3 cli_3.6.1 stringi_1.7.12
[41] cachem_1.0.7 fs_1.6.1 promises_1.2.0.1 bslib_0.4.2
[45] generics_0.1.3 vctrs_0.6.2 tools_4.1.2 glue_1.6.2
[49] hms_1.1.3 processx_3.8.0 fastmap_1.1.1 yaml_2.3.7
[53] timechange_0.2.0 colorspace_2.1-0 knitr_1.42 sass_0.4.5