cTWAS paper figures
2025-8-18
Last updated: 2025-08-18
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Knit directory: multigroup_ctwas_analysis/
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Figure 2: cTWAS estimates genetic architecture of complex traits from GTEx
Figure 2c: Partition of h2g (Multiple tissues contribute and Contribution of each modality)
TableGrob (2 x 3) "arrange": 4 grobs
z cells name grob
1 1 (2-2,1-1) arrange gtable[layout]
2 2 (2-2,2-2) arrange gtable[layout]
3 3 (2-2,3-3) arrange gtable[layout]
4 4 (1-1,1-3) arrange text[GRID.text.157]Figure 2d: Percent of TWAS regions with Coloc signals
Figure 3: Multi-cTWAS improves the discovery of candidate genes
Figure 3a: Incorporating multiple modality and tissues improves discovery power
Figure 3b: M-cTWAS identified genes with higher POPS scores
Averaged POPS scores across all traits stratified by M-cTWAS PIPs 
| Version | Author | Date |
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| ee48863 | sq-96 | 2025-08-18 |
Figure 3c: Comparison of number of signals per region between M-cTWAS, Coloc and TWAS
cTWAS tends to report single genes per locus, while coloc or TWAS report many. The number of signals of Coloc and TWAS are calcuated in regions with TWAS signals (after bonferroni correction). M-cTWAS signals are culated in regions selected by screen region step.
2025-08-18 19:21:23 INFO::Annotating susie alpha result...
2025-08-18 19:21:23 INFO::Map molecular traits to genes.
2025-08-18 19:21:26 INFO::Split PIPs for molecular traits mapped to multiple genes
2025-08-18 19:21:49 INFO::Compute combined PIPs...
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| ee48863 | sq-96 | 2025-08-18 |
Figure 3d: Compare with TGFM: compare with genes found in the paper. Show that unique genes by cTWAS are valid
For unique genes identified by M-cTWAS and TGFM, I plotted the distribution of POPS scores and showed that M-cTWAS unique genes have higher POPS score than TGFM unique genes. 
| Version | Author | Date |
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| ee48863 | sq-96 | 2025-08-18 |
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| ee48863 | sq-96 | 2025-08-18 |
Figure 4: cTWAS discovers candidate genes for complex traits and provides insights on their molecular mechanisms
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] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.22.0
[3] AnnotationFilter_1.22.0 GenomicFeatures_1.50.4
[5] AnnotationDbi_1.60.2 Biobase_2.58.0
[7] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[9] IRanges_2.32.0 S4Vectors_0.36.2
[11] BiocGenerics_0.44.0 grex_1.9
[13] patchwork_1.3.0 scales_1.3.0
[15] pheatmap_1.0.12 dplyr_1.1.4
[17] egg_0.4.5 gridExtra_2.3
[19] ggrepel_0.9.6 ggplot2_3.5.1
[21] data.table_1.16.0 ctwas_0.5.32
[23] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.1-1 rjson_0.2.23
[3] rprojroot_2.0.3 XVector_0.38.0
[5] locuszoomr_0.3.5 base64enc_0.1-3
[7] fs_1.6.4 rstudioapi_0.14
[9] farver_2.1.2 bit64_4.5.2
[11] fansi_1.0.6 xml2_1.3.3
[13] logging_0.10-108 codetools_0.2-18
[15] cachem_1.1.0 knitr_1.48
[17] jsonlite_1.8.9 Rsamtools_2.14.0
[19] dbplyr_2.5.0 png_0.1-7
[21] readr_2.1.5 compiler_4.2.0
[23] httr_1.4.7 Matrix_1.5-3
[25] fastmap_1.2.0 lazyeval_0.2.2
[27] cli_3.6.3 later_1.3.2
[29] htmltools_0.5.8.1 prettyunits_1.2.0
[31] tools_4.2.0 gtable_0.3.5
[33] glue_1.7.0 GenomeInfoDbData_1.2.9
[35] rappdirs_0.3.3 Rcpp_1.0.13
[37] jquerylib_0.1.4 vctrs_0.6.5
[39] Biostrings_2.66.0 rtracklayer_1.58.0
[41] xfun_0.47 stringr_1.5.1
[43] ps_1.7.1 irlba_2.3.5.1
[45] lifecycle_1.0.4 restfulr_0.0.15
[47] XML_3.99-0.14 zoo_1.8-12
[49] getPass_0.2-2 zlibbioc_1.44.0
[51] gggrid_0.2-0 hms_1.1.3
[53] promises_1.3.0 MatrixGenerics_1.10.0
[55] ProtGenerics_1.30.0 parallel_4.2.0
[57] SummarizedExperiment_1.28.0 RColorBrewer_1.1-3
[59] LDlinkR_1.4.0 yaml_2.3.10
[61] curl_5.2.3 memoise_2.0.1
[63] sass_0.4.9 biomaRt_2.54.1
[65] stringi_1.8.4 RSQLite_2.3.7
[67] highr_0.11 BiocIO_1.8.0
[69] filelock_1.0.3 BiocParallel_1.32.6
[71] repr_1.1.4 rlang_1.1.4
[73] pkgconfig_2.0.3 matrixStats_1.4.1
[75] bitops_1.0-8 evaluate_1.0.0
[77] lattice_0.20-45 purrr_1.0.2
[79] labeling_0.4.3 GenomicAlignments_1.34.1
[81] htmlwidgets_1.6.4 cowplot_1.1.3
[83] bit_4.5.0 processx_3.7.0
[85] tidyselect_1.2.1 magrittr_2.0.3
[87] AMR_2.1.1 R6_2.5.1
[89] generics_0.1.3 DelayedArray_0.24.0
[91] DBI_1.2.3 withr_3.0.1
[93] pgenlibr_0.3.7 pillar_1.9.0
[95] whisker_0.4 mixsqp_0.3-54
[97] KEGGREST_1.38.0 RCurl_1.98-1.16
[99] tibble_3.2.1 crayon_1.5.3
[101] utf8_1.2.4 BiocFileCache_2.6.1
[103] plotly_4.10.4 tzdb_0.4.0
[105] rmarkdown_2.28 progress_1.2.3
[107] blob_1.2.4 callr_3.7.2
[109] git2r_0.30.1 digest_0.6.37
[111] tidyr_1.3.1 httpuv_1.6.5
[113] munsell_0.5.1 viridisLite_0.4.2
[115] skimr_2.1.4 bslib_0.8.0