Last updated: 2022-02-10
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Knit directory: snRNA_eqtl/
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dir.create('output/shiny',showWarnings = FALSE)hdF5_file_path <- "output/shiny/data.h5"library(tidyverse)
library(rhdf5)sum_expression_ms <- readRDS('data_sensitive/expression/ms_sum_expression.individual_id.rds') %>%
mutate(dataset='ms')
sum_expression_ad <- readRDS('data_sensitive/expression/ad_sum_expression.individual_id.rds') %>%
mutate(dataset='ad')sum_expression <- rbind(sum_expression_ms,sum_expression_ad)sum_expression <- sum_expression %>%
group_by(individual_id,cell_type) %>%
mutate(libsize=sum(counts)) %>%
mutate(cpm=counts*10^6/libsize) %>%
mutate(log2_cpm=log2(cpm+1)) %>%
ungroup() %>%
mutate(gene=paste0(symbol,'_',ensembl))Only keep individuals, cell type with at least 10 cells
sum_expression <- filter(sum_expression,n_cells>10)h5createFile(hdF5_file_path)[1] TRUEh5createGroup(hdF5_file_path,"expression")[1] TRUEh5createGroup(hdF5_file_path,"genotype")[1] TRUEh5createGroup(hdF5_file_path,"eqtl_results")[1] TRUEh5createGroup(hdF5_file_path,"annotations")[1] TRUEh5createGroup(hdF5_file_path,"coloc")[1] TRUEsum_expression %>% group_by(gene) %>%
do(write=h5write(.[,c('cell_type','individual_id','log2_cpm')],
hdF5_file_path,paste0("expression/",unique(.$gene))))# A tibble: 25,938 x 2
# Rowwise:
gene write
<chr> <list>
1 A1BG_ENSG00000121410 <dbl [1]>
2 A1CF_ENSG00000148584 <dbl [1]>
3 A2M_ENSG00000175899 <dbl [1]>
4 A2ML1_ENSG00000166535 <dbl [1]>
5 A3GALT2_ENSG00000184389 <dbl [1]>
6 A4GALT_ENSG00000128274 <dbl [1]>
7 A4GNT_ENSG00000118017 <dbl [1]>
8 AAAS_ENSG00000094914 <dbl [1]>
9 AACS_ENSG00000081760 <dbl [1]>
10 AADAC_ENSG00000114771 <dbl [1]>
# … with 25,928 more rowsd <- read_tsv('output/eqtl/eqtl.PC70.txt') %>%
dplyr::select(cell_type,gene=pid,SNP=sid,slope,adj_p) %>%
mutate(slope=round(slope,digits=2),adj_p=signif(adj_p,digits=3))h5write(d, hdF5_file_path,"eqtl_results/eqtl_results_all")vcf <- data.table::fread("data_sensitive/genotypes/processed/combined_final.vcf.gz",
data.table=FALSE) %>%
as_tibble()vcf_filt <- filter(vcf,ID%in%d$SNP) %>%
gather(individual,genotype,10:201) %>%
mutate(genotype_parsed=case_when(
genotype=='1/1' ~ 2,
genotype=='0/1' ~ 1,
genotype=='1/0' ~ 1,
genotype=='0/0' ~ 0,
)) %>%
as_tibble() %>%
dplyr::select(ID,individual,REF,ALT,genotype_parsed) %>%
dplyr::rename(genotype=genotype_parsed) vcf_filt %>% group_by(ID) %>%
do(write=h5write(.[,c('individual','REF','ALT','genotype')],
hdF5_file_path,paste0("genotype/",unique(.$ID))))# A tibble: 77,887 x 2
# Rowwise:
ID write
<chr> <list>
1 chr1:120288148_G_A <dbl [1]>
2 chr1:144872614_G_C <dbl [1]>
3 chr1:15670060_C_G <dbl [1]>
4 chr1:201330714_G_T <dbl [1]>
5 chr1:22932928_C_G <dbl [1]>
6 chr10:100742917_G_C <dbl [1]>
7 chr10:103719262_C_G <dbl [1]>
8 chr10:17991504_A_G <dbl [1]>
9 chr10:26666405_A_G <dbl [1]>
10 chr10:36742286_G_C <dbl [1]>
# … with 77,877 more rowsd <- read_tsv('output/eqtl_specific/eqtl.PC70.specific.txt') %>%
#Sets pvalue to NA if the model did not converge
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)) %>%
filter(!is.na(nb_pvalue_aggregate), #Remove genes for which the model did not converge (9 genes)
!is.na(nb_pvalue_at_least_one),
nb_pvalue_at_least_one!=Inf) %>%
#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() %>%
mutate(nb_pvalue_all_adj = p.adjust(nb_pvalue_sig_all,method='fdr')) %>%
dplyr::select(cell_type=cell_type_id,
gene=gene_id,
SNP=snp_id,
nb_pvalue_aggregate_adj,
nb_pvalue_at_least_one_adj,
nb_pvalue_all_adj) %>%
mutate(nb_pvalue_aggregate_adj=signif(nb_pvalue_aggregate_adj,digits=3),
nb_pvalue_at_least_one_adj=signif(nb_pvalue_at_least_one_adj,digits=3),
nb_pvalue_all_adj=signif(nb_pvalue_all_adj,digits=3))h5write(d, hdF5_file_path,"eqtl_results/eqtl_results_specific")filter_coloc <- function(d,threshold=0.5){
genes2keep <- d %>% filter(PP.H4.abf>threshold) %>% pull(ensembl)
d <- filter(d,ensembl%in%genes2keep)
return(d)
}ad <- read_tsv('output/coloc/coloc.ad.txt') %>% filter_coloc()
pd <- read_tsv('output/coloc/coloc.pd.txt')%>% filter_coloc()
scz <- read_tsv('output/coloc/coloc.scz.txt')%>% filter_coloc()
ms <- read_tsv('output/coloc/coloc.ms.txt')%>% filter_coloc()
ms_gtex_dice <- read_tsv('output/coloc/coloc.ms.gtex_dice.txt') %>% filter_coloc()h5write(ad, hdF5_file_path,"coloc/ad")
h5write(pd, hdF5_file_path,"coloc/pd")
h5write(scz, hdF5_file_path,"coloc/scz")
h5write(ms, hdF5_file_path,"coloc/ms")
h5write(ms_gtex_dice, hdF5_file_path,"coloc/ms_gtex_dice")
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] rhdf5_2.32.4 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.4 readr_1.3.1 tidyr_1.1.0 tibble_3.0.1
[9] ggplot2_3.3.3 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 lubridate_1.7.9 lattice_0.20-41 assertthat_0.2.1
[5] rprojroot_1.3-2 digest_0.6.25 utf8_1.1.4 R6_2.4.1
[9] cellranger_1.1.0 backports_1.1.7 reprex_0.3.0 evaluate_0.14
[13] httr_1.4.1 pillar_1.4.4 rlang_0.4.10 readxl_1.3.1
[17] rstudioapi_0.11 data.table_1.12.8 whisker_0.4 blob_1.2.1
[21] R.oo_1.23.0 R.utils_2.9.2 rmarkdown_2.2 munsell_0.5.0
[25] broom_0.5.6 compiler_4.0.1 httpuv_1.5.4 modelr_0.1.8
[29] xfun_0.14 pkgconfig_2.0.3 htmltools_0.5.1.1 tidyselect_1.1.0
[33] workflowr_1.6.2 fansi_0.4.1 crayon_1.3.4 dbplyr_1.4.4
[37] withr_2.2.0 later_1.1.0.1 R.methodsS3_1.8.0 grid_4.0.1
[41] nlme_3.1-148 jsonlite_1.6.1 gtable_0.3.0 lifecycle_0.2.0
[45] DBI_1.1.0 git2r_0.27.1 magrittr_1.5 scales_1.1.1
[49] cli_2.0.2 stringi_1.4.6 fs_1.4.1 promises_1.1.1
[53] xml2_1.3.2 ellipsis_0.3.1 generics_0.0.2 vctrs_0.3.1
[57] Rhdf5lib_1.10.0 tools_4.0.1 glue_1.4.1 hms_0.5.3
[61] yaml_2.2.1 colorspace_1.4-1 rvest_0.3.5 knitr_1.28
[65] haven_2.3.1