Iterative Comparison
Last updated: 2023-06-20
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Knit directory: dgrp-starve/
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The following methods are currently implemented:
BayesC
GBLUP
MultiBayesC
MultiGBLUP
mr.mash
MultiLASSO
###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))))[1] "bayesC: 0.28144367623869" "gblup: 0.279982238117636"
[3] "multibayesC: 0.252335414801586" "multigblup: 0.272734703328458"
[5] "mr.mash: 0.263852919847503" "top_multibayesC: 0.241689113192319"
[7] "top_multiblup: 0.275657017370914" ### 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)Warning in data.table(cor = as.numeric(temp), method = label): NAs introduced
by coercionWarning in as.data.table.list(x, keep.rownames = keep.rownames, check.names =
check.names, : Item 1 has 351 rows but longest item has 400; recycled with
remainder.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))))[1] "bayesC: 0.348274542374069" "gblup: 0.349139906076041"
[3] "multibayesC: 0.313346489684489" "multigblup: 0.3433458357234"
[5] "mr.mash: 0.334111164959302" "top_multibayesC: 0.342689855485783"
[7] "top_multiblup: 0.364102522420843" Correlation Coefficient Boxplots
plot_grid(gg[[1]],gg[[2]], ncol=2)
Alpha variation
setwd('snake/data/20_cor/')
tempDat <- list.files(pattern="lasso_\\d") %>% map(readRDS) %>% bind_cols()New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...4`
• `` -> `...5`
• `` -> `...6`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...10`#tempDat <- list.files(path='snake/data/20_cor', pattern="lasso_\\d") %>% map(readRDS) %>% bind_cols()
dat <- data.frame(cor=unlist(tempDat[,seq(1,9,2)]))
label = rep(seq(0,1,0.25), each=iter)
data <- data.table(cor=as.numeric(unlist(dat)), alpha=as.factor(label))
gg[[3]] <- ggplot(data, aes(x=alpha, y=cor, fill=alpha)) +
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)
dat <- data.frame(cor=unlist(tempDat[,seq(2,10,2)]))
label = rep(seq(0,1,0.25), each=iter)
data <- data.table(cor=as.numeric(unlist(dat)), alpha=as.factor(label))
gg[[4]] <- ggplot(data, aes(x=alpha, y=cor, fill=alpha)) +
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)plot_grid(gg[[3]],gg[[4]], ncol=2)
| Version | Author | Date |
|---|---|---|
| 3a85d38 | nklimko | 2023-06-20 |
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 labeling_0.4.2 munsell_0.5.0 compiler_4.1.2
[21] httpuv_1.6.9 xfun_0.37 pkgconfig_2.0.3 htmltools_0.5.4
[25] tidyselect_1.2.0 gridExtra_2.3 codetools_0.2-19 fansi_1.0.4
[29] calibrate_1.7.7 tzdb_0.3.0 withr_2.5.0 later_1.3.0
[33] MASS_7.3-58.3 grid_4.1.2 jsonlite_1.8.4 gtable_0.3.3
[37] lifecycle_1.0.3 git2r_0.31.0 magrittr_2.0.3 cli_3.6.1
[41] stringi_1.7.12 cachem_1.0.7 farver_2.1.1 fs_1.6.1
[45] promises_1.2.0.1 bslib_0.4.2 generics_0.1.3 vctrs_0.6.2
[49] tools_4.1.2 glue_1.6.2 hms_1.1.3 processx_3.8.0
[53] fastmap_1.1.1 yaml_2.3.7 timechange_0.2.0 colorspace_2.1-0
[57] knitr_1.42 sass_0.4.5