Seven Uncorrelated Tissues Simulation
shengqian
2023-12-30
Last updated: 2023-12-30
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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library(ctwas)
library(data.table)
source("/project/xinhe/shengqian/cTWAS_simulation/summarize_basic_plots.R")
Attaching package: 'ggpubr'The following object is masked from 'package:cowplot':
get_legendsource("/project/xinhe/shengqian/cTWAS_simulation/summarize_ctwas_plots.R")
Attaching package: 'plyr'The following object is masked from 'package:ggpubr':
mutatesource("/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/analysis/simulation_help_functions.R")
plot_PIP <- function(configtag, runtag, simutags, ...){
phenofs <- paste0(results_dir, runtag, "_simu", simutags, "-pheno.Rd")
susieIfs <- paste0(results_dir, runtag, "_simu",simutags, "_config", configtag,"_LDR",".susieIrss.txt")
f1 <- caliPIP_plot(phenofs, susieIfs, ...)
return(f1)
}Simulation 1: Three causal tissues and four non-causal tissues
30% PVE and 2.5e-4 prior inclusion for SNPs, 3% PVE and 0.009 prior inclusion for Liver, Lung and Whole Blood. 0% PVE for other tissues.
Shared effect size parameters
For the cTWAS analysis, tissues shared an effect size parameter.
results_dir <- "/project/xinhe/shengqian/cTWAS_simulation/simulation_uncorrelated_seven_tissues/"
runtag = "ukb-s80.45-3_7uncorr"
configtag <- 1
simutags <- paste(1, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_joint_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#results using PIP threshold (gene+tissue)
results_df[,c("simutag", "n_causal", "n_detected_pip", "n_detected_pip_in_causal")] simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 212 28 23
2 1-2 233 28 23
3 1-3 215 31 28
4 1-4 208 20 18
5 1-5 250 18 17#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.872#results using combined PIP threshold
results_df[,c("simutag", "n_causal_combined", "n_detected_comb_pip", "n_detected_comb_pip_in_causal")] simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 211 48 44
2 1-2 232 45 40
3 1-3 215 48 41
4 1-4 205 39 36
5 1-5 249 38 37#mean percent causal using combined PIP > 0.8
sum(results_df$n_detected_comb_pip_in_causal)/sum(results_df$n_detected_comb_pip)[1] 0.9082569#prior inclusion and mean prior inclusion
results_df[,c(which(colnames(results_df)=="simutag"), setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df))))] simutag prior_SNP prior_Liver prior_Lung prior_Whole_Blood
1 1-1 0.0002485463 0.007621948 0.005613419 0.003948000
2 1-2 0.0002386113 0.009916968 0.006585072 0.005465533
3 1-3 0.0002323031 0.008577723 0.007549237 0.012772498
4 1-4 0.0002423999 0.008285412 0.006660686 0.006875146
5 1-5 0.0002163173 0.012400705 0.006698309 0.014335053
prior_Adipose_Subcutaneous prior_Artery_Tibial prior_Heart_Left_Ventricle
1 3.018931e-03 6.001398e-06 1.555114e-04
2 1.811114e-06 1.702405e-05 1.227370e-03
3 4.972467e-05 7.139294e-04 4.208939e-05
4 4.821703e-05 8.345974e-04 2.138326e-03
5 3.461578e-03 2.376967e-04 7.194429e-04
prior_Stomach
1 2.006821e-07
2 3.493262e-04
3 4.416738e-04
4 3.076348e-06
5 3.460707e-06colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_SNP prior_Liver
0.0002356356 0.0093605512
prior_Lung prior_Whole_Blood
0.0066213444 0.0086792459
prior_Adipose_Subcutaneous prior_Artery_Tibial
0.0013160525 0.0003618498
prior_Heart_Left_Ventricle prior_Stomach
0.0008565479 0.0001595475 #prior variance and mean prior variance
results_df[,c(which(colnames(results_df)=="simutag"), grep("prior_var", names(results_df)))] simutag prior_var_SNP prior_var_Liver prior_var_Lung prior_var_Whole_Blood
1 1-1 8.274608 22.62783 22.62783 22.62783
2 1-2 9.962285 20.11661 20.11661 20.11661
3 1-3 8.637861 13.02761 13.02761 13.02761
4 1-4 8.834089 20.22149 20.22149 20.22149
5 1-5 10.701504 13.16406 13.16406 13.16406
prior_var_Adipose_Subcutaneous prior_var_Artery_Tibial
1 22.62783 22.62783
2 20.11661 20.11661
3 13.02761 13.02761
4 20.22149 20.22149
5 13.16406 13.16406
prior_var_Heart_Left_Ventricle prior_var_Stomach
1 22.62783 22.62783
2 20.11661 20.11661
3 13.02761 13.02761
4 20.22149 20.22149
5 13.16406 13.16406colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_SNP prior_var_Liver
9.28207 17.83152
prior_var_Lung prior_var_Whole_Blood
17.83152 17.83152
prior_var_Adipose_Subcutaneous prior_var_Artery_Tibial
17.83152 17.83152
prior_var_Heart_Left_Ventricle prior_var_Stomach
17.83152 17.83152 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_SNP pve_Liver pve_Lung pve_Whole_Blood
1 1-1 0.2420956 0.02834227 0.02758864 0.01621921
2 1-2 0.2798223 0.03278381 0.02877234 0.01996167
3 1-3 0.2362074 0.01836379 0.02136129 0.03020995
4 1-4 0.2520731 0.02753296 0.02925445 0.02524089
5 1-5 0.2725011 0.02682638 0.01915204 0.03426091
pve_Adipose_Subcutaneous pve_Artery_Tibial pve_Heart_Left_Ventricle
1 1.472045e-02 2.919369e-05 0.0006470060
2 7.851009e-06 7.362261e-05 0.0045397654
3 1.395923e-04 1.999464e-03 0.0001008185
4 2.101060e-04 3.628138e-03 0.0079504194
5 9.819493e-03 6.726772e-04 0.0017413631
pve_Stomach
1 8.639000e-07
2 1.336897e-03
3 1.094658e-03
4 1.183479e-05
5 8.666961e-06colMeans(results_df[,grep("pve", names(results_df))]) pve_SNP pve_Liver pve_Lung
0.2565398813 0.0267698417 0.0252257509
pve_Whole_Blood pve_Adipose_Subcutaneous pve_Artery_Tibial
0.0251785254 0.0049794982 0.0012806191
pve_Heart_Left_Ventricle pve_Stomach
0.0029958745 0.0004905841 #TWAS results
results_df[,c(which(colnames(results_df)=="simutag"), grep("twas", names(results_df)))] simutag n_detected_twas n_detected_twas_in_causal n_detected_comb_twas
1 1-1 460 54 185
2 1-2 602 64 226
3 1-3 431 50 161
4 1-4 426 48 161
5 1-5 449 56 170
n_detected_comb_twas_in_causal
1 56
2 67
3 51
4 50
5 62sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.316722y1 <- results_df$prior_Liver
y2 <- results_df$prior_Lung
y3 <- results_df$prior_Whole_Blood
y4 <- results_df$prior_Adipose_Subcutaneous
y5 <- results_df$prior_Artery_Tibial
y6 <- results_df$prior_Heart_Left_Ventricle
y7 <- results_df$prior_Stomach
truth <- rbind(c(1,0.009),c(2,0.009),c(3,0.009),c(4,0),c(5,0),c(6,0),c(7,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Liver","Lung","Whole_Blood","Adipose_Subcutaneous","Artery_Tibial","Heart_Left_Ventricle","Stomach"),ylim=c(0,0.025),ylab="Prior inclusion")
| Version | Author | Date |
|---|---|---|
| 0a32579 | sq-96 | 2023-12-30 |
y1 <- results_df$pve_Liver
y2 <- results_df$pve_Lung
y3 <- results_df$pve_Whole_Blood
y4 <- results_df$pve_Adipose_Subcutaneous
y5 <- results_df$pve_Artery_Tibial
y6 <- results_df$pve_Heart_Left_Ventricle
y7 <- results_df$pve_Stomach
truth <- rbind(c(1,0.03),c(2,0.03),c(3,0.03),c(4,0),c(5,0),c(6,0),c(7,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Liver","Lung","Whole_Blood","Adipose_Subcutaneous","Artery_Tibial","Heart_Left_Ventricle","Stomach"),ylim=c(0,0.06),ylab="PVE")
| Version | Author | Date |
|---|---|---|
| 0a32579 | sq-96 | 2023-12-30 |
f1 <- plot_PIP(configtag, runtag, paste(1, 1:5, sep = "-"), main = "")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.f1
| Version | Author | Date |
|---|---|---|
| 0a32579 | sq-96 | 2023-12-30 |
Separate effect size parameters
For the cTWAS analysis, each tissue had its own prior inclusion parameter end effect size parameter.
results_dir <- "/project/xinhe/shengqian/cTWAS_simulation/simulation_uncorrelated_seven_tissues/"
runtag = "ukb-s80.45-3_7uncorr"
configtag <- 2
simutags <- paste(1, 1:5, sep = "-")
thin <- 0.1
sample_size <- 45000
PIP_threshold <- 0.8results_df <- get_sim_joint_res(results_dir,runtag,configtag,simutags,thin,sample_size,PIP_threshold)#results using PIP threshold (gene+tissue)
results_df[,c("simutag", "n_causal", "n_detected_pip", "n_detected_pip_in_causal")] simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 212 26 22
2 1-2 233 28 23
3 1-3 215 31 28
4 1-4 208 20 19
5 1-5 250 20 18#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.88#results using combined PIP threshold
results_df[,c("simutag", "n_causal_combined", "n_detected_comb_pip", "n_detected_comb_pip_in_causal")] simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 211 47 43
2 1-2 232 45 39
3 1-3 215 48 41
4 1-4 205 38 35
5 1-5 249 40 39#mean percent causal using combined PIP > 0.8
sum(results_df$n_detected_comb_pip_in_causal)/sum(results_df$n_detected_comb_pip)[1] 0.9036697#prior inclusion and mean prior inclusion
results_df[,c(which(colnames(results_df)=="simutag"), setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df))))] simutag prior_SNP prior_Liver prior_Lung prior_Whole_Blood
1 1-1 0.0002443644 0.010502956 0.005091416 0.002940554
2 1-2 0.0002346972 0.010027416 0.006077122 0.004684825
3 1-3 0.0002304808 0.009965012 0.007305465 0.011857198
4 1-4 0.0002367544 0.006121703 0.007944045 0.005803923
5 1-5 0.0002094775 0.011459792 0.005492726 0.012925182
prior_Adipose_Subcutaneous prior_Artery_Tibial prior_Heart_Left_Ventricle
1 0.0025745066 0.0006824154 0.0018343196
2 0.0010463815 0.0008888367 0.0030441927
3 0.0003555987 0.0006158948 0.0009995712
4 0.0005985625 0.0038859034 0.0027553008
5 0.0077478112 0.0009992981 0.0031037525
prior_Stomach
1 0.0007234330
2 0.0014207567
3 0.0007210691
4 0.0005181261
5 0.0002281063colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_SNP prior_Liver
0.0002311549 0.0096153757
prior_Lung prior_Whole_Blood
0.0063821547 0.0076423365
prior_Adipose_Subcutaneous prior_Artery_Tibial
0.0024645721 0.0014144697
prior_Heart_Left_Ventricle prior_Stomach
0.0023474274 0.0007222983 #prior variance and mean prior variance
results_df[,c(which(colnames(results_df)=="simutag"), grep("prior_var", names(results_df)))] simutag prior_var_SNP prior_var_Liver prior_var_Lung prior_var_Whole_Blood
1 1-1 8.380843 14.22444 25.98711 32.88325
2 1-2 10.078291 19.68738 23.31072 22.07177
3 1-3 8.721318 10.50035 13.83639 14.35451
4 1-4 8.991367 29.64595 15.10820 25.76661
5 1-5 10.952164 13.15210 17.94149 16.29645
prior_var_Adipose_Subcutaneous prior_var_Artery_Tibial
1 29.561451 2.630071
2 1.646979 2.758958
3 1.159702 17.940360
4 1.901471 5.806594
5 4.601767 2.371968
prior_var_Heart_Left_Ventricle prior_var_Stomach
1 3.522158 1.2074772
2 6.259636 3.9220217
3 1.439703 3.6824503
4 13.936568 1.1864726
5 3.944363 0.9562117colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_SNP prior_var_Liver
9.424797 17.442046
prior_var_Lung prior_var_Whole_Blood
19.236783 22.274517
prior_var_Adipose_Subcutaneous prior_var_Artery_Tibial
7.774274 6.301590
prior_var_Heart_Left_Ventricle prior_var_Stomach
5.820485 2.190927 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_SNP pve_Liver pve_Lung pve_Whole_Blood
1 1-1 0.2410782 0.02455119 0.02873799 0.01755551
2 1-2 0.2784371 0.03244163 0.03076900 0.01877328
3 1-3 0.2366188 0.01719521 0.02195486 0.03090152
4 1-4 0.2505855 0.02982382 0.02606839 0.02715116
5 1-5 0.2700658 0.02476838 0.02140456 0.03824189
pve_Adipose_Subcutaneous pve_Artery_Tibial pve_Heart_Left_Ventricle
1 1.640003e-02 0.0003858423 0.0011879190
2 3.713667e-04 0.0005271821 0.0035036781
3 8.886517e-05 0.0023753700 0.0002645998
4 2.452586e-04 0.0048507294 0.0070603762
5 7.682959e-03 0.0005095625 0.0022509561
pve_Stomach
1 1.661840e-04
2 1.060087e-03
3 5.051563e-04
4 1.169513e-04
5 4.149573e-05colMeans(results_df[,grep("pve", names(results_df))]) pve_SNP pve_Liver pve_Lung
0.255357052 0.025756045 0.025786960
pve_Whole_Blood pve_Adipose_Subcutaneous pve_Artery_Tibial
0.026524672 0.004957696 0.001729737
pve_Heart_Left_Ventricle pve_Stomach
0.002853506 0.000377975 #TWAS results
results_df[,c(which(colnames(results_df)=="simutag"), grep("twas", names(results_df)))] simutag n_detected_twas n_detected_twas_in_causal n_detected_comb_twas
1 1-1 460 54 185
2 1-2 602 64 226
3 1-3 431 50 161
4 1-4 426 48 161
5 1-5 449 56 170
n_detected_comb_twas_in_causal
1 56
2 67
3 51
4 50
5 62sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.316722y1 <- results_df$prior_Liver
y2 <- results_df$prior_Lung
y3 <- results_df$prior_Whole_Blood
y4 <- results_df$prior_Adipose_Subcutaneous
y5 <- results_df$prior_Artery_Tibial
y6 <- results_df$prior_Heart_Left_Ventricle
y7 <- results_df$prior_Stomach
truth <- rbind(c(1,0.009),c(2,0.009),c(3,0.009),c(4,0),c(5,0),c(6,0),c(7,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Liver","Lung","Whole_Blood","Adipose_Subcutaneous","Artery_Tibial","Heart_Left_Ventricle","Stomach"),ylim=c(0,0.025),ylab="Prior inclusion")
| Version | Author | Date |
|---|---|---|
| 0a32579 | sq-96 | 2023-12-30 |
y1 <- results_df$pve_Liver
y2 <- results_df$pve_Lung
y3 <- results_df$pve_Whole_Blood
y4 <- results_df$pve_Adipose_Subcutaneous
y5 <- results_df$pve_Artery_Tibial
y6 <- results_df$pve_Heart_Left_Ventricle
y7 <- results_df$pve_Stomach
truth <- rbind(c(1,0.03),c(2,0.03),c(3,0.03),c(4,0),c(5,0),c(6,0),c(7,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Liver","Lung","Whole_Blood","Adipose_Subcutaneous","Artery_Tibial","Heart_Left_Ventricle","Stomach"),ylim=c(0,0.06),ylab="PVE")
| Version | Author | Date |
|---|---|---|
| 0a32579 | sq-96 | 2023-12-30 |
f1 <- plot_PIP(configtag, runtag, paste(1, 1:5, sep = "-"), main = "")
f1
| Version | Author | Date |
|---|---|---|
| 0a32579 | sq-96 | 2023-12-30 |
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
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] stats graphics grDevices utils datasets methods base
other attached packages:
[1] plyr_1.8.8 ggpubr_0.6.0 plotrix_3.8-4 cowplot_1.1.1
[5] ggplot2_3.4.0 data.table_1.14.6 ctwas_0.1.35 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.9 lattice_0.20-44 tidyr_1.3.0 getPass_0.2-2
[5] ps_1.7.2 assertthat_0.2.1 rprojroot_2.0.3 digest_0.6.31
[9] foreach_1.5.2 utf8_1.2.2 R6_2.5.1 backports_1.2.1
[13] evaluate_0.19 highr_0.9 httr_1.4.4 pillar_1.8.1
[17] rlang_1.1.1 rstudioapi_0.14 car_3.1-1 whisker_0.4.1
[21] callr_3.7.3 jquerylib_0.1.4 Matrix_1.3-3 rmarkdown_2.19
[25] labeling_0.4.2 stringr_1.5.0 munsell_0.5.0 broom_1.0.2
[29] compiler_4.1.0 httpuv_1.6.7 xfun_0.35 pkgconfig_2.0.3
[33] htmltools_0.5.4 tidyselect_1.2.0 tibble_3.1.8 logging_0.10-108
[37] codetools_0.2-18 fansi_1.0.3 crayon_1.5.2 dplyr_1.0.10
[41] withr_2.5.0 later_1.3.0 grid_4.1.0 jsonlite_1.8.4
[45] gtable_0.3.1 lifecycle_1.0.3 DBI_1.1.3 git2r_0.30.1
[49] magrittr_2.0.3 scales_1.2.1 carData_3.0-4 cli_3.6.1
[53] stringi_1.7.8 cachem_1.0.6 farver_2.1.0 ggsignif_0.6.4
[57] fs_1.5.2 promises_1.2.0.1 pgenlibr_0.3.2 bslib_0.4.1
[61] vctrs_0.6.3 generics_0.1.3 iterators_1.0.14 tools_4.1.0
[65] glue_1.6.2 purrr_1.0.2 abind_1.4-5 processx_3.8.0
[69] fastmap_1.1.0 yaml_2.3.6 colorspace_2.0-3 rstatix_0.7.2
[73] knitr_1.41 sass_0.4.4