Seven Correlated Tissues Simulation
shengqian
2023-12-30
Last updated: 2024-01-16
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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A simulation of seven correlated tissues is conducted to evaluate cTWAS performance (parameter estimation, PIP calibration …). Seven tissues used in this simulation are Artery Aorta, Spleen, Skin (not sun exposed suprapubic), Lung, Adipose Subcutaneous, Pancreas, Heart Artial Appendage. Pairwise correlation of gene expression are with 0.6-0.8. The first three tissues are set to be causal and the other four tissues are non-causal.
Adipose_Subcutaneous Lung Artery_Aorta
Adipose_Subcutaneous 1 0.8001729 0.7837312
Lung NA 1.0000000 0.7378609
Artery_Aorta NA NA 1.0000000
Heart_Atrial_Appendage NA NA NA
Skin_Not_Sun_Exposed_Suprapubic NA NA NA
Spleen NA NA NA
Pancreas NA NA NA
Heart_Atrial_Appendage
Adipose_Subcutaneous 0.7330170
Lung 0.7144555
Artery_Aorta 0.7395328
Heart_Atrial_Appendage 1.0000000
Skin_Not_Sun_Exposed_Suprapubic NA
Spleen NA
Pancreas NA
Skin_Not_Sun_Exposed_Suprapubic Spleen
Adipose_Subcutaneous 0.6954358 0.7152079
Lung 0.6977584 0.7773205
Artery_Aorta 0.6357966 0.6740020
Heart_Atrial_Appendage 0.6180385 0.6302362
Skin_Not_Sun_Exposed_Suprapubic 1.0000000 0.6336601
Spleen NA 1.0000000
Pancreas NA NA
Pancreas
Adipose_Subcutaneous 0.6917465
Lung 0.6969056
Artery_Aorta 0.6758016
Heart_Atrial_Appendage 0.6620150
Skin_Not_Sun_Exposed_Suprapubic 0.6635378
Spleen 0.6907535
Pancreas 1.0000000It current has two settings:
- 3% PVE, 0.9% \(\pi\) for causal tissues, 0.5% PVE, 0.15% \(\pi\) for non-causal tissues and 30% PVE, 2.5e-4 \(\pi\) for SNP.
- 3% PVE, 0.9% \(\pi\) for causal tissues, 0% PVE, 0% \(\pi\) for non-causal tissues and 30% PVE, 2.5e-4 \(\pi\) for SNP.
We observed that cTWAS always tend to overestimate PVE of non-causal tissues because parameters won’t be shrunk exactly to 0. Therefore, we assign non-zero (but very low) PVE to non-causal tissues in the first setting to check if it helps simulation results.
Simulation 1: 3% PVE for Causal Tissues and 0.5% PVE for Non-causal Tissues.
Results using Combined PIP Threshold
simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 312 78 70
2 1-2 345 88 77
3 1-3 323 60 58
4 1-4 320 59 56
5 1-5 302 55 51[1] 0.9176471Estimated Prior Inclusion Probability
Estimated PVE
Separate effect size parameters
For the cTWAS analysis, each tissue had its own prior inclusion parameter and effect size parameter.
Results using Combined PIP Threshold
simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 1-1 312 76 68
2 1-2 345 88 76
3 1-3 323 62 59
4 1-4 320 56 53
5 1-5 302 54 50[1] 0.9107143Estimated Prior Inclusion Probability
y1 <- results_df$prior_mashr_Artery_Aorta
y2 <- results_df$prior_mashr_Spleen
y3 <- results_df$prior_mashr_Skin_Not_Sun_Exposed_Suprapubic
y4 <- results_df$prior_mashr_Lung
y5 <- results_df$prior_mashr_Adipose_Subcutaneous
y6 <- results_df$prior_mashr_Pancreas
y7 <- results_df$prior_mashr_Heart_Atrial_Appendage
truth <- rbind(c(1,0.009),c(2,0.009),c(3,0.009),c(4,0.0015),c(5,0.0015),c(6,0.0015),c(7,0.0015))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Artery","Spleen","Skin","Lung","Adipose","Pancreas","Heart"),ylim=c(0,0.025),ylab="Prior inclusion")
Estimated PVE
y1 <- results_df$pve_mashr_Artery_Aorta
y2 <- results_df$pve_mashr_Spleen
y3 <- results_df$pve_mashr_Skin_Not_Sun_Exposed_Suprapubic
y4 <- results_df$pve_mashr_Lung
y5 <- results_df$pve_mashr_Adipose_Subcutaneous
y6 <- results_df$pve_mashr_Pancreas
y7 <- results_df$pve_mashr_Heart_Atrial_Appendage
truth <- rbind(c(1,0.03),c(2,0.03),c(3,0.03),c(4,0.005),c(5,0.005),c(6,0.005),c(7,0.005))
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("Artery","Spleen","Skin","Lung","Adipose","Pancreas","Heart"),ylim=c(0,0.06),ylab="PVE")
Simulation 2: 3% PVE for Causal Tissues and 0% PVE for Non-causal Tissues.
Results using Combined PIP Threshold
simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 2-1 250 66 58
2 2-2 274 51 44
3 2-3 261 50 44
4 2-4 246 51 39
5 2-5 255 43 40[1] 0.862069Estimated Prior Inclusion Probability
y1 <- results_df$prior_mashr_Artery_Aorta
y2 <- results_df$prior_mashr_Spleen
y3 <- results_df$prior_mashr_Skin_Not_Sun_Exposed_Suprapubic
y4 <- results_df$prior_mashr_Lung
y5 <- results_df$prior_mashr_Adipose_Subcutaneous
y6 <- results_df$prior_mashr_Pancreas
y7 <- results_df$prior_mashr_Heart_Atrial_Appendage
truth <- rbind(c(1,0.009),c(2,0.009),c(3,0.009),c(4,0),c(5,0),c(6,0),c(7,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Artery","Spleen","Skin","Lung","Adipose","Pancreas","Heart"),ylim=c(0,0.025),ylab="Prior inclusion")
Estimated PVE
y1 <- results_df$pve_mashr_Artery_Aorta
y2 <- results_df$pve_mashr_Spleen
y3 <- results_df$pve_mashr_Skin_Not_Sun_Exposed_Suprapubic
y4 <- results_df$pve_mashr_Lung
y5 <- results_df$pve_mashr_Adipose_Subcutaneous
y6 <- results_df$pve_mashr_Pancreas
y7 <- results_df$pve_mashr_Heart_Atrial_Appendage
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("Artery","Spleen","Skin","Lung","Adipose","Pancreas","Heart"),ylim=c(0,0.06),ylab="PVE")
PIP Calibration Plot
f1 <- plot_PIP(configtag, runtag, paste(2, 1:5, sep = "-"), main = "")
f1
| Version | Author | Date |
|---|---|---|
| 932e682 | sq-96 | 2024-01-15 |
Separate effect size parameters
Results using Combined PIP Threshold
simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1 2-1 250 63 56
2 2-2 274 50 43
3 2-3 261 48 41
4 2-4 246 51 39
5 2-5 255 41 39[1] 0.8616601Estimated Prior Inclusion Probability
y1 <- results_df$prior_mashr_Artery_Aorta
y2 <- results_df$prior_mashr_Spleen
y3 <- results_df$prior_mashr_Skin_Not_Sun_Exposed_Suprapubic
y4 <- results_df$prior_mashr_Lung
y5 <- results_df$prior_mashr_Adipose_Subcutaneous
y6 <- results_df$prior_mashr_Pancreas
y7 <- results_df$prior_mashr_Heart_Atrial_Appendage
truth <- rbind(c(1,0.009),c(2,0.009),c(3,0.009),c(4,0),c(5,0),c(6,0),c(7,0))
est <- rbind(cbind(1,y1),cbind(2,y2),cbind(3,y3),cbind(4,y4),cbind(5,y5),cbind(6,y6),cbind(7,y7))
plot_par_7(truth,est,xlabels = c("Artery","Spleen","Skin","Lung","Adipose","Pancreas","Heart"),ylim=c(0,0.025),ylab="Prior inclusion")
Estimated PVE
y1 <- results_df$pve_mashr_Artery_Aorta
y2 <- results_df$pve_mashr_Spleen
y3 <- results_df$pve_mashr_Skin_Not_Sun_Exposed_Suprapubic
y4 <- results_df$pve_mashr_Lung
y5 <- results_df$pve_mashr_Adipose_Subcutaneous
y6 <- results_df$pve_mashr_Pancreas
y7 <- results_df$pve_mashr_Heart_Atrial_Appendage
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("Artery","Spleen","Skin","Lung","Adipose","Pancreas","Heart"),ylim=c(0,0.06),ylab="PVE")
PIP Calibration Plot
f1 <- plot_PIP(configtag, runtag, paste(2, 1:5, sep = "-"), main = "")
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_0.1.40 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 dplyr_1.0.10 withr_2.5.0
[41] later_1.3.0 grid_4.1.0 jsonlite_1.8.4 gtable_0.3.1
[45] lifecycle_1.0.3 DBI_1.1.3 git2r_0.30.1 magrittr_2.0.3
[49] scales_1.2.1 carData_3.0-4 cli_3.6.1 stringi_1.7.8
[53] cachem_1.0.6 farver_2.1.0 ggsignif_0.6.4 fs_1.5.2
[57] promises_1.2.0.1 pgenlibr_0.3.2 bslib_0.4.1 vctrs_0.6.3
[61] generics_0.1.3 iterators_1.0.14 tools_4.1.0 glue_1.6.2
[65] purrr_1.0.2 abind_1.4-5 processx_3.8.0 fastmap_1.1.0
[69] yaml_2.3.6 colorspace_2.0-3 rstatix_0.7.2 knitr_1.41
[73] sass_0.4.4