Last updated: 2022-08-23
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Knit directory: RatXcan_Training/
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Predict gene expression using elastic net model trained on Ac tissue data.
conda activate imlabtools
# The path to Box drive is usually ~/Box, but may be different depending on operating system and Box Drive version
DIR=/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline
METAXCAN=/Users/sabrinami/Github/MetaXcan/software
GENO=$DIR/data/rat_genotypes_LD_pruned_0.95
MODEL=$DIR/Results/sql
OUTPUT=$DIR/Results/PrediXcan/metabolic_traitspython $METAXCAN/Predict.py \
--model_db_path $MODEL/Ac_best_prediXcan_db.db \
--text_genotypes \
$GENO/chr*.round2_impute2_3473.txt \
--on_the_fly_mapping METADATA "{}_{}_{}_{}" \
--text_sample_ids $GENO/samples_Rat_metab_phenos_file \
--prediction_output $OUTPUT/rat_metabolic_Ac_best__predict.txt \
--prediction_summary_output $OUTPUT/rat_metabolic_Ac_best__summary.txt \
--throw
python $METAXCAN/Predict.py \
--model_db_path $MODEL/Il_best_prediXcan_db.db \
--text_genotypes \
$GENO/chr*.round2_impute2_3473.txt \
--on_the_fly_mapping METADATA "{}_{}_{}_{}" \
--text_sample_ids $GENO/samples_Rat_metab_phenos_file \
--prediction_output $OUTPUT/rat_metabolic_Il_best__predict.txt \
--prediction_summary_output $OUTPUT/rat_metabolic_Il_best__summary.txt \
--throw
python $METAXCAN/Predict.py \
--model_db_path $MODEL/Lh_best_prediXcan_db.db \
--text_genotypes \
$GENO/chr*.round2_impute2_3473.txt \
--on_the_fly_mapping METADATA "{}_{}_{}_{}" \
--text_sample_ids $GENO/samples_Rat_metab_phenos_file \
--prediction_output $OUTPUT/rat_metabolic_Lh_best__predict.txt \
--prediction_summary_output $OUTPUT/rat_metabolic_Lh_best__summary.txt \
--throw
python $METAXCAN/Predict.py \
--model_db_path $MODEL/Pl_best_prediXcan_db.db \
--text_genotypes \
$GENO/chr*.round2_impute2_3473.txt \
--on_the_fly_mapping METADATA "{}_{}_{}_{}" \
--text_sample_ids $GENO/samples_Rat_metab_phenos_file \
--prediction_output $OUTPUT/rat_metabolic_Pl_best__predict.txt \
--prediction_summary_output $OUTPUT/rat_metabolic_Pl_best__summary.txt \
--throw
python $METAXCAN/Predict.py \
--model_db_path $MODEL/Vo_best_prediXcan_db.db \
--text_genotypes \
$GENO/chr*.round2_impute2_3473.txt \
--on_the_fly_mapping METADATA "{}_{}_{}_{}" \
--text_sample_ids $GENO/samples_Rat_metab_phenos_file \
--prediction_output $OUTPUT/rat_metabolic_Vo_best__predict.txt \
--prediction_summary_output $OUTPUT/rat_metabolic_Vo_best__summary.txt \
--throw
library(readr)
library(tidyverse)
tissues=c("Ac", "Il", "Lh", "Pl", "Vo")
"%&%" = function(a,b) paste(a,b,sep="")
dir="/Users/sabrinami/Library/CloudStorage/Box-Box/imlab-data/data-Github/Rat_Genomics_Paper_Pipeline/"Before running association, we filter predicted expression and phenotype files so that individuals are listed in the same order.
res.dir = dir %&% "Results/prediXcan/metabolic_traits/"
meta.dir = "/Users/sabrinami/Github/MetaXcan/software"
pheno.dir = dir %&% "data/expression/"
fn_write_files = function(tis){
pred_expr <- read_tsv(dir %&% "Results/prediXcan/metabolic_traits/rat_metabolic_" %&% tis %&% "_best__predict.txt", col_names = TRUE)
all_rats <- read_tsv(pheno.dir %&% "all_names.txt", col_names = TRUE)
pred_expr <- pred_expr %>% filter(!(FID %in% all_rats$ID))
write_tsv(pred_expr, dir %&% "Results/prediXcan/metabolic_traits/rat_metabolic_" %&% tis %&% "_target_set__predict.txt", col_names = TRUE)
pheno <- read_csv(pheno.dir %&% "processed_obesity_rat_Palmer_phenotypes.csv", col_names = TRUE) %>% filter(!(rat_rfid %in% all_rats$ID))
write_tsv(pheno, pheno.dir %&% "processed_obesity_rat_Palmer_phenotypes_target_set.tsv", col_names = TRUE)
}for (tis in tissues) {
fn_write_files(tis)
}Now run an association test for each column of our phenotype file.
for (tis in tissues){
n_pheno=length(colnames(pheno))-1
# use all pheno columns except rat_rfid and idx
for(i in 2:n_pheno){
trait <- colnames(pheno)[i]
runASSOC <- "python3 " %&% meta.dir %&% "/PrediXcanAssociation.py " %&% "--expression_file " %&% res.dir %&% "rat_metabolic_" %&% tis %&% "_target_set__predict.txt --input_phenos_file " %&% pheno.dir %&% "processed_obesity_rat_Palmer_phenotypes_target_set.tsv " %&% "--input_phenos_column " %&% trait %&% " --output " %&% res.dir %&% tis %&% "__association_" %&% trait %&% ".txt --verbosity 9 --throw"
system(runASSOC)
}
}
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
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 rstudioapi_0.11 whisker_0.4 knitr_1.39
[5] magrittr_1.5 workflowr_1.6.2 R6_2.4.1 rlang_1.0.2
[9] fastmap_1.1.0 stringr_1.4.0 tools_4.0.3 xfun_0.31
[13] cli_3.3.0 git2r_0.27.1 jquerylib_0.1.4 htmltools_0.5.2
[17] ellipsis_0.3.2 rprojroot_1.3-2 yaml_2.2.1 digest_0.6.27
[21] tibble_3.0.4 lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1
[25] sass_0.4.1 vctrs_0.4.1 fs_1.5.0 promises_1.1.1
[29] glue_1.6.2 evaluate_0.15 rmarkdown_2.14 stringi_1.5.3
[33] compiler_4.0.3 bslib_0.3.1 pillar_1.4.6 backports_1.1.10
[37] jsonlite_1.7.1 httpuv_1.5.4 pkgconfig_2.0.3