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config.example.yaml
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config.example.yaml
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# ------------------------- Input/Output paths ------------------------------- #
# for the paths, please use full path to avoid issues
datadir: 'path to brainxcan data'
outdir: 'output directory'
prefix: 'output prefix'
brainxcan_path: 'path to brainxcan repo'
# ------------------------- GWAS formatting information ---------------------- #
gwas: 'path to GWAS file (format: csv(.gz), tsv(.gz), parquet'
snpid: 'column name of GWAS SNP RSID'
effect_allele: 'column name of GWAS effect allele'
non_effect_allele: 'column name of GWAS non-effect allele'
chr: 'column name of chromosome'
position: 'column name of position' # required if using LD block permutation
# option 1 (high priority)
# Note: we assume effect is centered around zero, so use log(OR) for case control study
effect_size: 'column name of GWAS effect size'
effect_size_se: 'column name of GWAS effect size standard error'
# option 2
zscore: 'column name of GWAS z-score'
sample_size: 'column name of GWAS sample size'
allele_frequency: 'column name of allele frequency'
# ------------------------- Generating BrainXcan region ---------------------- #
# if want to generate interactive html to present the regions
bxcan_region_vis: True # or False
# ------------------------- Optional: default values are listed -------------- #
# no need to specify if you'd like to go with default
# ancestry population of the gwas (used for MR). Options are populations in 1000G: AFR, AMR, EAS, EUR, SAS
gwas_pop: 'EUR'
# IDP prediction model type: ridge or elastic_net
model_type: 'ridge'
# IDP sets to use: original or residual (after PC adjustment)
idp_type: 'residual'
# CV Spearman cutoff on models (only models passing this criteria will be shown)
spearman_cutoff: 0.1
# parameters to define signif BrainXcan results for MR
signif_pval: 5e-2
signif_max_idps: 10
# parameters used in defining instrument in MR
ld_clump_yaml: '{datadir}/mr/ld_clump.yaml'
# path to R and Python
rscript_exe: 'Rscript'
python_exe: 'python'
plink_exe: 'plink'
# generate empirical zscores with simulated weights in BrainXcan
bxcan_empirical_z: False
bxcan_empirical_z_seed: 1
bxcan_empirical_z_nrepeat: 1000
correction_factor_emp: 1
# generate empirical zscores with LD block-based permutation in BrainXcan
## path to the BED file (TAB-delimited base0 with header chr, start, stop)
bxcan_ldblock_perm: null
bxcan_ldblock_perm_seed: 1
bxcan_ldblock_perm_nrepeat: 10
correction_factor_perm: 1.1
# -------------------------- ABOUT DEPENDENT DATA --------------------------- #
# specify dependent data separately
# don't recommend changing typically
# only need when you want to use customized data in a nasty way
# bxcan_idp_meta: ''
# bxcan_color_code: ''
# bxcan_vis_datadir: ''
# geno_cov_pattern: ''
# idp_weights_pattern: ''
# idp_gwas_pattern: ''
# idp_gwas_snp_pattern: ''
# idp_weights_cols:
# snpid: ''
# effect_allele: ''
# non_effect_allele: ''
# chr: ''