Covariances EA hg38
::: {.container-fluid .main-container} ::: {#header .fluid-row} # covariances_hg38 {#covariances_hg38 .title .toc-ignore}
2021-06-04
:::
workflowr
::: tab-content ::: {#summary .tab-pane .fade .in .active} Last updated: 2021-09-08
Checks: 6 1
Knit directory: ~/Github/ARIC/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
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(12345) 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.
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:
Untracked files:
Untracked: .DS_Store
Untracked: .Rhistory
Untracked: ARIC_EA_hg38_validation.Rmd
Untracked: ARIC_EA_hg38_validation.html
Untracked: ARIC_EA_hg38_validation_height.Rmd
Untracked: ARIC_EA_hg38_validation_height.html
Untracked: PWAS/
Untracked: code/
Untracked: covariances_EA_hg38.Rmd
Untracked: figure/
Untracked: models/
Untracked: results/
Untracked: test_data/
Untracked: weights_EA.Rmd
Untracked: weights_EA.htmlNote 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.
There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.
| ::: |
|---|
| title: Covariances EA hg38 author: Sabrina Mi date: ‘2021-09-08’ |
covariances_hg38
2021-06-04
workflowr
Last updated: 2021-09-08
Checks: 6 1
Knit directory: ~/Github/ARIC/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
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(12345) 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.
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:
Untracked files:
Untracked: .DS_Store
Untracked: .Rhistory
Untracked: ARIC_EA_hg38_validation.Rmd
Untracked: ARIC_EA_hg38_validation.html
Untracked: ARIC_EA_hg38_validation_height.Rmd
Untracked: ARIC_EA_hg38_validation_height.html
Untracked: PWAS/
Untracked: code/
Untracked: covariances_EA_hg38.Rmd
Untracked: figure/
Untracked: models/
Untracked: results/
Untracked: test_data/
Untracked: weights_EA.Rmd
Untracked: weights_EA.htmlNote 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.
There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.
Download Data
covariance_for_model.py takes genotypes in parquet format. Run git clone https://github.com/hakyimlab/summary-gwas-imputation.git. The data can be downloaded: https://zenodo.org/record/3569954#.XyRiqChKiUk. Or in CRI: /gpfs/data/im-lab/nas40t2/Data/1000G_hg38_EUR_maf0.01_parquet
Calculating Covariance
CODE=/Users/t.med.scmi/Github/summary-gwas-imputation
DATA=/gpfs/data/im-lab/nas40t2/Data/1000G_hg38_EUR_maf0.01_parquet
MODEL=/gpfs/data/im-lab/nas40t2/sabrina/ARICparquet_genotype_pattern helps identify genotype files by chromosome. ARIC_EA_hg38.db is a PrediXcan format prediction model defined in hg38. The script can also be submitted as a job in CRI: /gpfs/data/im-lab/nas40t2/sabrina/ARIC/covariances_1000G_hg38.sh
python $CODE/covariance_for_model.py \
-parquet_genotype_folder $DATA \
-parquet_genotype_pattern "chr(.*).variants.parquet" \
-model_db $MODEL/ARIC_EA_hg38.db \
-output $MODEL/ARIC_EA_hg38.txt.gz \
-parsimony 1
Session information
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_1.0.5 rstudioapi_0.11 knitr_1.30 magrittr_1.5
[5] workflowr_1.6.2 R6_2.4.1 rlang_0.4.8 stringr_1.4.0
[9] tools_4.0.3 xfun_0.18 git2r_0.27.1 htmltools_0.5.0
[13] ellipsis_0.3.1 yaml_2.2.1 digest_0.6.27 rprojroot_1.3-2
[17] tibble_3.0.4 lifecycle_0.2.0 crayon_1.3.4 later_1.1.0.1
[21] vctrs_0.3.4 promises_1.1.1 fs_1.5.0 glue_1.4.2
[25] evaluate_0.14 rmarkdown_2.5 stringi_1.5.3 compiler_4.0.3
[29] pillar_1.4.6 backports_1.1.10 httpuv_1.5.4 pkgconfig_2.0.3 ```````````````````