Last updated: 2020-08-18

Checks: 6 1

Knit directory: psychencode/

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/get_r2_LV.Rmd) and HTML (docs/get_r2_LV.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 72edb67 GitHub 2020-08-18 Merge pull request #2 from lvairus/master
Rmd f312374 Hae Kyung Im 2020-08-18 fixed exists rmat
Rmd 9da6862 Laura Vairus 2020-08-17 fixed paths
Rmd c705f35 Laura Vairus 2020-08-17 debugging
Rmd 633a130 Laura Vairus 2020-08-17 created matrix of rsq values from PEC_TWAS_weights.tar.gz files

Load Libraries

Run in R:

suppressPackageStartupMessages(library(RSQLite))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(data.table))
cd $DATA
mkdir PEC_TWAS_weights
cd PEC_TWAS_weights
wget "http://resource.psychencode.org/Datasets/Derived/PEC_TWAS_weights.tar.gz"
tar xvf PEC_TWAS_weights.tar.gz

Definitions

PRE="/Users/lvairus"
DATA=glue::glue("{PRE}/data-Github/psychencode")
OUTPUT=DATA

This script is to copy and paste the rsq data from the PEC_TWAS_weights.tar.gz files into a matrix

filelist <- list.files(glue::glue("{DATA}/PEC_TWAS_weights/"))
filelist <- filelist[substr(filelist,1,4)=="ENSG"]

file=filelist[1]

load(glue::glue("{DATA}/PEC_TWAS_weights/{file}"))
ngenes <- length(filelist)
R2_mat <- matrix(NA, ngenes, 5)
genelist <- substr(filelist, 1, 15)
rownames(R2_mat) <- genelist
colnames(R2_mat) <- colnames(cv.performance)


for (file in filelist){
  load(glue::glue("{DATA}/PEC_TWAS_weights/{file}"))
  genename <- substr(file, 1, 15)
  R2_mat[genename, ] <- cv.performance["rsq", ]
}

saveRDS(R2_mat, "~/data-Github/psychencode/R2_mat.RDS")
if( !exists("R2_mat") ) { 
  R2_mat = readRDS(glue::glue("{DATA}/R2_mat.RDS"))
} 

plot(R2_mat[,"enet"],R2_mat[,"top1"],main="CV R2 comparison top SNP vs enet");abline(0,1,col='gray')

plot(R2_mat[,"enet"],R2_mat[,"blup"],main="CV R2 comparison blup vs enet");abline(0,1,col='gray')

plot(R2_mat[,"enet"],R2_mat[,"bslmm"],main="CV R2 comparison bslmm vs enet");abline(0,1,col='gray')

plot(R2_mat[,"enet"],R2_mat[,"lasso"],main="CV R2 comparison lasso vs enet");abline(0,1,col='gray')

pairs(R2_mat)

Conclusion: these figure shows that Elastic Net has overall better prediction performance (points are above the one to one line)

You can see on the graphs that, overall, the prediction performance of the Elastic Net is larger compared to the other methods.


sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] data.table_1.12.8 forcats_0.5.0     stringr_1.4.0     dplyr_0.8.5      
 [5] purrr_0.3.4       readr_1.3.1       tidyr_1.0.2       tibble_3.0.0     
 [9] ggplot2_3.3.0     tidyverse_1.3.0   RSQLite_2.2.0    

loaded via a namespace (and not attached):
 [1] tidyselect_1.0.0 xfun_0.13        haven_2.2.0      lattice_0.20-41 
 [5] colorspace_1.4-1 vctrs_0.2.4      generics_0.0.2   htmltools_0.4.0 
 [9] yaml_2.2.1       blob_1.2.1       rlang_0.4.5      later_1.0.0     
[13] pillar_1.4.3     withr_2.1.2      glue_1.4.1       DBI_1.1.0       
[17] bit64_0.9-7      dbplyr_1.4.2     readxl_1.3.1     modelr_0.1.6    
[21] lifecycle_0.2.0  cellranger_1.1.0 munsell_0.5.0    gtable_0.3.0    
[25] workflowr_1.6.2  rvest_0.3.5      memoise_1.1.0    evaluate_0.14   
[29] knitr_1.28       httpuv_1.5.2     fansi_0.4.1      broom_0.5.5     
[33] Rcpp_1.0.4.6     promises_1.1.0   backports_1.1.6  scales_1.1.0    
[37] jsonlite_1.6.1   fs_1.4.1         bit_1.1-15.2     hms_0.5.3       
[41] digest_0.6.25    stringi_1.4.6    rprojroot_1.3-2  grid_3.6.3      
[45] cli_2.0.2        tools_3.6.3      magrittr_1.5     crayon_1.3.4    
[49] whisker_0.4      pkgconfig_2.0.3  ellipsis_0.3.0   xml2_1.3.1      
[53] reprex_0.3.0     lubridate_1.7.8  rstudioapi_0.11  httr_1.4.1      
[57] assertthat_0.2.1 rmarkdown_2.1    R6_2.4.1         nlme_3.1-147    
[61] git2r_0.26.1     compiler_3.6.3