Last updated: 2022-03-02
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Knit directory: cTWAS_analysis/
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| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config.R | code/ctwas_config.R |
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#number of imputed weights
nrow(qclist_all)[1] 31542#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
2949 2110 1909 1116 1306 1374 1747 1105 1302 1325 1901 1686 587 1093 1113 1580
17 18 19 20 21 22
2203 399 2251 1027 412 1047 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 28610#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.907#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$z <- z_gene[ctwas_gene_res$intron_id,]$z
z_snp <- z_snp[z_snp$id %in% ctwas_snp_res$id,]
ctwas_snp_res$z <- z_snp$z[match(ctwas_snp_res$id, z_snp$id)]
#merge gene and snp results with added information
ctwas_snp_res$genename=NA
ctwas_snp_res$gene_type=NA
ctwas_snp_res$intron_id=NA
saveRDS(ctwas_gene_res, file = paste0(results_dir,"/",analysis_id,"_ctwas_gene_res.RDS"))
saveRDS(ctwas_snp_res, file = paste0(results_dir,"/",analysis_id,"_ctwas_snp_res.RDS"))ctwas_intron_res <- readRDS(paste0(results_dir,"/",analysis_id,"_ctwas_gene_res.RDS"))
ctwas_intron_res <- ctwas_intron_res[!is.na(ctwas_intron_res$genename),]
ctwas_snp_res <- readRDS(paste0(results_dir,"/",analysis_id,"_ctwas_snp_res.RDS"))
#get number of eQTL for inrtons
num_sqtl <- c()
for (i in 1:22){
load(paste0(results_dir, "/", analysis_id, "_expr_chr", i, ".exprqc.Rd"))
num_sqtl <- c(num_sqtl, unlist(lapply(wgtlist, nrow)))
}
ctwas_intron_res$num_sqtl <- num_sqtl[ctwas_intron_res$intron_id]library(dplyr)
by_genename <- ctwas_intron_res %>% group_by(genename)
ctwas_gene_res <- data.frame(by_genename %>% summarise(
chrom = chrom[1],
id = id[1],
pos = min(pos),
type = type[1],
region_tag1 = region_tag1[1],
region_tag2 = region_tag2[1],
cs_index = cs_index[1],
susie_pip = sum(susie_pip),
mu2 = mu2[which.max(abs(mu2))],
region_tag = region_tag[1],
PVE = sum(susie_pip*PVE),
z = z[which.max(abs(z))],
gene_type = type[1],
num_intron = length(intron_id),
num_sqtl = sum(num_sqtl)))
ctwas_snp_res$num_intron <- NA
ctwas_snp_res$num_sqtl <- NActwas_res <- rbind(ctwas_gene_res,
ctwas_snp_res[,colnames(ctwas_gene_res)])
#store columns to report
report_cols <- colnames(ctwas_gene_res)[!(colnames(ctwas_gene_res) %in% c("type", "region_tag1", "region_tag2", "cs_index", "gene_type", "z_flag", "id", "chrom", "pos"))]
first_cols <- c("genename", "region_tag")
report_cols <- c(first_cols, report_cols[!(report_cols %in% first_cols)])
report_cols_snps <- c("id", report_cols[-1])
report_cols_snps <- report_cols_snps[!(report_cols_snps %in% "num_sqtl")]
#get number of SNPs from s1 results; adjust for thin argument
ctwas_res_s1 <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.s1.susieIrss.txt"))
n_snps <- sum(ctwas_res_s1$type=="SNP")/thin
rm(ctwas_res_s1)#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior)
#estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var)
#report sample size
print(sample_size)
#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)
#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve)
#compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))#number of genes for gene set enrichment
length(genes)#number of genes in known annotations
print(length(known_annotations))
#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))
#significance threshold for TWAS
print(sig_thresh)
#number of ctwas genes
length(ctwas_genes)
#number of TWAS genes
length(twas_genes)
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]
#sensitivity / recall
print(sensitivity)
#specificity
print(specificity)
#precision / PPV
print(precision)
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] EnsDb.Hsapiens.v79_2.99.0 ensembldb_2.8.0
[3] AnnotationFilter_1.8.0 GenomicFeatures_1.36.3
[5] AnnotationDbi_1.46.0 Biobase_2.44.0
[7] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0
[9] IRanges_2.18.1 S4Vectors_0.22.1
[11] BiocGenerics_0.30.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.2 bit64_4.0.5
[3] getPass_0.2-2 highr_0.9
[5] blob_1.2.2 GenomeInfoDbData_1.2.1
[7] Rsamtools_2.0.0 yaml_2.2.1
[9] progress_1.2.2 pillar_1.6.4
[11] RSQLite_2.2.8 lattice_0.20-38
[13] glue_1.6.2 digest_0.6.29
[15] promises_1.0.1 XVector_0.24.0
[17] htmltools_0.5.2 httpuv_1.5.1
[19] Matrix_1.2-18 XML_3.99-0.3
[21] pkgconfig_2.0.3 biomaRt_2.40.1
[23] zlibbioc_1.30.0 processx_3.5.2
[25] whisker_0.3-2 later_0.8.0
[27] BiocParallel_1.18.0 git2r_0.26.1
[29] tibble_3.1.6 ellipsis_0.3.2
[31] cachem_1.0.6 SummarizedExperiment_1.14.1
[33] lazyeval_0.2.2 cli_3.1.0
[35] magrittr_2.0.2 crayon_1.5.0
[37] memoise_2.0.1 evaluate_0.14
[39] ps_1.6.0 fs_1.5.2
[41] fansi_1.0.2 tools_3.6.1
[43] data.table_1.14.2 prettyunits_1.1.1
[45] hms_1.1.1 lifecycle_1.0.1
[47] matrixStats_0.57.0 stringr_1.4.0
[49] DelayedArray_0.10.0 callr_3.7.0
[51] Biostrings_2.52.0 compiler_3.6.1
[53] jquerylib_0.1.4 rlang_1.0.1
[55] grid_3.6.1 RCurl_1.98-1.5
[57] rstudioapi_0.13 bitops_1.0-7
[59] rmarkdown_2.11 DBI_1.1.2
[61] curl_4.3.2 R6_2.5.1
[63] GenomicAlignments_1.20.1 knitr_1.36
[65] rtracklayer_1.44.4 fastmap_1.1.0
[67] bit_4.0.4 utf8_1.2.2
[69] rprojroot_2.0.2 ProtGenerics_1.16.0
[71] stringi_1.7.6 Rcpp_1.0.8
[73] vctrs_0.3.8 xfun_0.29