Seven Correlated 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 Brain Cerebellum, Brain Hippocampus and Brain Caudate basal ganglia. 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_correlated_seven_tissues/"
runtag = "ukb-s80.45-3_7corr"
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 1-1 211 10 8
2 1-2 233 32 28
3 1-3 215 21 18
4 1-4 212 30 29
5 1-5 254 17 16#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.9#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 1-1 209 40 36
2 1-2 232 69 55
3 1-3 212 40 37
4 1-4 212 56 52
5 1-5 253 49 43#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] 0.8779528#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_Brain_Cerebellum prior_Brain_Hippocampus
1 1-1 0.0002395754 0.004483670 0.0068481880
2 1-2 0.0002427045 0.007869741 0.0132591028
3 1-3 0.0002355897 0.006018338 0.0003961507
4 1-4 0.0002386003 0.004966132 0.0113152506
5 1-5 0.0002455809 0.005096201 0.0106738503
prior_Brain_Caudate_basal_ganglia prior_Brain_Cerebellar_Hemisphere
1 0.000748970 4.543431e-03
2 0.012492771 1.787225e-06
3 0.012111091 4.935098e-04
4 0.007530475 3.052034e-04
5 0.009940482 1.602602e-03
prior_Brain_Cortex prior_Brain_Hypothalamus prior_Brain_Putamen_basal_ganglia
1 2.754072e-06 0.0034925487 0.0026968720
2 1.488891e-03 0.0025931239 0.0003692669
3 2.332537e-03 0.0009549596 0.0020164558
4 1.487342e-05 0.0002722532 0.0051363722
5 4.787281e-03 0.0031010995 0.0003623928colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_SNP prior_Brain_Cerebellum
0.0002404102 0.0056868163
prior_Brain_Hippocampus prior_Brain_Caudate_basal_ganglia
0.0084985085 0.0085647577
prior_Brain_Cerebellar_Hemisphere prior_Brain_Cortex
0.0013893067 0.0017252673
prior_Brain_Hypothalamus prior_Brain_Putamen_basal_ganglia
0.0020827970 0.0021162719 #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_Brain_Cerebellum prior_var_Brain_Hippocampus
1 1-1 8.664607 22.16341 22.16341
2 1-2 8.468107 17.40351 17.40351
3 1-3 8.053270 15.65865 15.65865
4 1-4 9.213080 16.77674 16.77674
5 1-5 8.160598 16.02897 16.02897
prior_var_Brain_Caudate_basal_ganglia prior_var_Brain_Cerebellar_Hemisphere
1 22.16341 22.16341
2 17.40351 17.40351
3 15.65865 15.65865
4 16.77674 16.77674
5 16.02897 16.02897
prior_var_Brain_Cortex prior_var_Brain_Hypothalamus
1 22.16341 22.16341
2 17.40351 17.40351
3 15.65865 15.65865
4 16.77674 16.77674
5 16.02897 16.02897
prior_var_Brain_Putamen_basal_ganglia
1 22.16341
2 17.40351
3 15.65865
4 16.77674
5 16.02897colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_SNP prior_var_Brain_Cerebellum
8.511932 17.606253
prior_var_Brain_Hippocampus prior_var_Brain_Caudate_basal_ganglia
17.606253 17.606253
prior_var_Brain_Cerebellar_Hemisphere prior_var_Brain_Cortex
17.606253 17.606253
prior_var_Brain_Hypothalamus prior_var_Brain_Putamen_basal_ganglia
17.606253 17.606253 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_SNP pve_Brain_Cerebellum pve_Brain_Hippocampus
1 1-1 0.2443557 0.01942860 0.026851422
2 1-2 0.2419332 0.02677741 0.040823104
3 1-3 0.2233366 0.01842478 0.001097412
4 1-4 0.2587660 0.01628910 0.033583565
5 1-5 0.2359109 0.01597068 0.030267860
pve_Brain_Caudate_basal_ganglia pve_Brain_Cerebellar_Hemisphere
1 0.003170917 1.911470e-02
2 0.041531674 5.904227e-06
3 0.036226089 1.466889e-03
4 0.024133137 9.719500e-04
5 0.030436649 4.876164e-03
pve_Brain_Cortex pve_Brain_Hypothalamus pve_Brain_Putamen_basal_ganglia
1 1.189866e-05 0.0137061600 0.011161401
2 5.051098e-03 0.0079909213 0.001200049
3 7.119819e-03 0.0026477446 0.005896100
4 4.864122e-05 0.0008087556 0.016091104
5 1.495824e-02 0.0088015269 0.001084693colMeans(results_df[,grep("pve", names(results_df))]) pve_SNP pve_Brain_Cerebellum
0.240860492 0.019378117
pve_Brain_Hippocampus pve_Brain_Caudate_basal_ganglia
0.026524673 0.027099693
pve_Brain_Cerebellar_Hemisphere pve_Brain_Cortex
0.005287121 0.005437939
pve_Brain_Hypothalamus pve_Brain_Putamen_basal_ganglia
0.006791022 0.007086669 #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 1-1 545 58 190
2 1-2 830 72 275
3 1-3 494 61 168
4 1-4 701 70 246
5 1-5 535 56 184
n_detected_comb_twas_in_causal
1 62
2 75
3 60
4 72
5 58sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.3076199y1 <- results_df$prior_Brain_Cerebellum
y2 <- results_df$prior_Brain_Hippocampus
y3 <- results_df$prior_Brain_Caudate_basal_ganglia
y4 <- results_df$prior_Brain_Cerebellar_Hemisphere
y5 <- results_df$prior_Brain_Cortex
y6 <- results_df$prior_Brain_Hypothalamus
y7 <- results_df$prior_Brain_Putamen_basal_ganglia
truth <- rbind(c(1,0.009),c(2,0.009),c(3,0.009),c(4,0),c(5,0),c(6,0),c(7,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Liver","Lung","Whole_Blood","Adipose_Subcutaneous","Artery_Tibial","Heart_Left_Ventricle","Stomach"),ylim=c(0,0.025),ylab="Prior inclusion")
y1 <- results_df$pve_Brain_Cerebellum
y2 <- results_df$pve_Brain_Hippocampus
y3 <- results_df$pve_Brain_Caudate_basal_ganglia
y4 <- results_df$pve_Brain_Cerebellar_Hemisphere
y5 <- results_df$pve_Brain_Cortex
y6 <- results_df$pve_Brain_Hypothalamus
y7 <- results_df$pve_Brain_Putamen_basal_ganglia
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")
f1 <- plot_PIP(configtag, runtag, paste(1, 1:5, sep = "-"), main = "high gene PVE")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
Separate effect size parameters
For the cTWAS analysis, each tissue had its own prior inclusion parameter and effect size parameter.
results_dir <- "/project/xinhe/shengqian/cTWAS_simulation/simulation_correlated_seven_tissues/"
runtag = "ukb-s80.45-3_7corr"
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 1-1 211 11 8
2 1-2 233 33 29
3 1-3 215 24 21
4 1-4 212 30 29
5 1-5 254 16 15#mean percent causal using PIP > 0.8
sum(results_df$n_detected_pip_in_causal)/sum(results_df$n_detected_pip)[1] 0.8947368#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 1-1 209 41 36
2 1-2 232 72 57
3 1-3 212 41 38
4 1-4 212 54 50
5 1-5 253 49 41#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] 0.8638132#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_Brain_Cerebellum prior_Brain_Hippocampus
1 1-1 0.0002388188 0.004553117 0.0067204279
2 1-2 0.0002383224 0.008350487 0.0121854702
3 1-3 0.0002294211 0.006668920 0.0003872109
4 1-4 0.0002357946 0.003410703 0.0112904059
5 1-5 0.0002320507 0.003101128 0.0205486027
prior_Brain_Caudate_basal_ganglia prior_Brain_Cerebellar_Hemisphere
1 0.001041899 0.0043737070
2 0.012236224 0.0005124543
3 0.010046520 0.0016055117
4 0.008177793 0.0009211086
5 0.009264066 0.0009420104
prior_Brain_Cortex prior_Brain_Hypothalamus prior_Brain_Putamen_basal_ganglia
1 0.000484178 0.0037810618 0.002623404
2 0.002434187 0.0019239138 0.003480012
3 0.005639370 0.0015970038 0.002961637
4 0.000370470 0.0003570503 0.006688261
5 0.004062700 0.0049716004 0.002879252colMeans(results_df[,setdiff(grep("prior", names(results_df)), grep("prior_var", names(results_df)))]) prior_SNP prior_Brain_Cerebellum
0.0002348815 0.0052168710
prior_Brain_Hippocampus prior_Brain_Caudate_basal_ganglia
0.0102264235 0.0081533005
prior_Brain_Cerebellar_Hemisphere prior_Brain_Cortex
0.0016709584 0.0025981809
prior_Brain_Hypothalamus prior_Brain_Putamen_basal_ganglia
0.0025261260 0.0037265131 #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_Brain_Cerebellum prior_var_Brain_Hippocampus
1 1-1 8.683177 21.22213 23.507837
2 1-2 8.594290 14.60871 19.860611
3 1-3 8.182719 12.49572 43.785247
4 1-4 9.302851 30.96215 16.422893
5 1-5 8.539146 31.75806 5.129005
prior_var_Brain_Caudate_basal_ganglia prior_var_Brain_Cerebellar_Hemisphere
1 14.85855 25.591868
2 17.92465 0.862841
3 21.65575 4.287568
4 14.25179 4.583603
5 17.58322 43.927529
prior_var_Brain_Cortex prior_var_Brain_Hypothalamus
1 0.8232463 15.015413
2 4.6919025 29.792984
3 6.3542669 5.761578
4 2.6826180 14.362451
5 29.4854636 5.940242
prior_var_Brain_Putamen_basal_ganglia
1 25.579367
2 3.511273
3 9.979775
4 11.867409
5 3.332750colMeans(results_df[,grep("prior_var", names(results_df))]) prior_var_SNP prior_var_Brain_Cerebellum
8.660437 22.209353
prior_var_Brain_Hippocampus prior_var_Brain_Caudate_basal_ganglia
21.741118 17.254791
prior_var_Brain_Cerebellar_Hemisphere prior_var_Brain_Cortex
15.850682 8.807499
prior_var_Brain_Hypothalamus prior_var_Brain_Putamen_basal_ganglia
14.174534 10.854115 #PVE and mean PVE
results_df[,c(which(colnames(results_df)=="simutag"), grep("pve", names(results_df)))] simutag pve_SNP pve_Brain_Cerebellum pve_Brain_Hippocampus
1 1-1 0.2441061 0.01889163 0.027948899
2 1-2 0.2411050 0.02385036 0.042814415
3 1-3 0.2209848 0.01629251 0.002999373
4 1-4 0.2582150 0.02064650 0.032803057
5 1-5 0.2332538 0.01925506 0.018645348
pve_Brain_Caudate_basal_ganglia pve_Brain_Cerebellar_Hemisphere
1 0.002957235 2.124705e-02
2 0.041896900 8.393305e-05
3 0.041559736 1.306687e-03
4 0.022263289 8.014286e-04
5 0.031116012 7.854879e-03
pve_Brain_Cortex pve_Brain_Hypothalamus pve_Brain_Putamen_basal_ganglia
1 7.769999e-05 0.0100528192 0.012530743
2 2.226328e-03 0.0101493080 0.002281746
3 6.985253e-03 0.0016292369 0.005519178
4 1.937305e-04 0.0009080186 0.014821460
5 2.335118e-02 0.0052292231 0.001791861colMeans(results_df[,grep("pve", names(results_df))]) pve_SNP pve_Brain_Cerebellum
0.239532928 0.019787215
pve_Brain_Hippocampus pve_Brain_Caudate_basal_ganglia
0.025042218 0.027958635
pve_Brain_Cerebellar_Hemisphere pve_Brain_Cortex
0.006258796 0.006566838
pve_Brain_Hypothalamus pve_Brain_Putamen_basal_ganglia
0.005593721 0.007388997 #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 1-1 545 58 190
2 1-2 830 72 275
3 1-3 494 61 168
4 1-4 701 70 246
5 1-5 535 56 184
n_detected_comb_twas_in_causal
1 62
2 75
3 60
4 72
5 58sum(results_df$n_detected_comb_twas_in_causal)/sum(results_df$n_detected_comb_twas)[1] 0.3076199y1 <- results_df$prior_Brain_Cerebellum
y2 <- results_df$prior_Brain_Hippocampus
y3 <- results_df$prior_Brain_Caudate_basal_ganglia
y4 <- results_df$prior_Brain_Cerebellar_Hemisphere
y5 <- results_df$prior_Brain_Cortex
y6 <- results_df$prior_Brain_Hypothalamus
y7 <- results_df$prior_Brain_Putamen_basal_ganglia
truth <- rbind(c(1,0.009),c(2,0.009),c(3,0.009),c(4,0),c(5,0),c(6,0),c(7,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Liver","Lung","Whole_Blood","Adipose_Subcutaneous","Artery_Tibial","Heart_Left_Ventricle","Stomach"),ylim=c(0,0.025),ylab="Prior inclusion")
y1 <- results_df$pve_Brain_Cerebellum
y2 <- results_df$pve_Brain_Hippocampus
y3 <- results_df$pve_Brain_Caudate_basal_ganglia
y4 <- results_df$pve_Brain_Cerebellar_Hemisphere
y5 <- results_df$pve_Brain_Cortex
y6 <- results_df$pve_Brain_Hypothalamus
y7 <- results_df$pve_Brain_Putamen_basal_ganglia
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")
f1 <- plot_PIP(configtag, runtag, paste(1, 1:5, sep = "-"), main = "high gene PVE")
f1
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