Last updated: 2020-06-04
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Knit directory: QGT-Columbia-HKI/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d65c555 | Hae Kyung Im | 2020-06-04 | twmr |
| html | d65c555 | Hae Kyung Im | 2020-06-04 | twmr |
| html | 682b6e2 | Hae Kyung Im | 2020-06-04 | Build site. |
| Rmd | d3502e8 | Hae Kyung Im | 2020-06-04 | wflow_publish(“analysis/analysis_plan.Rmd”) |
| Rmd | 4420fc7 | Hae Kyung Im | 2020-06-04 | wflow_rename(“analysis/predixcan_analysis.Rmd”, “analysis/analysis_plan.Rmd”) |
| html | 4420fc7 | Hae Kyung Im | 2020-06-04 | wflow_rename(“analysis/predixcan_analysis.Rmd”, “analysis/analysis_plan.Rmd”) |
library(tidyverse)── Attaching packages ───────────────────────────────────────────────────────── tidyverse 1.3.0 ──✓ ggplot2 3.3.0 ✓ purrr 0.3.4
✓ tibble 3.0.0 ✓ dplyr 0.8.5
✓ tidyr 1.0.2 ✓ stringr 1.4.0
✓ readr 1.3.1 ✓ forcats 0.5.0── Conflicts ──────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag() masks stats::lag()Data and copies of repositories can be downloaded from Box here
The latest version of the analysis plan that generated this page is on github here
print(getwd())[1] "/Users/haekyungim/Github/QGT-Columbia-HKI"
export DATA=$PRE/s-predixcan/data
export MODEL=$PRE/models
export METAXCAN=$PRE/MetaXcan-master/software
export RESULTS=$PRE/results
printf "Predict expression\n\n"
python3 $METAXCAN/Predict.py \
--model_db_path $DATA/models/gtex_v8_en/en_Whole_Blood.db \
--vcf_genotypes $DATA/1000G_hg38/ALL.chr22.shapeit2_integrated_snvindels_v2a_27022019.GRCh38.phased.vcf.gz \
--vcf_mode genotyped \
--variant_mapping $DATA/gtex_v8_eur_filtered_maf0.01_monoallelic_variants.txt.gz id rsid \
--on_the_fly_mapping METADATA "chr{}_{}_{}_{}_b38" \
--prediction_output $RESULTS/vcf_1000G_hg38_en/Whole_Blood__predict.txt \
--prediction_summary_output $RESULTS/vcf_1000G_hg38_en/Whole_Blood__summary.txt \
--verbosity 9 \
--throw## merge with GEUVADIS expression data
## calculate spearman correlation
## select a few genes and plot predicted vs observed expressionprintf "association\n\n"
python3 $METAXCAN/PrediXcanAssociation.py \
--expression_file $RESULTS/vcf_1000G_hg38_en/Whole_Blood__predict.txt \
--input_phenos_file $DATA/1000G_hg38/random_pheno_1000G_hg38.txt \
--input_phenos_column pheno \
--output $RESULTS/vcf_1000G_hg38_en/Whole_Blood__association.txt \
--verbosity 9 \
--throw
export PRE=/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI
export DATA=$PRE/s-predixcan/data
export MODEL=$PRE/models
export METAXCAN=$PRE/MetaXcan-master/software
export OUTPUT=$PRE/results
echo $PRE
echo $DATA
echo $MODEL
echo $METAXCAN
echo $OUTPUT
/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI
/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI/s-predixcan/data
/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI/models
/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI/MetaXcan-master/software
/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI/results
export PRE=/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI
export DATA=$PRE/s-predixcan/data
export MODEL=$PRE/models
export METAXCAN=$PRE/MetaXcan-master/software
export OUTPUT=$PRE/results
python $METAXCAN/SPrediXcan.py \
--gwas_file $DATA/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz \
--snp_column panel_variant_id --effect_allele_column effect_allele --non_effect_allele_column non_effect_allele --zscore_column zscore \
--model_db_path $MODEL/gtex_v8_mashr/mashr_Whole_Blood.db \
--covariance $MODEL/gtex_v8_mashr/mashr_Whole_Blood.txt.gz \
--keep_non_rsid --additional_output --model_db_snp_key varID \
--throw \
--output_file $OUTPUT/spredixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE__PM__Whole_Blood.csv
python $METAXCAN/SMulTiXcan.py \
--models_folder $DATA/models/eqtl/mashr \
--models_name_pattern "mashr_(.*).db" \
--snp_covariance $DATA/models/gtex_v8_expression_mashr_snp_covariance.txt.gz \
--metaxcan_folder $OUTPUT/spredixcan/eqtl/ \
--metaxcan_filter "CARDIoGRAM_C4D_CAD_ADDITIVE__PM__(.*).csv" \
--metaxcan_file_name_parse_pattern "(.*)__PM__(.*).csv" \
--gwas_file $OUTPUT/processed_summary_imputation/imputed_CARDIoGRAM_C4D_CAD_ADDITIVE.txt.gz \
--snp_column panel_variant_id --effect_allele_column effect_allele --non_effect_allele_column non_effect_allele --zscore_column zscore --keep_non_rsid --model_db_snp_key varID \
--cutoff_condition_number 30 \
--verbosity 7 \
--throw \
--output $OUTPUT/smultixcan/eqtl/CARDIoGRAM_C4D_CAD_ADDITIVE_smultixcan.txt
We will run torus due to time limitation but ideally we would like to run a method that allows multiple causal variants per locus.
#torus -d Height.torus.zval.gz --load_zval -dump_pip Height.gwas.pip
#gzip Height.gwas.pip
torus -d /Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI/fastenloc/data/Height.torus.zval.gz --load_zval -dump_pip /Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI/fastenloc/data/Height.gwas.pip
gzip Height.gwas.pip
is this done internally by fastENLOC?
## tutorial https://github.com/xqwen/fastenloc/tree/master/tutorial
export EQTLGZ=eqtl_annotation_gzipped
export GWASGZ=gwas_data_gzipped
export TISSUE=Whole_Blood
fastenloc -eqtl EQTLGZ -gwas GWASGZ -t tissue_name #[-total_variants total_snp] [-thread n] [-prefix prefix_name] [-s shrinkage]
## optional - compare with s-predixcan results
cd $TWMR
export PRE=/Users/haekyungim/Box/LargeFiles/QGT-Columbia-HKI
export TWMR=$PRE/repos/TWMR-master
export OUTPUT=$PRE/results
GENE=ENSG00000002919
R < $TWMR/MR.R --no-save $GENE
cd $PRE
-[ ] todo: write an R function
sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2
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] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.5 purrr_0.3.4
[5] readr_1.3.1 tidyr_1.0.2 tibble_3.0.0 ggplot2_3.3.0
[9] tidyverse_1.3.0
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 rlang_0.4.5 later_1.0.0 pillar_1.4.3
[13] withr_2.1.2 glue_1.4.1 DBI_1.1.0 dbplyr_1.4.2
[17] readxl_1.3.1 modelr_0.1.6 lifecycle_0.2.0 cellranger_1.1.0
[21] munsell_0.5.0 gtable_0.3.0 workflowr_1.6.2 rvest_0.3.5
[25] evaluate_0.14 knitr_1.28 httpuv_1.5.2 fansi_0.4.1
[29] broom_0.5.5 Rcpp_1.0.4.6 promises_1.1.0 backports_1.1.6
[33] scales_1.1.0 jsonlite_1.6.1 fs_1.4.1 hms_0.5.3
[37] digest_0.6.25 stringi_1.4.6 rprojroot_1.3-2 grid_3.6.3
[41] cli_2.0.2 tools_3.6.3 magrittr_1.5 crayon_1.3.4
[45] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.0 xml2_1.3.1
[49] reprex_0.3.0 lubridate_1.7.8 rstudioapi_0.11 assertthat_0.2.1
[53] rmarkdown_2.1 httr_1.4.1 R6_2.4.1 nlme_3.1-147
[57] git2r_0.26.1 compiler_3.6.3