Last updated: 2022-08-20
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Knit directory: RatXcan_Training/
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library(tidyverse)── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.0.4 ✔ dplyr 1.0.2
✔ tidyr 1.1.2 ✔ stringr 1.4.0
✔ readr 1.4.0 ✔ forcats 0.5.0── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()library(data.table)
Attaching package: 'data.table'The following objects are masked from 'package:dplyr':
between, first, lastThe following object is masked from 'package:purrr':
transposelibrary(RSQLite)
library(glmnet)Loading required package: Matrix
Attaching package: 'Matrix'The following objects are masked from 'package:tidyr':
expand, pack, unpackLoaded glmnet 4.1-3"%&%" = function(a,b) paste(a,b,sep="")
devtools::source_gist("ee5f67abddd0b761ee24410ea71c41aa")Sourcing https://gist.githubusercontent.com/natashasanthanam/ee5f67abddd0b761ee24410ea71c41aa/raw/185ab37e5a31f1d3bab1530e468950e30ff31337/fn_generate_trait.RSHA-1 hash of file is c56941964697a118c351c3f81322a221fa13a1b3devtools::source_gist("38431b74c6c0bf90c12f")Sourcing https://gist.githubusercontent.com/hakyim/38431b74c6c0bf90c12f/raw/f16d9de559d20ce605e1e13eee75e82a0f6e1507/qqunif.RSHA-1 hash of file is c5aba9ddce06b95125b727d96bffe7bd1557fcc3devtools::source_gist("1e9053c8f35c30396429350a08f33ea7")Sourcing https://gist.githubusercontent.com/natashasanthanam/1e9053c8f35c30396429350a08f33ea7/raw/e35c8cabb742f17f1998f9ac4198878f9c683605/qqunif.RSHA-1 hash of file is 7388784ab8c7c2dc5c3f950dc8a47a1c76e3d7acYanyu’s PTRS weights estimate the effect of genes on a given trait, in this case we pick height and BMI.
traits <- c("height", "bmi")
# folder with PrediXcan results
results.dir <- "/Users/sabrinami/Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/Results/PrediXcan/metabolic_traits/"
# folder with PTRS weights, predicted traits will output here
data.dir <- "/Users/sabrinami/Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/data/"The orth.rats file contains gives a dictionary between
human genes and the corresponding gene in rats.
orth.rats <- read_tsv(data.dir %&% "expression/ortholog_genes_rats_humans.tsv", col_names = TRUE)We first replace rat genes in the predicted expression results with their corresponding human genes, so that it could be compatible with PTRS weights.
pred_expr <- read_tsv(results.dir %&% "rat_metabolic_Ac_best__predict.txt") %>% select(-c(FID))
#filter only for genes that have a human ortholog
pred_expr <- pred_expr %>% select(c(IID, intersect(colnames(pred_expr), orth.rats$rnorvegicus_homolog_ensembl_gene) ))
#change name to human ensembl id in humans
colnames(pred_expr)[2:ncol(pred_expr)] <- orth.rats[match(colnames(pred_expr)[2:ncol(pred_expr)], orth.rats$rnorvegicus_homolog_ensembl_gene), 1] %>% .[["ensembl_gene_id"]]Then we reformat the PTRS weight files, removing the Ensembl version from gene names.
fn_weights = function(trait)
{
weights <- read_tsv(data.dir %&% "PTRS_weights/weight_files/elastic_net_alpha_0.1_British.export_model/weights." %&% trait %&% ".tsv.gz")
weights$gene_id <- sapply(strsplit(weights$gene_id, "\\."), `[`, 1)
rownames(weights) <- weights$gene_id
weights <- weights %>% rename(gene_name = gene_id)
return(weights)
}We converted the predicted expression for rat genes to corresponding human gene names, matching the PTRS gene names. This lets us combine PTRS weights, trained from human transcriptomic data, with predicted transciptome of the rats using the fn_generate_trait function below. The output is the predicted height for individual rats.
In some ways, we can interpret generate_trait as the opposite of PrediXcan. Both start from the predicted transcriptome of a group of individuals, PrediXcan works backwards from values of their observed trait to compute association between genes and the trait, whereas fn_generate_trait assumes those associations to predict the trait for each individual. PTRS is particularly insightful in this case, because of its portability across different population groups. We hope this extends across species, motivating our final goal of comparing the performance of PTRS in humans and rats.
for(trait in traits) {
weights <- fn_weights(trait)
pred_trait <- fn_generate_trait(pred_expr, weights)
saveRDS(pred_trait, data.dir %&% "rat_pred_" %&% trait %&% "_w_Human_best_PTRS.RDS")
return(pred_trait)
}
sessionInfo()R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16
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] glmnet_4.1-3 Matrix_1.2-18 RSQLite_2.2.1 data.table_1.13.2
[5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
[9] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.6
[13] tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] fs_1.5.0 usethis_1.6.3 lubridate_1.7.9 devtools_2.3.2
[5] bit64_4.0.5 httr_1.4.2 rprojroot_1.3-2 tools_4.0.3
[9] backports_1.1.10 bslib_0.3.1 R6_2.4.1 DBI_1.1.0
[13] colorspace_1.4-1 withr_2.3.0 tidyselect_1.1.0 prettyunits_1.1.1
[17] processx_3.4.4 bit_4.0.4 curl_4.3 compiler_4.0.3
[21] git2r_0.27.1 cli_3.3.0 rvest_0.3.6 xml2_1.3.2
[25] desc_1.2.0 sass_0.4.1 scales_1.1.1 callr_3.5.1
[29] digest_0.6.27 rmarkdown_2.14 pkgconfig_2.0.3 htmltools_0.5.2
[33] sessioninfo_1.1.1 dbplyr_1.4.4 fastmap_1.1.0 rlang_1.0.2
[37] readxl_1.3.1 rstudioapi_0.11 shape_1.4.6 jquerylib_0.1.4
[41] generics_0.0.2 jsonlite_1.7.1 magrittr_1.5 Rcpp_1.0.8.3
[45] munsell_0.5.0 lifecycle_0.2.0 stringi_1.5.3 yaml_2.2.1
[49] pkgbuild_1.1.0 grid_4.0.3 blob_1.2.1 promises_1.1.1
[53] crayon_1.3.4 lattice_0.20-41 haven_2.3.1 splines_4.0.3
[57] hms_0.5.3 knitr_1.39 ps_1.4.0 pillar_1.4.6
[61] codetools_0.2-16 pkgload_1.1.0 reprex_0.3.0 glue_1.6.2
[65] evaluate_0.15 remotes_2.2.0 modelr_0.1.8 vctrs_0.4.1
[69] httpuv_1.5.4 foreach_1.5.2 testthat_2.3.2 cellranger_1.1.0
[73] gtable_0.3.0 assertthat_0.2.1 xfun_0.31 broom_0.8.0
[77] later_1.1.0.1 survival_3.2-7 iterators_1.0.14 memoise_1.1.0
[81] workflowr_1.6.2 ellipsis_0.3.2