SCZ - Brain Cortex
sheng Qian
2021-2-6
Last updated: 2022-09-03
Checks: 5 2
Knit directory: cTWAS_analysis/
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| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config_b37.R | code/ctwas_config_b37.R |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2b787bd | sq-96 | 2022-09-03 | update |
| html | 2b787bd | sq-96 | 2022-09-03 | update |
[1] 11502[1] 10248
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1041 726 590 400 470 587 492 367 394 426 617 601 210 343 347 435
17 18 19 20 21 22
630 168 787 331 25 261 [1] 0.6857Load ctwas results
Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| 2b787bd | sq-96 | 2022-09-03 |
gene snp
0.0131343 0.0003062 gene snp
11.53 10.50 [1] 42.9[1] 105318[1] 10248 6309950 gene snp
0.01474 0.19261 [1] 0.2074 gene
0.07109 Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| 2b787bd | sq-96 | 2022-09-03 |
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z num_eqtl
10276 ZNF823 19_10 0.9852 37.03 0.0003464 6.181 2
NA.3119 <NA> 6_102 0.9579 23.05 0.0002097 -4.712 2
3705 ARMC7 17_42 0.9041 22.49 0.0001931 4.486 2
385 TRIT1 1_25 0.8947 20.82 0.0001768 -4.162 3
NA.3114 <NA> 3_36 0.8837 37.45 0.0003142 -6.807 1
NA.3126 <NA> 12_33 0.8775 26.41 0.0002200 5.065 1
2928 SF3B1 2_117 0.8357 48.83 0.0003875 7.265 1
4685 RCBTB1 13_21 0.8072 21.32 0.0001634 -4.251 2
NA.3123 <NA> 9_13 0.8005 23.18 0.0001762 4.362 2
2533 VPS29 12_67 0.7991 40.26 0.0003055 -6.461 1
3013 EDEM3 1_92 0.7964 21.59 0.0001633 4.223 2
3928 SPECC1 17_16 0.7887 25.56 0.0001914 4.822 1
345 CUL3 2_132 0.7630 30.14 0.0002184 -5.730 1
5604 METTL21A 2_122 0.7628 21.45 0.0001554 -4.284 1
2583 NT5DC3 12_62 0.7438 22.58 0.0001594 -4.142 2
5543 ITPKB 1_116 0.7154 22.29 0.0001514 -4.033 2
2284 CCDC6 10_39 0.6983 21.24 0.0001408 -3.918 2
2795 PCCB 3_84 0.6976 41.45 0.0002746 -6.724 1
2200 TLE4 9_38 0.6885 21.15 0.0001382 4.279 1
NA.3017 <NA> 20_38 0.6812 21.85 0.0001413 3.659 1Comparing z scores and PIPs
locus_plot_final_pub <- function(region_tag, xlim=NULL, return_table=F, focus=NULL, label_panel="TWAS", label_genes=NULL, label_pos=NULL, plot_eqtl=NULL, rerun_ctwas=F, rerun_load_only=F, legend_side="right", legend_panel="cTWAS", twas_ymax=NULL){
region_tag1 <- unlist(strsplit(region_tag, "_"))[1]
region_tag2 <- unlist(strsplit(region_tag, "_"))[2]
a <- ctwas_res[ctwas_res$region_tag==region_tag,]
regionlist <- readRDS(paste0(results_dir, "/", analysis_id, "_ctwas.regionlist.RDS"))
region <- regionlist[[as.numeric(region_tag1)]][[region_tag2]]
R_snp_info <- do.call(rbind, lapply(region$regRDS, function(x){data.table::fread(paste0(tools::file_path_sans_ext(x), ".Rvar"))}))
if (isTRUE(rerun_ctwas)){
ld_exprfs <- paste0(results_dir, "/", analysis_id, "_expr_chr", 1:22, ".expr.gz")
temp_reg <- data.frame("chr" = paste0("chr",region_tag1), "start" = region$start, "stop" = region$stop)
write.table(temp_reg,
#file= paste0(results_dir, "/", analysis_id, "_ctwas.temp.reg.txt") ,
file= "temp_reg.txt",
row.names=F, col.names=T, sep="\t", quote = F)
load(paste0(results_dir, "/", analysis_id, "_expr_z_snp.Rd"))
z_gene_temp <- z_gene[z_gene$id %in% a$id[a$type=="gene"],]
z_snp_temp <- z_snp[z_snp$id %in% R_snp_info$id,]
if (!rerun_load_only){
ctwas::ctwas_rss(z_gene_temp, z_snp_temp, ld_exprfs, ld_pgenfs = NULL,
ld_R_dir = dirname(region$regRDS)[1],
ld_regions_custom = "temp_reg.txt", thin = 1,
outputdir = ".", outname = "temp", ncore = 1, ncore.rerun = 1, prob_single = 0,
group_prior = estimated_group_prior, group_prior_var = estimated_group_prior_var,
estimate_group_prior = F, estimate_group_prior_var = F)
}
a_bkup <- a
a <- as.data.frame(data.table::fread("temp.susieIrss.txt", header = T))
rownames(z_snp_temp) <- z_snp_temp$id
z_snp_temp <- z_snp_temp[a$id[a$type=="SNP"],]
z_gene_temp <- z_gene_temp[a$id[a$type=="gene"],]
a$genename <- NA
a$gene_type <- NA
a[a$type=="gene",c("genename", "gene_type")] <- a_bkup[match(a$id[a$type=="gene"], a_bkup$id),c("genename","gene_type")]
a$z <- NA
a$z[a$type=="SNP"] <- z_snp_temp$z
a$z[a$type=="gene"] <- z_gene_temp$z
}
a_pos_bkup <- a$pos
a$pos[a$type=="gene"] <- G_list$tss[match(sapply(a$id[a$type=="gene"], function(x){unlist(strsplit(x, "[.]"))[1]}) ,G_list$ensembl_gene_id)]
a$pos[is.na(a$pos)] <- a_pos_bkup[is.na(a$pos)]
a$pos <- a$pos/1000000
if (!is.null(xlim)){
if (is.na(xlim[1])){
xlim[1] <- min(a$pos)
}
if (is.na(xlim[2])){
xlim[2] <- max(a$pos)
}
a <- a[a$pos>=xlim[1] & a$pos<=xlim[2],,drop=F]
}
if (is.null(focus)){
focus <- a$genename[a$z==max(abs(a$z)[a$type=="gene"])]
}
if (is.null(label_genes)){
label_genes <- focus
}
if (is.null(label_pos)){
label_pos <- rep(3, length(label_genes))
}
if (is.null(plot_eqtl)){
plot_eqtl <- focus
}
focus <- a$id[which(a$genename==focus)]
a$focus <- 0
a$focus <- as.numeric(a$id==focus)
a$PVALUE <- (-log(2) - pnorm(abs(a$z), lower.tail=F, log.p=T))/log(10)
R_gene <- readRDS(region$R_g_file)
R_snp_gene <- readRDS(region$R_sg_file)
R_snp <- as.matrix(Matrix::bdiag(lapply(region$regRDS, readRDS)))
rownames(R_gene) <- region$gid
colnames(R_gene) <- region$gid
rownames(R_snp_gene) <- R_snp_info$id
colnames(R_snp_gene) <- region$gid
rownames(R_snp) <- R_snp_info$id
colnames(R_snp) <- R_snp_info$id
a$r2max <- NA
a$r2max[a$type=="gene"] <- R_gene[focus,a$id[a$type=="gene"]]
a$r2max[a$type=="SNP"] <- R_snp_gene[a$id[a$type=="SNP"],focus]
r2cut <- 0.4
colorsall <- c("#7fc97f", "#beaed4", "#fdc086")
start <- min(a$pos)
end <- max(a$pos)
layout(matrix(1:4, ncol = 1), widths = 1, heights = c(1.5,0.25,1.75,0.75), respect = FALSE)
par(mar = c(0, 4.1, 0, 2.1))
if (is.null(twas_ymax)){
twas_ymax <- max(a$PVALUE)*1.1
}
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 21, xlab=paste0("Chromosome ", region_tag1, " position (Mb)"), frame.plot=FALSE, bg = colorsall[1], ylab = "-log10(p value)", panel.first = grid(), ylim =c(0, twas_ymax), xaxt = 'n', xlim=c(start, end))
abline(h=-log10(alpha/nrow(ctwas_gene_res)), col ="red", lty = 2)
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$PVALUE[a$type == "SNP" & a$r2max > r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$focus == 1], a$PVALUE[a$type == "SNP" & a$focus == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$PVALUE[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$PVALUE[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$focus == 1], a$PVALUE[a$type == "gene" & a$focus == 1], pch = 22, bg = "salmon", cex = 2)
if (legend_panel=="TWAS"){
x_pos <- ifelse(legend_side=="right", max(a$pos)-0.2*(max(a$pos)-min(a$pos)), min(a$pos))
legend(x_pos, y= twas_ymax*0.95, c("Gene", "SNP","Lead TWAS Gene", "R2 > 0.4", "R2 <= 0.4"), pch = c(22,21,19,19,19), col = c("black", "black", "salmon", "purple", colorsall[1]), cex=0.7, title.adj = 0)
}
if (label_panel=="TWAS" | label_panel=="both"){
for (i in 1:length(label_genes)){
text(a$pos[a$genename==label_genes[i]], a$PVALUE[a$genename==label_genes[i]], labels=label_genes[i], pos=label_pos[i], cex=0.7)
}
}
par(mar = c(0.25, 4.1, 0.25, 2.1))
plot(NA, xlim = c(start, end), ylim = c(0, length(plot_eqtl)), frame.plot = F, axes = F, xlab = NA, ylab = NA)
for (i in 1:length(plot_eqtl)){
cgene <- a$id[which(a$genename==plot_eqtl[i])]
load(paste0(results_dir, "/",analysis_id, "_expr_chr", region_tag1, ".exprqc.Rd"))
eqtls <- rownames(wgtlist[[cgene]])
eqtl_pos <- a$pos[a$id %in% eqtls]
#col="grey"
col="#c6e8f0"
rect(start, length(plot_eqtl)+1-i-0.8, end, length(plot_eqtl)+1-i-0.2, col = col, border = T, lwd = 1)
if (length(eqtl_pos)>0){
for (j in 1:length(eqtl_pos)){
segments(x0=eqtl_pos[j], x1=eqtl_pos[j], y0=length(plot_eqtl)+1-i-0.2, length(plot_eqtl)+1-i-0.8, lwd=1.5)
}
}
}
text(start, length(plot_eqtl)-(1:length(plot_eqtl))+0.5,
labels = paste0(plot_eqtl, " eQTL"), srt = 0, pos = 2, xpd = TRUE, cex=0.7)
par(mar = c(4.1, 4.1, 0, 2.1))
plot(a$pos[a$type=="SNP"], a$susie_pip[a$type == "SNP"], pch = 19, xlab=paste0("Chromosome ", region_tag1, " position (Mb)"),frame.plot=FALSE, col = "white", ylim= c(0,1.1), ylab = "cTWAS PIP", xlim = c(start, end))
grid()
points(a$pos[a$type=="SNP"], a$susie_pip[a$type == "SNP"], pch = 21, xlab="Genomic position", bg = colorsall[1])
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$susie_pip[a$type == "SNP" & a$r2max >r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$focus == 1], a$susie_pip[a$type == "SNP" & a$focus == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$susie_pip[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$susie_pip[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$focus == 1], a$susie_pip[a$type == "gene" & a$focus == 1], pch = 22, bg = "salmon", cex = 2)
if (legend_panel=="cTWAS"){
x_pos <- ifelse(legend_side=="right", max(a$pos)-0.2*(max(a$pos)-min(a$pos)), min(a$pos))
legend(x_pos, y= 1 ,c("Gene", "SNP","Lead TWAS Gene", "R2 > 0.4", "R2 <= 0.4"), pch = c(22,21,19,19,19), col = c("black", "black", "salmon", "purple", colorsall[1]), cex=0.7, title.adj = 0)
}
if (label_panel=="cTWAS" | label_panel=="both"){
for (i in 1:length(label_genes)){
text(a$pos[a$genename==label_genes[i]], a$susie_pip[a$genename==label_genes[i]], labels=label_genes[i], pos=label_pos[i], cex=0.7)
}
}
if (return_table){
return(a)
}
}
####################
library(Gviz)Loading required package: S4VectorsLoading required package: stats4Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Attaching package: 'S4Vectors'The following object is masked from 'package:base':
expand.gridLoading required package: IRangesLoading required package: GenomicRangesLoading required package: GenomeInfoDbLoading required package: gridlocus_plot_gene_track_pub <- function(a, label_pos=NULL){
chr <- unique(a$chrom)
start <- min(a$pos)*1000000
end <- max(a$pos)*1000000
biomTrack <- BiomartGeneRegionTrack(chromosome = chr,
start = start,
end = end,
name = "ENSEMBL",
biomart = ensembl,
filters=list(biotype="protein_coding"))
biomTrack <- as(biomTrack, "GeneRegionTrack")
biomTrack <- biomTrack[biomTrack@range@elementMetadata@listData$feature %in% c("protein_coding", "utr3", "utr5")]
if (isTRUE(label_pos=="above")){
displayPars(biomTrack)$just.group <- "above"
}
grid.newpage()
plotTracks(biomTrack, collapseTranscripts = "meta", transcriptAnnotation = "symbol", from=start, to=end, panel.only=T, add=F)
}a <- locus_plot_final_pub(region_tag="19_10", return_table=T,
focus="ZNF823",
label_genes=c("ZNF823"),
label_pos=c(3,3),
label_panel="both",
plot_eqtl=c("ZNF823"),
legend_side="left",
legend_panel="cTWAS")
| Version | Author | Date |
|---|---|---|
| 2b787bd | sq-96 | 2022-09-03 |
locus_plot_gene_track_pub(a, label_pos="above")
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] Gviz_1.38.4 GenomicRanges_1.46.0 GenomeInfoDb_1.26.7
[4] IRanges_2.24.1 S4Vectors_0.28.1 BiocGenerics_0.40.0
[7] biomaRt_2.50.0 cowplot_1.1.1 ggplot2_3.3.6
[10] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-3 rjson_0.2.20
[3] ellipsis_0.3.2 rprojroot_2.0.3
[5] htmlTable_2.2.1 biovizBase_1.42.0
[7] XVector_0.34.0 base64enc_0.1-3
[9] fs_1.5.2 dichromat_2.0-0.1
[11] rstudioapi_0.13 farver_2.1.0
[13] bit64_4.0.5 AnnotationDbi_1.56.1
[15] fansi_1.0.3 xml2_1.3.2
[17] splines_4.1.0 cachem_1.0.6
[19] knitr_1.33 Formula_1.2-4
[21] jsonlite_1.8.0 Rsamtools_2.10.0
[23] cluster_2.1.2 dbplyr_2.1.1
[25] png_0.1-7 compiler_4.1.0
[27] httr_1.4.3 backports_1.2.1
[29] lazyeval_0.2.2 assertthat_0.2.1
[31] Matrix_1.3-3 fastmap_1.1.0
[33] cli_3.3.0 later_1.2.0
[35] htmltools_0.5.3 prettyunits_1.1.1
[37] tools_4.1.0 gtable_0.3.0
[39] glue_1.6.2 GenomeInfoDbData_1.2.7
[41] dplyr_1.0.9 rappdirs_0.3.3
[43] Rcpp_1.0.9 Biobase_2.54.0
[45] jquerylib_0.1.4 vctrs_0.4.1
[47] Biostrings_2.62.0 rtracklayer_1.54.0
[49] xfun_0.24 stringr_1.4.0
[51] ps_1.7.0 lifecycle_1.0.1
[53] ensembldb_2.18.4 restfulr_0.0.13
[55] XML_3.99-0.6 getPass_0.2-2
[57] zlibbioc_1.40.0 scales_1.2.0
[59] BSgenome_1.62.0 VariantAnnotation_1.40.0
[61] ProtGenerics_1.26.0 hms_1.1.1
[63] promises_1.2.0.1 MatrixGenerics_1.6.0
[65] parallel_4.1.0 SummarizedExperiment_1.24.0
[67] AnnotationFilter_1.18.0 RColorBrewer_1.1-3
[69] yaml_2.2.1 curl_4.3.2
[71] gridExtra_2.3 memoise_2.0.1
[73] sass_0.4.0 rpart_4.1-15
[75] latticeExtra_0.6-29 stringi_1.7.6
[77] RSQLite_2.2.14 highr_0.9
[79] BiocIO_1.4.0 checkmate_2.0.0
[81] GenomicFeatures_1.46.1 filelock_1.0.2
[83] BiocParallel_1.28.0 rlang_1.0.4
[85] pkgconfig_2.0.3 bitops_1.0-7
[87] matrixStats_0.62.0 evaluate_0.15
[89] lattice_0.20-44 purrr_0.3.4
[91] htmlwidgets_1.5.3 GenomicAlignments_1.30.0
[93] labeling_0.4.2 bit_4.0.4
[95] processx_3.5.3 tidyselect_1.1.2
[97] magrittr_2.0.3 R6_2.5.1
[99] generics_0.1.2 Hmisc_4.5-0
[101] DelayedArray_0.20.0 DBI_1.1.2
[103] foreign_0.8-81 pillar_1.7.0
[105] whisker_0.4 withr_2.5.0
[107] nnet_7.3-16 survival_3.2-11
[109] KEGGREST_1.34.0 RCurl_1.98-1.6
[111] tibble_3.1.7 crayon_1.5.1
[113] utf8_1.2.2 BiocFileCache_2.2.0
[115] rmarkdown_2.9 jpeg_0.1-8.1
[117] progress_1.2.2 data.table_1.14.2
[119] blob_1.2.3 callr_3.7.0
[121] git2r_0.28.0 digest_0.6.29
[123] httpuv_1.6.1 munsell_0.5.0
[125] bslib_0.4.0