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.
Artery Spleen Skin Lung Adipose Pancreas Heart
Artery 1 0.8 0.784 0.733 0.695 0.715 0.692
Spleen NA 1.0 0.738 0.714 0.698 0.777 0.697
Skin NA NA 1.000 0.740 0.636 0.674 0.676
Lung NA NA NA 1.000 0.618 0.630 0.662
Adipose NA NA NA NA 1.000 0.634 0.664
Pancreas NA NA NA NA NA 1.000 0.691
Heart NA NA NA NA NA NA 1.000It 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 (the first setting) to check if it helps simulation results.
Conclusion: It seems that for tissues with moderate correlation (0.6-0.8), Assigning non-zero (but very low) PVE to non-causal tissues does not outperform zero PVE case (the second simulation). cTWAS estimates parameters more accurately in the second simulation (estimated PVE very close to 0) and has lower false positive rates in the PIP calibration plot.
Simulation 1: 3% PVE for Causal Tissues and 0.5% PVE for Non-causal Tissues.
Results using PIP Threshold
simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 313 39 33
2 1-2 350 34 27
3 1-3 324 29 27
4 1-4 323 16 15
5 1-5 303 33 28[1] 0.8609272Results 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
Results using PIP Threshold
simutag n_causal n_detected_pip n_detected_pip_in_causal
1 1-1 313 39 33
2 1-2 350 35 27
3 1-3 324 33 29
4 1-4 323 15 15
5 1-5 303 37 30[1] 0.8427673Results 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
Estimated PVE
Simulation 2: 3% PVE for Causal Tissues and 0% PVE for Non-causal Tissues.
Results using PIP Threshold
simutag n_causal n_detected_pip n_detected_pip_in_causal
1 2-1 251 36 33
2 2-2 275 27 23
3 2-3 262 20 18
4 2-4 248 35 27
5 2-5 258 26 24[1] 0.8680556Results 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
Separate effect size parameters
Results using PIP Threshold
simutag n_causal n_detected_pip n_detected_pip_in_causal
1 2-1 251 35 32
2 2-2 275 27 23
3 2-3 262 20 18
4 2-4 248 35 26
5 2-5 258 24 22[1] 0.858156Results 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
PIP Calibration Plot
| Version | Author | Date |
|---|---|---|
| 85b8a1a | sq-96 | 2024-01-16 |
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