Last updated: 2022-05-12
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Knit directory: cTWAS_analysis/
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library(reticulate)
use_python("/scratch/midway2/shengqian/miniconda3/envs/PythonForR/bin/python",required=T)#number of imputed weights
nrow(qclist_all)[1] 15774#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1480 1078 891 631 654 819 931 556 642 735 948 858 322 583 532 618
17 18 19 20 21 22
1087 198 1146 543 34 488 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 14087#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8931from pyensembl import EnsemblRelease #repl_python()
import math
import time
import numpy as np
import pandas as pd
data = EnsemblRelease(104)
ctwas_gene_res_df = r.ctwas_gene_res
ctwas_gene_res_df['intron_pos'] = ctwas_gene_res_df['id'].apply(lambda x: math.floor((int(x.split("_")[2])+int(x.split("_")[3]))/2))
ctwas_gene_res_df['genename'] = np.nan
ctwas_gene_res_df['intron_id'] = ctwas_gene_res_df['id']for index, row in ctwas_gene_res_df.iterrows():
gene_names = data.gene_names_at_locus(contig=int(row["chrom"]), position=int(row["intron_pos"]))
if len(gene_names)!=0:
for i in gene_names:
if i != '':
gene_info = data.genes_by_name(i)[0]
ctwas_gene_res_df.loc[index,'genename'] = gene_info.gene_name
ctwas_gene_res_df.loc[index,'id'] = gene_info.gene_id
ctwas_gene_res_df.loc[index,'type'] = gene_info.biotype
break
ctwas_gene_res_df = ctwas_gene_res_df.dropna(subset=['genename'])
print("finish")finish#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res <- py$ctwas_gene_res_df
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
ctwas_snp_res$intron_pos=NA
saveRDS(ctwas_gene_res, file = paste0(results_dir,"/",analysis_id,"_ctwas_gene_res.RDS"))
saveRDS(ctwas_snp_res, file = paste0(results_dir,"/",analysis_id,"_ctwas_snp_res.RDS"))ctwas_intron_res <- readRDS(paste0(results_dir,"/",analysis_id,"_ctwas_gene_res.RDS"))
ctwas_intron_res <- ctwas_intron_res[!is.na(ctwas_intron_res$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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reticulate_1.16 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8 highr_0.9 jquerylib_0.1.4 compiler_3.6.1
[5] pillar_1.6.4 later_0.8.0 git2r_0.26.1 tools_3.6.1
[9] getPass_0.2-2 digest_0.6.29 jsonlite_1.7.2 lattice_0.20-38
[13] evaluate_0.14 tibble_3.1.6 lifecycle_1.0.1 pkgconfig_2.0.3
[17] rlang_1.0.1 Matrix_1.2-18 cli_3.1.0 rstudioapi_0.13
[21] yaml_2.2.1 xfun_0.29 fastmap_1.1.0 httr_1.4.2
[25] stringr_1.4.0 knitr_1.36 fs_1.5.2 vctrs_0.3.8
[29] grid_3.6.1 rprojroot_2.0.2 data.table_1.14.2 glue_1.6.2
[33] R6_2.5.1 processx_3.5.2 fansi_1.0.2 rmarkdown_2.11
[37] callr_3.7.0 magrittr_2.0.2 whisker_0.3-2 ps_1.6.0
[41] promises_1.0.1 htmltools_0.5.2 ellipsis_0.3.2 httpuv_1.5.1
[45] utf8_1.2.2 stringi_1.7.6 crayon_1.5.0