simulation_summary
2024-10-02
Last updated: 2024-11-21
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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Simulation 1: Four tissues expression trait
Four expression traits from PredictDB are used in this study, which are Liver, Adipose, Lung and stomach. Liver and Adipose are selected as causal tissues, each with 3% PVE and 0.9% prior. Lung and stomach are non-causal tissues with 0% PVE.
Number of causal genes detected
simutag ctwas_detected_genes ctwas_causal_genes total_causal_genes
1 1-1 15 13 140
2 1-2 33 28 180
3 1-3 23 22 156
4 1-4 7 6 126
5 1-5 28 22 154Estimated parameters
PIP Calibration Plot
Compare to other methods (molecular-tissue pair)
Compare to other methods (gene)
Simulation 2: Three tissues expression and splicing trait
A simulation of expression and splicing traits from three tissues is conducted to evaluate cTWAS performance (parameter estimation, PIP calibration …). Three tissues used in this simulation are Liver, Adipose and Lung. Only expression and splicing from liver are causal, each with 3% PVE, 0.9% \(\pi\). SNPs have 30% PVE, 2.5e-4 \(\pi\).
Number of causal genes detected
simutag ctwas_detected_genes ctwas_causal_genes total_causal_genes
1 1-1 22 18 198
2 1-2 15 15 191
3 1-3 21 20 197
4 1-4 19 15 181
5 1-5 13 12 203
Compare to other methods
plot_df$method <- factor(plot_df$method, levels=c("TWAS", "single-cTWAS", "multi-cTWAS"))
plot_df$ifcausal <- plot_df$ifcausal + as.numeric(as.factor(plot_df$method))*10
plot_df$ifcausal <- as.factor(plot_df$ifcausal)
library(ggpubr)
| Version | Author | Date |
|---|---|---|
| 770965a | sq-96 | 2024-10-25 |
library(ggpubr)
colset = c("#ebebeb", "#7fc97f", "#beaed4", "#fdc086")
ggbarplot(plot_df,
x = "method",
y = "count",
add = "mean_se",
fill = "method", # Change here to fill by 'method' for different colors per bar
legend = "none",
width = 0.5,
ylab = "Count",
xlab = "",
palette = colset) +
grids(linetype = "dashed") +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_y_break(c(50, 600), space = 0.5, scales = "free")
Simulation 3: Seven tissues correlated tissues expression trait
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, each with 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.
Comparing number of regions screened
Number of causal genes detected
simutag ctwas_detected_genes ctwas_causal_genes total_causal_genes
1 1-1 35 31 251
2 1-2 22 17 275
3 1-3 21 19 262
4 1-4 32 25 248
5 1-5 23 21 258
Compare to other methods (gene)
Simulation 4: Seven correlated brain tissues expression trait
A simulation of seven correlated brain tissues is conducted to evaluate cTWAS performance (parameter estimation, PIP calibration …). Three tissues are causal
Number of causal genes detected
simutag ctwas_detected_genes ctwas_causal_genes total_causal_genes
1 1-1 14 11 211
2 1-2 31 26 233
3 1-3 23 19 215
4 1-4 29 28 212
5 1-5 20 16 254Estimated parameters
Compare to other methods (gene)
Simulation 5: seven tissues uncorrelated tissues expression trait
A simulation of seven uncorrelated tissues is conducted to evaluate cTWAS performance (parameter estimation, PIP calibration …). Seven tissues used in this simulation are Liver, Lung, Whole Blood, Adipose Subcutaneous, Artery_Tibial, Heart Left Ventricle and Stomach. Pairwise correlation of gene expression are with 0.6-0.8. The first three tissues are set to be causal, each with 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.
Number of causal genes detected
simutag ctwas_detected_genes ctwas_causal_genes total_causal_genes
1 1-1 29 26 212
2 1-2 28 23 233
3 1-3 30 29 215
4 1-4 18 17 208
5 1-5 24 21 250Estimated parameters
PIP Calibration Plot of molecular traits
Compare to other methods
Compare to other methods (gene)
PIP partition across tissues
sessionInfo()R version 4.2.0 (2022-04-22)
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] dplyr_1.1.4 plyr_1.8.7 plotrix_3.8-2 cowplot_1.1.3
[5] ggpubr_0.6.0 ggplot2_3.5.1 ggbreak_0.1.2 data.table_1.16.0
[9] ctwas_0.4.15 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] backports_1.4.1 BiocFileCache_2.6.1
[3] lazyeval_0.2.2 BiocParallel_1.32.6
[5] GenomeInfoDb_1.34.9 LDlinkR_1.4.0
[7] digest_0.6.37 yulab.utils_0.1.7
[9] ensembldb_2.22.0 htmltools_0.5.8.1
[11] fansi_1.0.6 magrittr_2.0.3
[13] memoise_2.0.1 tzdb_0.4.0
[15] Biostrings_2.66.0 readr_2.1.5
[17] matrixStats_1.4.1 locuszoomr_0.3.5
[19] prettyunits_1.2.0 colorspace_2.1-1
[21] blob_1.2.4 rappdirs_0.3.3
[23] ggrepel_0.9.6 xfun_0.47
[25] callr_3.7.2 crayon_1.5.3
[27] RCurl_1.98-1.16 jsonlite_1.8.9
[29] zoo_1.8-12 glue_1.7.0
[31] gtable_0.3.5 zlibbioc_1.44.0
[33] XVector_0.38.0 DelayedArray_0.24.0
[35] car_3.1-1 BiocGenerics_0.44.0
[37] abind_1.4-5 scales_1.3.0
[39] DBI_1.2.3 rstatix_0.7.2
[41] Rcpp_1.0.13 viridisLite_0.4.2
[43] progress_1.2.3 gridGraphics_0.5-1
[45] bit_4.5.0 stats4_4.2.0
[47] htmlwidgets_1.6.4 httr_1.4.7
[49] pkgconfig_2.0.3 XML_3.99-0.14
[51] farver_2.1.2 sass_0.4.9
[53] dbplyr_2.5.0 utf8_1.2.4
[55] labeling_0.4.3 ggplotify_0.1.2
[57] tidyselect_1.2.1 rlang_1.1.4
[59] later_1.3.2 AnnotationDbi_1.60.2
[61] munsell_0.5.1 pgenlibr_0.3.7
[63] tools_4.2.0 cachem_1.1.0
[65] cli_3.6.3 generics_0.1.3
[67] RSQLite_2.3.7 broom_1.0.5
[69] evaluate_1.0.0 stringr_1.5.1
[71] fastmap_1.2.0 yaml_2.3.10
[73] processx_3.7.0 knitr_1.48
[75] bit64_4.5.2 fs_1.6.4
[77] purrr_1.0.2 KEGGREST_1.38.0
[79] AnnotationFilter_1.22.0 whisker_0.4
[81] aplot_0.2.3 xml2_1.3.3
[83] biomaRt_2.54.1 compiler_4.2.0
[85] rstudioapi_0.14 plotly_4.10.4
[87] filelock_1.0.3 curl_5.2.3
[89] png_0.1-7 ggsignif_0.6.3
[91] tibble_3.2.1 bslib_0.8.0
[93] stringi_1.8.4 highr_0.11
[95] ps_1.7.1 GenomicFeatures_1.50.4
[97] lattice_0.20-45 ProtGenerics_1.30.0
[99] Matrix_1.5-3 vctrs_0.6.5
[101] pillar_1.9.0 lifecycle_1.0.4
[103] jquerylib_0.1.4 bitops_1.0-8
[105] irlba_2.3.5.1 httpuv_1.6.5
[107] patchwork_1.3.0 rtracklayer_1.58.0
[109] GenomicRanges_1.50.2 R6_2.5.1
[111] BiocIO_1.8.0 promises_1.3.0
[113] gridExtra_2.3 IRanges_2.32.0
[115] codetools_0.2-18 SummarizedExperiment_1.28.0
[117] rprojroot_2.0.3 rjson_0.2.23
[119] withr_3.0.1 GenomicAlignments_1.34.1
[121] Rsamtools_2.14.0 S4Vectors_0.36.2
[123] GenomeInfoDbData_1.2.9 parallel_4.2.0
[125] hms_1.1.3 grid_4.2.0
[127] ggfun_0.1.6 tidyr_1.3.1
[129] gggrid_0.2-0 rmarkdown_2.28
[131] MatrixGenerics_1.10.0 carData_3.0-5
[133] logging_0.10-108 git2r_0.30.1
[135] mixsqp_0.3-54 getPass_0.2-2
[137] Biobase_2.58.0 restfulr_0.0.15