Last updated: 2022-02-11
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Knit directory: snRNA_eqtl/
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library(tidyverse)dir.create('output/tables',showWarnings = FALSE)Manually created
matched_snps <- read_tsv('output/eqtl/eqtl.PC70.metabrain.matched.txt') %>% dplyr::select(cell_type,sid=SNP,ensembl,beta_metabrain,p_metabrain,Replication)Parsed with column specification:
cols(
SNP_id_hg38 = col_character(),
ensembl = col_character(),
AlleleAssessed = col_character(),
beta_metabrain = col_double(),
se_metabrain = col_double(),
p_metabrain = col_double(),
cell_type = col_character(),
symbol = col_character(),
SNP = col_character(),
chr_hg38 = col_double(),
effect_allele = col_character(),
other_allele = col_character(),
slope = col_double(),
se = col_double(),
bpval = col_double(),
adj_p = col_double(),
p_rep_adj = col_double(),
Replication = col_character()
)snp_alleles <- data.table::fread('data_sensitive/genotypes/processed/plink.frq',data.table=FALSE) %>% dplyr::select(sid=SNP,effect_allele=A1,other_allele=A2) %>% as_tibble()tableS2 <- read_tsv('output/eqtl/eqtl.PC70.txt') %>%
separate(pid,into=c('symbol','ensembl'),sep='_') %>%
inner_join(.,snp_alleles,by='sid') %>%
left_join(.,matched_snps,by=c('cell_type','sid','ensembl')) %>%
dplyr::select(cell_type,symbol,ensembl,SNP=sid,effect_allele,other_allele,dist_TSS=dist,beta=slope,bpval,adj_p,beta_metabrain,p_metabrain,Replication) %>%
write_csv(.,'output/tables/tableS2.csv')snp_pos <- read_tsv('data_sensitive/genotypes/processed/snp_pos_hg38.txt',col_names=FALSE) %>% dplyr::rename(CHROM=X1,POS=X2,SNP=X3)tableS3 <- read_tsv('output/eqtl/eqtl.70PCs.caveman.txt') %>%
separate(GENE,into=c('symbol','ensembl','chr','pos_top_cis_eQTL','A1_eqtl','A2_eqtl'),sep='_') %>%
dplyr::select(cell_type,symbol,ensembl,CHROM,POS,CaVEMaN,Probability) %>% arrange(-Probability) %>%
unique() %>%
left_join(.,snp_pos,by=c('CHROM','POS')) %>%
dplyr::select(cell_type,symbol,ensembl,SNP,chr_hg38=CHROM,pos_hg38=POS,CaVEMaN,Probability) %>%
write_csv(.,'output/tables/tableS3.csv')tableS4 <- read_tsv('output/eqtl_specific/eqtl.PC70.specific.txt') %>%
#Sets pvalue to NA if the model did not converge or if the model output is Inf
mutate(nb_pvalue_aggregate=ifelse(nb_pvalue_aggregate_model_converged==FALSE,NA,nb_pvalue_aggregate)) %>%
mutate(nb_pvalue_at_least_one=ifelse(nb_pvalue_at_least_one_model_converged==FALSE,NA,nb_pvalue_at_least_one)) %>%
#Get adjusted pvalues
mutate(nb_pvalue_aggregate_adj=p.adjust(nb_pvalue_aggregate,method='fdr'),
nb_pvalue_at_least_one_adj=p.adjust(nb_pvalue_at_least_one,method = 'fdr')) %>%
#For each row, get the maximum pvalue across all cell types, this will be the gene-level pvalue testing whether the genetic effect on gene expression is different than all other cell types
rowwise() %>%
mutate(nb_pvalue_sig_all=max(Astrocytes_p,`Endothelial cells_p`,`Excitatory neurons_p`,`Inhibitory neurons_p`,Microglia_p,Oligodendrocytes_p,`OPCs / COPs_p`,Pericytes_p,na.rm=TRUE)) %>%
ungroup() %>%
#Sets pvalue to NA if the model did not converge
mutate(nb_pvalue_sig_all=ifelse(nb_pvalue_at_least_one_model_converged==FALSE | nb_pvalue_sig_all==Inf | nb_pvalue_sig_all==-Inf,NA,nb_pvalue_sig_all)) %>%
mutate(nb_pvalue_all_adj = p.adjust(nb_pvalue_sig_all,method='fdr')) %>%
separate(gene_id,into=c('symbol','ensembl'),sep='_') %>%
dplyr::select(cell_type_id,symbol,ensembl,
snp_id,nb_pvalue_aggregate,nb_pvalue_aggregate_adj,nb_pvalue_at_least_one, nb_pvalue_at_least_one_adj,nb_pvalue_all=nb_pvalue_sig_all, nb_pvalue_all_adj,nb_pvalue_aggregate_model_converged,
nb_pvalue_at_least_one_model_converged,Astrocytes_p:Pericytes_p) %>%
arrange(nb_pvalue_aggregate) %>%
write_csv('output/tables/tableS4.csv')read_tsv('output/coloc/coloc.ms.txt') %>% write_csv('output/tables/tableS5.ms.csv')
read_tsv('output/coloc/coloc.ad.txt') %>% write_csv('output/tables/tableS5.ad.csv')
read_tsv('output/coloc/coloc.scz.txt') %>% write_csv('output/tables/tableS5.scz.csv')
read_tsv('output/coloc/coloc.pd.txt') %>% write_csv('output/tables/tableS5.pd.csv')read_tsv('output/coloc/coloc.ms.gtex_dice.txt') %>% write_csv('output/tables/tableS6.csv')read_csv('output/gwas_epigenome_overlap/GWAS_epigenomic_marks.closest_gene.closest_coloc.filtered.SNP2TFBS.txt') %>% write_csv('output/tables/tableS7.csv')
sessionInfo()R version 4.0.1 (2020-06-06)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /pstore/apps/OpenBLAS/0.3.1-GCC-7.3.0-2.30/lib/libopenblasp-r0.3.1.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0 purrr_0.3.4
[5] readr_1.3.1 tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.3
[9] tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.0 xfun_0.14 haven_2.3.1 lattice_0.20-41
[5] colorspace_1.4-1 vctrs_0.3.1 generics_0.0.2 htmltools_0.5.1.1
[9] yaml_2.2.1 blob_1.2.1 rlang_0.4.10 later_1.1.0.1
[13] pillar_1.4.4 withr_2.2.0 glue_1.4.1 DBI_1.1.0
[17] dbplyr_1.4.4 modelr_0.1.8 readxl_1.3.1 lifecycle_0.2.0
[21] munsell_0.5.0 gtable_0.3.0 cellranger_1.1.0 rvest_0.3.5
[25] evaluate_0.14 knitr_1.28 httpuv_1.5.4 fansi_0.4.1
[29] broom_0.5.6 Rcpp_1.0.6 promises_1.1.1 backports_1.1.7
[33] scales_1.1.1 jsonlite_1.6.1 fs_1.4.1 hms_0.5.3
[37] digest_0.6.25 stringi_1.4.6 grid_4.0.1 rprojroot_1.3-2
[41] cli_2.0.2 tools_4.0.1 magrittr_1.5 crayon_1.3.4
[45] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1 data.table_1.12.8
[49] xml2_1.3.2 reprex_0.3.0 lubridate_1.7.9 rstudioapi_0.11
[53] assertthat_0.2.1 rmarkdown_2.2 httr_1.4.1 R6_2.4.1
[57] nlme_3.1-148 git2r_0.27.1 compiler_4.0.1