T2D - Adipose Subcutaneous
sheng Qian
2021-2-6
Last updated: 2022-02-13
Checks: 6 1
Knit directory: cTWAS_analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
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.
The command set.seed(20211220) 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.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.
| absolute | relative |
|---|---|
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/ | data |
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config.R | code/ctwas_config.R |
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 eb13ecf. 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:
Ignored files:
Ignored: .ipynb_checkpoints/
Untracked files:
Untracked: code/.ipynb_checkpoints/
Untracked: code/AF_out/
Untracked: code/BMI_out/
Untracked: code/T2D_out/
Untracked: code/ctwas_config.R
Untracked: code/mapping.R
Untracked: code/out/
Untracked: code/run_AF_analysis.sbatch
Untracked: code/run_AF_analysis.sh
Untracked: code/run_AF_ctwas_rss_LDR.R
Untracked: code/run_BMI_analysis.sbatch
Untracked: code/run_BMI_analysis.sh
Untracked: code/run_BMI_ctwas_rss_LDR.R
Untracked: code/run_T2D_analysis.sbatch
Untracked: code/run_T2D_analysis.sh
Untracked: code/run_T2D_ctwas_rss_LDR.R
Untracked: data/.ipynb_checkpoints/
Untracked: data/AF/
Untracked: data/BMI/
Untracked: data/T2D/
Untracked: data/UKBB/
Untracked: data/UKBB_SNPs_Info.text
Untracked: data/gene_OMIM.txt
Untracked: data/gene_pip_0.8.txt
Untracked: data/mashr_Heart_Atrial_Appendage.db
Untracked: data/summary_known_genes_annotations.xlsx
Untracked: data/untitled.txt
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/T2D_Adipose_Subcutaneous.Rmd) and HTML (docs/T2D_Adipose_Subcutaneous.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 | eb13ecf | sq-96 | 2022-02-13 | update |
| html | e6bc169 | sq-96 | 2022-02-13 | Build site. |
| Rmd | 87fee8b | sq-96 | 2022-02-13 | update |
Weight QC
[1] 12414
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1223 885 712 493 574 708 607 468 472 481 750 671 248 411 431 580
17 18 19 20 21 22
758 186 940 370 138 308 [1] 8861[1] 0.7137909Load ctwas results
Check convergence of parameters
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
gene snp
0.0066047523 0.0001681642 gene snp
3.629571 1.542519 [1] 337159[1] 12414 7535010 gene snp
0.0008826506 0.0057971306 [1] 0.008240852 0.104643058Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
genename region_tag susie_pip mu2 PVE z num_eqtl
3633 CCND2 12_4 0.9960588 26.82338 7.924350e-05 5.640253 2
4039 DAW1 2_134 0.6520366 24.10133 4.660990e-05 4.212144 2
3927 KLHL7 7_20 0.3117293 28.82218 2.664831e-05 3.761454 2
1055 ADCY2 5_7 0.3110596 29.13539 2.688004e-05 -3.602686 1
5948 SAT2 17_7 0.3034725 27.90152 2.511380e-05 3.462292 1
8729 ZNF180 19_31 0.2994262 28.43816 2.525553e-05 3.487253 2
7067 NUS1 6_78 0.2961966 28.74758 2.525496e-05 3.716370 1
14507 RP6-65G23.5 14_33 0.2820679 26.95154 2.254771e-05 3.369949 1
2137 NIPAL2 8_67 0.2774893 29.77768 2.450769e-05 -3.500588 2
2923 GNPTAB 12_61 0.2585525 26.98745 2.069549e-05 3.600816 1
7368 AP3S2 15_41 0.2457994 26.57908 1.937697e-05 -3.675343 2
11538 RABL6 9_74 0.2439157 26.89396 1.945627e-05 -3.491221 1
8342 MAMDC2 9_31 0.2436855 27.46993 1.985420e-05 3.779388 1
7460 PINK1 1_14 0.2391013 26.38648 1.871237e-05 3.208561 1
5761 DIAPH3 13_28 0.2384631 26.45894 1.871366e-05 -3.353072 1
11408 RRP7A 22_18 0.2329284 25.57072 1.766569e-05 3.082913 4
7974 LMOD1 1_102 0.2310904 25.35682 1.737969e-05 3.200403 1
9603 HPSE 4_56 0.2282649 26.26893 1.778471e-05 -3.109502 1
10568 LIPF 10_56 0.2252638 26.18648 1.749580e-05 2.991710 1
12381 KCTD11 17_6 0.2232470 25.41206 1.682638e-05 3.070309 2Genes with largest effect sizes
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
genename region_tag susie_pip mu2 PVE z num_eqtl
2137 NIPAL2 8_67 0.2774893 29.77768 2.450769e-05 -3.500588 2
1055 ADCY2 5_7 0.3110596 29.13539 2.688004e-05 -3.602686 1
3927 KLHL7 7_20 0.3117293 28.82218 2.664831e-05 3.761454 2
7067 NUS1 6_78 0.2961966 28.74758 2.525496e-05 3.716370 1
12700 LINC01537 11_41 0.1922338 28.61611 1.631569e-05 -3.271391 2
8729 ZNF180 19_31 0.2994262 28.43816 2.525553e-05 3.487253 2
5948 SAT2 17_7 0.3034725 27.90152 2.511380e-05 3.462292 1
8342 MAMDC2 9_31 0.2436855 27.46993 1.985420e-05 3.779388 1
2923 GNPTAB 12_61 0.2585525 26.98745 2.069549e-05 3.600816 1
14507 RP6-65G23.5 14_33 0.2820679 26.95154 2.254771e-05 3.369949 1
13511 AP001257.1 11_34 0.1747441 26.93090 1.395786e-05 3.363327 2
11538 RABL6 9_74 0.2439157 26.89396 1.945627e-05 -3.491221 1
3633 CCND2 12_4 0.9960588 26.82338 7.924350e-05 5.640253 2
7368 AP3S2 15_41 0.2457994 26.57908 1.937697e-05 -3.675343 2
5761 DIAPH3 13_28 0.2384631 26.45894 1.871366e-05 -3.353072 1
7460 PINK1 1_14 0.2391013 26.38648 1.871237e-05 3.208561 1
9603 HPSE 4_56 0.2282649 26.26893 1.778471e-05 -3.109502 1
12996 ZBED5 11_8 0.1569989 26.26162 1.222879e-05 -3.094448 2
10568 LIPF 10_56 0.2252638 26.18648 1.749580e-05 2.991710 1
9172 SIK2 11_66 0.2107616 25.57879 1.598957e-05 -3.728002 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3633 CCND2 12_4 0.9960588 26.82338 7.924350e-05 5.640253 2
4039 DAW1 2_134 0.6520366 24.10133 4.660990e-05 4.212144 2
1055 ADCY2 5_7 0.3110596 29.13539 2.688004e-05 -3.602686 1
3927 KLHL7 7_20 0.3117293 28.82218 2.664831e-05 3.761454 2
8729 ZNF180 19_31 0.2994262 28.43816 2.525553e-05 3.487253 2
7067 NUS1 6_78 0.2961966 28.74758 2.525496e-05 3.716370 1
5948 SAT2 17_7 0.3034725 27.90152 2.511380e-05 3.462292 1
2137 NIPAL2 8_67 0.2774893 29.77768 2.450769e-05 -3.500588 2
14507 RP6-65G23.5 14_33 0.2820679 26.95154 2.254771e-05 3.369949 1
2923 GNPTAB 12_61 0.2585525 26.98745 2.069549e-05 3.600816 1
8342 MAMDC2 9_31 0.2436855 27.46993 1.985420e-05 3.779388 1
11538 RABL6 9_74 0.2439157 26.89396 1.945627e-05 -3.491221 1
7368 AP3S2 15_41 0.2457994 26.57908 1.937697e-05 -3.675343 2
5761 DIAPH3 13_28 0.2384631 26.45894 1.871366e-05 -3.353072 1
7460 PINK1 1_14 0.2391013 26.38648 1.871237e-05 3.208561 1
9603 HPSE 4_56 0.2282649 26.26893 1.778471e-05 -3.109502 1
11408 RRP7A 22_18 0.2329284 25.57072 1.766569e-05 3.082913 4
10568 LIPF 10_56 0.2252638 26.18648 1.749580e-05 2.991710 1
7974 LMOD1 1_102 0.2310904 25.35682 1.737969e-05 3.200403 1
12381 KCTD11 17_6 0.2232470 25.41206 1.682638e-05 3.070309 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
3633 CCND2 12_4 0.99605876 26.82338 7.924350e-05 5.640253 2
4039 DAW1 2_134 0.65203660 24.10133 4.660990e-05 4.212144 2
8342 MAMDC2 9_31 0.24368551 27.46993 1.985420e-05 3.779388 1
3927 KLHL7 7_20 0.31172926 28.82218 2.664831e-05 3.761454 2
9172 SIK2 11_66 0.21076159 25.57879 1.598957e-05 -3.728002 1
7067 NUS1 6_78 0.29619664 28.74758 2.525496e-05 3.716370 1
7368 AP3S2 15_41 0.24579938 26.57908 1.937697e-05 -3.675343 2
1055 ADCY2 5_7 0.31105965 29.13539 2.688004e-05 -3.602686 1
2923 GNPTAB 12_61 0.25855245 26.98745 2.069549e-05 3.600816 1
6954 ZFP36L2 2_27 0.09181774 19.66710 5.355897e-06 -3.577139 2
1731 RBX1 22_17 0.17255431 22.56815 1.155013e-05 -3.521311 1
14659 LINC01126 2_27 0.08675796 19.20577 4.942040e-06 3.518883 2
2137 NIPAL2 8_67 0.27748935 29.77768 2.450769e-05 -3.500588 2
11538 RABL6 9_74 0.24391565 26.89396 1.945627e-05 -3.491221 1
8729 ZNF180 19_31 0.29942623 28.43816 2.525553e-05 3.487253 2
8071 SPDYA 2_17 0.13662530 21.97074 8.903094e-06 -3.478973 2
5948 SAT2 17_7 0.30347254 27.90152 2.511380e-05 3.462292 1
9512 DNAJB7 22_17 0.15659375 21.74971 1.010167e-05 3.462008 1
5651 CNOT6L 4_52 0.20603518 25.05407 1.531034e-05 3.460483 1
12362 PPP1CB 2_17 0.12457721 21.20505 7.835074e-06 3.405525 1Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
[1] 8.055421e-05 genename region_tag susie_pip mu2 PVE z num_eqtl
3633 CCND2 12_4 0.99605876 26.82338 7.924350e-05 5.640253 2
4039 DAW1 2_134 0.65203660 24.10133 4.660990e-05 4.212144 2
8342 MAMDC2 9_31 0.24368551 27.46993 1.985420e-05 3.779388 1
3927 KLHL7 7_20 0.31172926 28.82218 2.664831e-05 3.761454 2
9172 SIK2 11_66 0.21076159 25.57879 1.598957e-05 -3.728002 1
7067 NUS1 6_78 0.29619664 28.74758 2.525496e-05 3.716370 1
7368 AP3S2 15_41 0.24579938 26.57908 1.937697e-05 -3.675343 2
1055 ADCY2 5_7 0.31105965 29.13539 2.688004e-05 -3.602686 1
2923 GNPTAB 12_61 0.25855245 26.98745 2.069549e-05 3.600816 1
6954 ZFP36L2 2_27 0.09181774 19.66710 5.355897e-06 -3.577139 2
1731 RBX1 22_17 0.17255431 22.56815 1.155013e-05 -3.521311 1
14659 LINC01126 2_27 0.08675796 19.20577 4.942040e-06 3.518883 2
2137 NIPAL2 8_67 0.27748935 29.77768 2.450769e-05 -3.500588 2
11538 RABL6 9_74 0.24391565 26.89396 1.945627e-05 -3.491221 1
8729 ZNF180 19_31 0.29942623 28.43816 2.525553e-05 3.487253 2
8071 SPDYA 2_17 0.13662530 21.97074 8.903094e-06 -3.478973 2
5948 SAT2 17_7 0.30347254 27.90152 2.511380e-05 3.462292 1
9512 DNAJB7 22_17 0.15659375 21.74971 1.010167e-05 3.462008 1
5651 CNOT6L 4_52 0.20603518 25.05407 1.531034e-05 3.460483 1
12362 PPP1CB 2_17 0.12457721 21.20505 7.835074e-06 3.405525 1Sensitivity, specificity and precision for silver standard genes
[1] 72[1] 35[1] 4.609947[1] 1[1] 1[1] genename region_tag susie_pip mu2 PVE z num_eqtl
<0 rows> (or 0-length row.names)ctwas TWAS
0 0 ctwas TWAS
0.9999192 0.9999192 ctwas TWAS
0 0 
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] readxl_1.3.1 cowplot_1.0.0 ggplot2_3.3.5 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.29 purrr_0.3.4 colorspace_2.0-2
[5] vctrs_0.3.8 generics_0.1.1 htmltools_0.5.2 yaml_2.2.1
[9] utf8_1.2.2 blob_1.2.2 rlang_0.4.12 jquerylib_0.1.4
[13] later_0.8.0 pillar_1.6.4 glue_1.5.1 withr_2.4.3
[17] DBI_1.1.1 bit64_4.0.5 lifecycle_1.0.1 stringr_1.4.0
[21] cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0 evaluate_0.14
[25] memoise_2.0.1 labeling_0.4.2 knitr_1.36 fastmap_1.1.0
[29] httpuv_1.5.1 fansi_0.5.0 highr_0.9 Rcpp_1.0.7
[33] promises_1.0.1 scales_1.1.1 cachem_1.0.6 farver_2.1.0
[37] fs_1.5.2 bit_4.0.4 digest_0.6.29 stringi_1.7.6
[41] dplyr_1.0.7 rprojroot_2.0.2 grid_3.6.1 tools_3.6.1
[45] magrittr_2.0.1 tibble_3.1.6 RSQLite_2.2.8 crayon_1.4.2
[49] whisker_0.3-2 pkgconfig_2.0.3 ellipsis_0.3.2 data.table_1.14.2
[53] assertthat_0.2.1 rmarkdown_2.11 R6_2.5.1 git2r_0.26.1
[57] compiler_3.6.1