Last updated: 2024-01-19

Checks: 6 1

Knit directory: multigroup_ctwas_analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20231112)

The command set.seed(20231112) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 653fefa

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 653fefa. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Unstaged changes:
    Modified:   analysis/index.Rmd
    Modified:   analysis/simulation_E_S_three_tissues.Rmd
    Modified:   analysis/simulation_seven_tissues_correlated.Rmd
    Modified:   analysis/simulation_seven_tissues_uncorrelated.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/simulation_E_S_three_tissues.Rmd) and HTML (docs/simulation_E_S_three_tissues.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 445a9b5 sq-96 2024-01-18 update
Rmd 206ef7b sq-96 2024-01-16 update

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, Lung, Spleen.

It current has three settings:

  • One tissue case: 3% PVE, 0.9% \(\pi\) for E and S from Liver and 30% PVE, 2.5e-4 \(\pi\) for SNP.
  • 3% PVE, 0.9% \(\pi\) for E from three tissues, 0% PVE, 0% \(\pi\) for S from three tissues and 30% PVE, 2.5e-4 \(\pi\) for SNP.
  • 3% PVE, 0.9% \(\pi\) for Liver E, Liver S and Lung E, 0% PVE, 0% \(\pi\) for the others and 30% PVE, 2.5e-4 \(\pi\) for SNP.

Findings: I think there are still correlations between E and S within a tissues. For example, in the first case, we see inflations in the PIP calibration plots.

Simulation 1: Expression and Splicing from Liver (3% PVE)

Results using PIP Threshold

  simutag n_causal n_detected_pip n_detected_pip_in_causal
1     1-1      339             51                       48
2     1-2      314             36                       32
3     1-3      296             44                       38
4     1-4      308             34                       28
5     1-5      329             33                       29
[1] 0.8838384

Estimated Prior Inclusion Probability

Estimated PVE

Estimated Prior Variance

Estimated Enrichment

PIP Calibration Plot

Simulation 2: Expression traits from three tissues are causal (3% PVE).

Results using PIP Threshold

  simutag n_causal n_detected_pip n_detected_pip_in_causal
1     2-1      222             33                       27
2     2-2      259             37                       32
3     2-3      241             32                       30
4     2-4      246             30                       26
5     2-5      248             19                       16
[1] 0.8675497

Estimated Prior Inclusion Probability

Estimated PVE

Estimated Prior Variance

Estimated Enrichment

PIP Calibration Plot

Simulation 2: Expression traits from Liver and Lung and Splicing traits from Liver are causal (3% PVE).

Results using PIP Threshold

  simutag n_causal n_detected_pip n_detected_pip_in_causal
1     3-1      274             25                       24
2     3-2      292             21                       20
3     3-3      290             13                       12
4     3-4      291             30                       26
5     3-5      268             15                       12
[1] 0.9038462

Estimated Prior Inclusion Probability

Estimated PVE

Estimated Prior Variance

Estimated Enrichment

PIP Calibration Plot


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 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