Last updated: 2021-06-01
Checks: 2 0
Knit directory: PSYMETAB/
This reproducible R Markdown analysis was created with workflowr (version 1.6.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
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:
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Modified: code/extractions/master_extract.sh
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Modified: slurm_clustermq.tmpl
Note 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.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 41ada84 | jennysjaarda | 2021-05-10 | update to extract imputation metricts |
| html | 300900b | Jenny Sjaarda | 2021-04-13 | Build site. |
| Rmd | d0e0695 | jennysjaarda | 2021-03-02 | Merge branch ‘master’ of https://github.com/jennysjaarda/PSYMETAB |
| Rmd | 941b66d | Jenny Sjaarda | 2021-03-02 | add new Rmd files and respective html files |
| Rmd | a87444c | jennysjaarda | 2021-03-02 | update how to extract genetic data |
| html | 16d0800 | Jenny Sjaarda | 2020-09-10 | Build site. |
| html | 4b99157 | Jenny Sjaarda | 2020-09-10 | Build site. |
| Rmd | 299e192 | Jenny Sjaarda | 2020-09-10 | adding instructions for extracting genetic data |
| html | d53b378 | Jenny Sjaarda | 2020-09-08 | Build site. |
| Rmd | 8743745 | Jenny Sjaarda | 2020-09-08 | wflow_publish(“analysis/extract_genetic_data.Rmd”) |
The following document outlines the procedure to extract genetic data located on the HPC1 servers.
${snp_list}).HPC1 server via filezilla:hpc1.chuv.chje4649XXXX22/data/sgg2/jenny/projects/PSYMETAB/data/raw/extractionsrequestor) with the format: FirstnameLastname./data/sgg2/jenny/projects/PSYMETAB/data/raw/extractions/${requestor} (Note: to maintain file organization, please maintain only one folder per requestor).HPC1 using PUTTY:je4649@hpc1.chuv.chXXXXsbatch $projects/PSYMETAB/code/extractions/master_extract.sh (this assumes projects is defined as /data/sgg2/jenny/projects, if not replace $projects with explicit path or define projects before hand, if logged in as je4649, project is automatically defined)./data/sgg2/jenny/projects/PSYMETAB/code/extractions/template_extract.sh./data/sgg2/jenny/projects/PSYMETAB/code/extractions/master_extract.sh) will automatically do the following:
/data/sgg2/jenny/projects/PSYMETAB/code/extractions/${requestor}.sh.FirstnameLastname in Template with new “requestor” (e.g. replace Aurelie with FirstnameLastname), there should be four replace instances: (1) SBATCH job-name, (2) folder input, (3) folder ouput, (4) comment at end of file indicating how to launch the script.sbatch job./data/sgg2/jenny/projects/PSYMETAB/data/processed/extractions/${resquester}/${snp_list}${QC_dir}/15_final_processing/FULL/PSYMETAB_GWAS.FULL/data/sgg2/jenny/projects/PSYMETAB/data/processed/extractions/${resquester}/${snp_list}.${snp_list}_rsids.txt: list of rsIDs with empty characters removed (in most cases this should be identical to the input file from #1).${snp_list}_SNP_HRC_info.txt: info file for each rsID, extracted from HRC list (see HRC website), columns correspond to: #CHROM POS ID REF ALT AC AN AF AC_EXCLUDING_1000G AN_EXCLUDING_1000G AF_EXCLUDING_1000G AA.${snp_list}_SNP_impute_info.txt: imputation info file for each rsID, extracted from imputed plink data (pvar file), columns correspond to: #CHROM POS ID REF ALT FILTER INFO. See plink2 pvar website for full description of this file. This file should be used if imputation metrics are required.$chr), extracted from imputed data, there are the following files from extracting the chromosome specific genetic data using PLINK, specifically the raw file description can be found here:${snp_list}_chr${chr}.txt: list of rsIDs on chr${chr}${snp_list}_chr${chr}.psam${snp_list}_chr${chr}.pvar${snp_list}_chr${chr}.pgen${snp_list}_chr${chr}.pgen${snp_list}_chr${chr}.raw${snp_list}_chr${chr}.log${snp_list}_extraction.txt: merged genetic data from imputed files.${snp_list}_missing_snps.txt: list of SNPs that were not extracted from imputed files (if this file is empty, all SNPs were in imputed data set).${snp_list}_missing_snps_extract.fam: PLINK extraction from original genotype data.${snp_list}_missing_snps_extract.bim: PLINK extraction from original genotype data.${snp_list}_missing_snps_extract.bed: PLINK extraction from original genotype data.${snp_list}_missing_snps_extract.raw: PLINK extraction from original genotype data.${snp_list}_missing_snps_extract.log: PLINK extraction from original genotype data.${snp_list}_extraction_geno.txt: merged genetic data, same file as above, but with additional SNPs extracted from original genotype data (if necessary), this is the main results file:
GPCR ID.VariantID_countedallele, where VariantID is usually an “rs#” and countedallele represents the allele that is counted (data extracted using --raw flag in PLINK).CEU samples as this our largest and cleanest sample. If using only European, adjust for at least the first 10 PCs in your analysis. If you need to include additional ethnic groups, you have two options: (1) analyze each ethnic group seperately, adjusting for PCs or (2) analyze the entire group together without PCs (PCs were calculated in each ethnic group seperately so you cannot include them in a model where all ethnic groups are included).${snp_list}_missing_geno_snps.txt: list of SNPs that are still missing after extracting SNPs from original genotyped data.
${snp_list}_missing_snps_extract.bim.${snp_list}_extraction_geno.txt) to a CHUV computer via filezilla.requestor.