Iterative Comparison
Last updated: 2023-05-30
Checks: 7 0
Knit directory: dgrp-starve/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20221101) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version a742cc3. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: code/snake/
Ignored: data/snake/
Untracked files:
Untracked: .snakemake/
Untracked: Rplot.pdf
Untracked: analysis/linearReg.Rmd
Untracked: bglr-f.R
Untracked: code/PCA/
Untracked: code/data-prep/
Untracked: code/fabio/
Untracked: code/gBayesC.R
Untracked: code/id_bank_creation.R
Untracked: code/intro-starve/
Untracked: code/methodComp/
Untracked: code/regress/
Untracked: data/bayesF.rds
Untracked: data/bayesM.rds
Untracked: data/bglr-f-130k.rds
Untracked: data/bglr-f.rds
Untracked: data/bglr-m-130k.rds
Untracked: data/bglr-m.rds
Untracked: data/corLoop-f-minus.rds
Untracked: data/corLoop-f.rds
Untracked: data/corLoop-m-Minus.rds
Untracked: data/corLoop-m-minus.rds
Untracked: data/corLoop-m.rds
Untracked: data/fRegress.txt
Untracked: data/fRegress_adj.txt
Untracked: data/fm.burglar
Untracked: data/gbayesC-f.Rds
Untracked: data/gbayesC-m.Rds
Untracked: data/gbayesC.Rds
Untracked: data/gbayes_100k-f.Rds
Untracked: data/gbayes_100k-m.Rds
Untracked: data/goGroups.txt
Untracked: data/id_bank
Untracked: data/id_bank.Rds
Untracked: data/mPart.txt
Untracked: data/mRegress.txt
Untracked: data/mRegress_adj.txt
Untracked: data/multiReg.rData
Untracked: data/pheno_f
Untracked: data/pheno_m
Untracked: data/starve-f.txt
Untracked: data/starve-m.txt
Untracked: data/xp-f.txt
Untracked: data/xp-m.txt
Untracked: data/xp_f
Untracked: data/xp_m
Untracked: data/y_save.txt
Untracked: f-cor.png
Untracked: figure/
Untracked: filesnake1
Untracked: filesnake4_3
Untracked: junk/
Untracked: m-cor.png
Untracked: notes/
Untracked: posterPlots.R
Untracked: runtime.png
Untracked: snake/
Unstaged changes:
Modified: .Rprofile
Modified: .gitattributes
Modified: .gitignore
Modified: README.md
Modified: _workflowr.yml
Modified: analysis/_site.yml
Modified: analysis/about.Rmd
Deleted: analysis/gremlo.R
Modified: analysis/license.Rmd
Modified: analysis/linReg.Rmd
Deleted: analysis/methodComp-f.Rmd
Deleted: analysis/methodComp-m.Rmd
Modified: analysis/methodComp.Rmd
Modified: analysis/methodPred.Rmd
Modified: analysis/multiComp.Rmd
Modified: analysis/multiReg.Rmd
Modified: analysis/pca.Rmd
Modified: analysis/predict.Rmd
Modified: analysis/recap.Rmd
Modified: analysis/rewrite.Rmd
Modified: analysis/starve.Rmd
Deleted: analysis/stepwise-f.Rmd
Deleted: analysis/stepwise-m.Rmd
Deleted: analysis/testing.R
Deleted: analysis/tips.Rmd
Modified: analysis/trace.Rmd
Modified: code/README.md
Deleted: code/baseScript-lineComp.R
Deleted: code/combineSNP.R
Deleted: code/four-comp.76979.err
Deleted: code/four-comp.76979.out
Deleted: code/four-comp.sbatch
Deleted: code/fourLinePrep.R
Deleted: code/line_avgMinus.R
Deleted: code/line_avgPlus.R
Deleted: code/line_difMinus.R
Deleted: code/line_difPlus.R
Deleted: code/snpGene.R
Deleted: code/starveDataPrep.R
Modified: data/README.md
Modified: data/avgMinus-result.txt
Modified: data/avgMinus.txt
Modified: data/avgPlus-result.txt
Modified: data/avgPlus.txt
Modified: data/difMinus-result.txt
Modified: data/difMinus.txt
Modified: data/difPlus-result.txt
Modified: data/difPlus.txt
Modified: data/geneHits.txt
Modified: data/snpList.txt
Modified: data/starve.csv
Modified: dgrp-starve.Rproj
Modified: output/README.md
Modified: output/avgMinus-result.txt
Modified: output/avgPlus-result.txt
Modified: output/difMinus-result.txt
Modified: output/difPlus-result.txt
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/multitraitPred.Rmd) and HTML (docs/multitraitPred.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 | a742cc3 | nklimko | 2023-05-30 | wflow_publish("analysis/multitraitPred.Rmd") |
| html | 21bf8de | nklimko | 2023-05-30 | Build site. |
| Rmd | 9fb475c | nklimko | 2023-05-30 | wflow_publish("analysis/multitraitPred.Rmd") |
The overarching goal of this process is to predict the phenotype for starvation resistance, a continuous trait, by using gene expression data, another continuous trait. This is done using k-fold cross validation to create models based on a subset of the data and calculating the correlation of that model with the remaining partition. By repeating this process multiple times with different training and testing partitions, model bias can be significantly reduced and allows for calculation of average correlation coefficients for each model. The primary difference between the methods in question is the prior distribution used.
#loop count and data limit
iter <- 50
#ggplot holder list
gg <- vector(mode='list', length=12)The first main set of data used for this analysis is a matrix of gene expression by DGRP line matched to raw starvation resistance. A second cluster of data sets provided by the Morgante Lab includes information on Wolbachia infection status and inversion status by line along with functions to adjust phenotypic values based on these two factors.
#wolb infection and inversion status data with phenotype adjustment function
load("/data2/morgante_lab/data/dgrp/misc/adjustData.RData")
#expression data matched to line and starvation phenotype
xp_f <- fread("data/xp-f.txt")
# xp_m <- fread("data/xp-m.txt")
#setwd("C:/Users/noahk/OneDrive/Desktop/amogus")
#getwd()
#create matrix of only gene expression, trims line and starvation
X <- as.matrix(xp_f[,3:11340])
rownames(X) <- xp_f[,line]
W <- scale(X)
y_temp <- xp_f[,starvation]
dat <- data.frame(id=xp_f[,line], y=y_temp)
y <- adjustPheno(dat, "starvation")
#model to solve for, vector of ones
mu <- matrix(rep(1, length(y)), ncol=1)
#names(mu) <- paste0("line", 1:length(mu))
rownames(mu) <- xp_f[,line]
TRM <- tcrossprod(W)/ncol(W)
# k-fold parameters
n <- length(y)
fold <- 5The matrix containing only gene expression by line data was then scaled to an absolute max of 1. along with this, a Translation Relationship Matrix was generated by taking the crossproduct of the scaled expression matrix and scaling it down by the number of genes.
5 was chosen for k-fold cross validation resulting in 39 lines per validation set and 159 lines per training set.
The following methods are currently implemented:
BGLR - Bayesian General Linear Regression has the capability to perform BayesC linear regression using a Gibbs Sampler.
Under this, both GBLUP and BayesC have been implemented in single trait and multitrait prediction based methods.
ids <- readRDS("data/id_bank.Rds")
registerDoParallel(cores = 1)
iter <- 1
i <- 1
corLoop <- foreach(i=1:iter) %dopar% {
#result holder
corResult <- rep(0, 5)
#setup train and test sets with trait vectors
#test_IDs <- sample(1:n, as.integer(n / fold))
test_IDs <- unlist(ids[i])
W_train <- W[-test_IDs,]
W_test <- W[test_IDs,]
y_train <- y[-test_IDs]
y_test <- y[test_IDs]
### BGLR
fitBGLR <- BGLR::BGLR(y_train, response_type = "gaussian", a=NULL, b=NULL, ETA = list(list(X=W_train, model="BayesC")), nIter = 500, burnIn = 200)
y_calc <- predict(fitBGLR, W_test)
corResult[5] <- cor(y_test, y_calc)
length(y_calc)
length(y_test)
#Overall result
corResult
}
corLoop <- foreach(i=1:iter) %dopar% {
#result holder
corResult <- rep(0, 5)
#setup train and test sets with trait vectors
#test_IDs <- sample(1:n, as.integer(n / fold))
test_IDs <- unlist(ids[i])
W_train <- W[-test_IDs,]
W_test <- W[test_IDs,]
y_train <- y[-test_IDs]
y_test <- y[test_IDs]
### QGG::GREML
fitGreml <- qgg::greml(y=y, X=mu, GRM=list(A=TRM), validate = matrix(test_IDs,ncol=1), verbose=FALSE)
corResult[1] <- fitGreml$accuracy$Corr
### QGG::GBAYES-C
fitC <- qgg::gbayes(y=y_train, W=W_train, method="bayesC", scaleY=FALSE, nit=50000, nburn=20000)
# \hat{y}_test = W_{test} * \hat{b} + \hat{mu}
y_calc <- W_test %*% fitC$b + mean(y_train)
corResult[2] <- cor(y_test, y_calc)
### VARBVS
fitVarb <- varbvs::varbvs(X = W_train, NULL, y=y_train, family = "gaussian", logodds=seq(-3.5,-1,0.1), sa = 1, verbose=TRUE)
y_calc <- predict(fitVarb, X=W_test)
corResult[3] <- cor(y_test, y_calc)
### GLMNET::LASSO
fitlm <- glmnet::cv.glmnet(x=W_train, y=y_train, alpha=1)
y_calc <- predict(fitlm, W_test, s="lambda.min")
corResult[4] <- cor(y_test, y_calc)
### BGLR
fitBGLR <- BGLR::BGLR(y_train, response_type = "gaussian", a=NULL, b=NULL,ETA = list(list(X=W_train, model="BayesC")), nIter = 50000, burnIn = 20000)
y_calc <- predict(fitBGLR, W_test)
corResult[5] <- cor(y_test, y_calc)
#Overall result
corResult
}###FEMALE
bayesC <- readRDS("snake/data/2_cor/bayesC_f_starvation.Rds")
gblup <- readRDS("snake/data/2_cor/gblup_f_starvation.Rds")
multigblupData <- readRDS('snake/data/2_cor/multigblup_f_starvation.Rds')
temp <- unlist(multigblupData)
multigblup <- temp[seq(1, length(temp), by=4)]
multigblupM <- temp[seq(4, length(temp), by=4)]
multibayesCData <- readRDS('snake/data/2_cor/multibayesC_f_starvation.Rds')
temp <- unlist(multibayesCData)
multibayesC <- temp[seq(1, length(temp), by=4)]
multibayesCM <- temp[seq(4, length(temp), by=4)]
temp <- c(gblup, bayesC, multigblup, multibayesC)
label <- c(rep("gblup", iter), rep("bayesC", iter), rep("multigblup", iter), rep("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'),': ',c(mean(bayesC), mean(gblup), mean(multibayesC), mean(multigblup))))[1] "bayesC: 0.28144367623869" "gblup: 0.279982238117636"
[3] "multibayesC: 0.820469324518272" "multigblup: 0.868640583346076" bayesC <- readRDS("snake/data/2_cor/bayesC_m_starvation.Rds")
gblup <- readRDS("snake/data/2_cor/gblup_m_starvation.Rds")
multigblupData <- readRDS('snake/data/2_cor/multigblup_m_starvation.Rds')
temp <- unlist(multigblupData)
multigblup <- temp[seq(4, length(temp), by=4)]
multibayesCData <- readRDS('snake/data/2_cor/multibayesC_m_starvation.Rds')
temp <- unlist(multibayesCData)
multibayesC <- temp[seq(4, length(temp), by=4)]
temp <- c(gblup, bayesC, multigblup, multibayesC)
label <- c(rep("gblup", iter), rep("bayesC", iter), rep("multigblup", iter), rep("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'),': ',c(mean(bayesC), mean(gblup), mean(multibayesC), mean(multigblup))))[1] "bayesC: 0.348274542374069" "gblup: 0.349139906076041"
[3] "multibayesC: 0.76387819244448" "multigblup: 0.827503263458944"Correlation Coefficient Boxplots
plot_grid(gg[[1]],gg[[2]], ncol=2)
| Version | Author | Date |
|---|---|---|
| 21bf8de | nklimko | 2023-05-30 |
Runtimes
#plot_grid(gg[[3]],gg[[4]], ncol=2)#FEMALE
female <- c(bayesC=mean(bayesC),lasso=mean(lasso),rr=mean(rr),varbvs=mean(varbvs))
female
male
male <- c(bayesC=mean(bayesC),lasso=mean(na.omit(lasso)),rr=mean(rr),varbvs=mean(varbvs))
#MALE
male <- c(mean(bayesC),mean(lasso),mean(rr),mean(varbvs))
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] BGLR_1.1.0 glmnet_4.1-6 Matrix_1.5-3 varbvs_2.5-16
[5] qgg_1.1.1 scales_1.2.1 viridis_0.6.2 viridisLite_0.4.1
[9] doParallel_1.0.17 iterators_1.0.14 foreach_1.5.2 qqman_0.1.8
[13] cowplot_1.1.1 ggplot2_3.4.1 data.table_1.14.8 dplyr_1.1.0
[17] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.5 sass_0.4.5 nor1mix_1.3-0
[4] jsonlite_1.8.4 splines_4.1.2 bslib_0.4.2
[7] getPass_0.2-2 statmod_1.5.0 highr_0.10
[10] latticeExtra_0.6-30 yaml_2.3.7 pillar_1.8.1
[13] lattice_0.20-45 quantreg_5.94 glue_1.6.2
[16] digest_0.6.31 RColorBrewer_1.1-3 promises_1.2.0.1
[19] colorspace_2.1-0 htmltools_0.5.4 httpuv_1.6.9
[22] pkgconfig_2.0.3 SparseM_1.81 calibrate_1.7.7
[25] processx_3.8.0 whisker_0.4.1 jpeg_0.1-10
[28] later_1.3.0 MatrixModels_0.5-1 git2r_0.31.0
[31] tibble_3.2.0 farver_2.1.1 generics_0.1.3
[34] cachem_1.0.7 withr_2.5.0 cli_3.6.0
[37] survival_3.5-5 magrittr_2.0.3 deldir_1.0-6
[40] mcmc_0.9-7 evaluate_0.20 ps_1.7.2
[43] fs_1.6.1 fansi_1.0.4 MASS_7.3-58.3
[46] truncnorm_1.0-9 tools_4.1.2 lifecycle_1.0.3
[49] stringr_1.5.0 MCMCpack_1.6-3 interp_1.1-3
[52] munsell_0.5.0 callr_3.7.3 compiler_4.1.2
[55] jquerylib_0.1.4 rlang_1.1.0 grid_4.1.2
[58] rstudioapi_0.14 labeling_0.4.2 rmarkdown_2.20
[61] gtable_0.3.1 codetools_0.2-19 R6_2.5.1
[64] gridExtra_2.3 knitr_1.42 fastmap_1.1.1
[67] utf8_1.2.3 rprojroot_2.0.3 shape_1.4.6
[70] stringi_1.7.12 Rcpp_1.0.10 vctrs_0.5.2
[73] png_0.1-8 tidyselect_1.2.0 xfun_0.37
[76] coda_0.19-4