Last updated: 2024-01-18

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Knit directory: multigroup_ctwas_analysis/

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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, Stomach. The first three tissues are set to be causal and the other four tissues are non-causal.

            Adipose Lung Artery Stomach Heart Whole_Blood Liver
Adipose           1  0.8  0.780   0.753 0.674       0.435 0.632
Lung             NA  1.0  0.709   0.814 0.670       0.535 0.706
Artery           NA   NA  1.000   0.710 0.664       0.423 0.594
Stomach          NA   NA     NA   1.000 0.705       0.484 0.760
Heart            NA   NA     NA      NA 1.000       0.438 0.650
Whole_Blood      NA   NA     NA      NA    NA       1.000 0.498
Liver            NA   NA     NA      NA    NA          NA 1.000

Simulation 1: 3% PVE for Causal Tissues and 0% PVE for Non-causal Tissues.

Shared Prior Variance Parameters

Results using PIP Threshold

  simutag n_causal n_detected_pip n_detected_pip_in_causal
1     1-1      212             28                       23
2     1-2      233             28                       23
3     1-3      215             31                       28
4     1-4      208             20                       18
5     1-5      250             18                       17
[1] 0.872

Results using Combined PIP Threshold

  simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1     1-1               211                  48                            44
2     1-2               232                  45                            40
3     1-3               215                  48                            41
4     1-4               205                  39                            36
5     1-5               249                  38                            37
[1] 0.9082569

Estimated Prior Inclusion Probability

Version Author Date
55ada4e sq-96 2024-01-18
0a32579 sq-96 2023-12-30

Estimated PVE

Version Author Date
55ada4e sq-96 2024-01-18
2f6c0dd sq-96 2023-12-30
0a32579 sq-96 2023-12-30

Estimated Prior Variance

Version Author Date
55ada4e sq-96 2024-01-18

Estimated Enrichment

Version Author Date
55ada4e sq-96 2024-01-18

PIP Calibration Plot

Version Author Date
55ada4e sq-96 2024-01-18

Separate effect size parameters

Results using PIP Threshold

  simutag n_causal n_detected_pip n_detected_pip_in_causal
1     1-1      212             26                       22
2     1-2      233             28                       23
3     1-3      215             31                       28
4     1-4      208             20                       19
5     1-5      250             20                       18
[1] 0.88

Results using Combined PIP Threshold

  simutag n_causal_combined n_detected_comb_pip n_detected_comb_pip_in_causal
1     1-1               211                  47                            43
2     1-2               232                  45                            39
3     1-3               215                  48                            41
4     1-4               205                  38                            35
5     1-5               249                  40                            39
[1] 0.9036697

Estimated Prior Inclusion Probability

Version Author Date
55ada4e sq-96 2024-01-18

Estimated PVE

Version Author Date
55ada4e sq-96 2024-01-18

Estimated Prior Variance

Version Author Date
55ada4e sq-96 2024-01-18

Estimated Enrichment

Version Author Date
55ada4e sq-96 2024-01-18

PIP Calibration Plot

Version Author Date
55ada4e sq-96 2024-01-18

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     latex2exp_0.9.6   data.table_1.14.6 ctwas_0.1.40     
[9] 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 gridExtra_2.3    tibble_3.1.8    
[37] logging_0.10-108 codetools_0.2-18 fansi_1.0.3      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