Last updated: 2024-05-26
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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sum_pve_across_types(ctwas_parameters) type total_pve Adipose_Subcutaneous Adrenal_Gland Esophagus_Mucosa Liver
1 apaQTL 0.0008 0.0002 0.0000 0.0000 0.0002
2 eQTL 0.0166 0.0000 0.0014 0.0013 0.0070
3 sQTL 0.0165 0.0024 0.0001 0.0013 0.0065
4 SNP 0.0283 0.0000 0.0000 0.0000 0.0000
Spleen
1 0.0004
2 0.0069
3 0.0062
4 0.0000sum_pve_across_contexts(ctwas_parameters) type total_pve apaQTL eQTL sQTL
1 Adipose_Subcutaneous 0.0026 2e-04 0.0000 0.0024
2 Adrenal_Gland 0.0015 0e+00 0.0014 0.0001
3 Esophagus_Mucosa 0.0026 0e+00 0.0013 0.0013
4 Liver 0.0137 2e-04 0.0070 0.0065
5 Spleen 0.0135 4e-04 0.0069 0.0062
6 SNP 0.0283 0e+00 0.0000 0.0000DT::datatable(combined_pip_by_types[combined_pip_by_types$combined_pip>0.8,])DT::datatable(combined_pip_by_contexts[combined_pip_by_contexts$combined_pip>0.8,])sum_pve_across_types(ctwas_parameters) type total_pve Adipose_Subcutaneous Cells_Cultured_fibroblasts
1 apaQTL 0.0044 0.0019 0.0003
2 eQTL 0.0508 0.0098 0.0001
3 sQTL 0.0347 0.0021 0.0081
4 SNP 0.1610 0.0000 0.0000
Esophagus_Mucosa Heart_Left_Ventricle Whole_Blood
1 0.0000 0.0000 0.0022
2 0.0028 0.0124 0.0257
3 0.0176 0.0016 0.0053
4 0.0000 0.0000 0.0000sum_pve_across_contexts(ctwas_parameters) type total_pve apaQTL eQTL sQTL
1 Adipose_Subcutaneous 0.0138 0.0019 0.0098 0.0021
2 Cells_Cultured_fibroblasts 0.0085 0.0003 0.0001 0.0081
3 Esophagus_Mucosa 0.0204 0.0000 0.0028 0.0176
4 Heart_Left_Ventricle 0.0140 0.0000 0.0124 0.0016
5 Whole_Blood 0.0332 0.0022 0.0257 0.0053
6 SNP 0.1610 0.0000 0.0000 0.0000DT::datatable(combined_pip_by_types[combined_pip_by_types$combined_pip>0.8,])DT::datatable(combined_pip_by_contexts[combined_pip_by_contexts$combined_pip>0.8,])sum_pve_across_types(ctwas_parameters) type total_pve Adrenal_Gland Artery_Coronary Brain_Cerebellum
1 apaQTL 0.0014 0.0000 0.0000 0.0000
2 eQTL 0.0286 0.0050 0.0044 0.0064
3 sQTL 0.0206 0.0029 0.0065 0.0084
4 SNP 0.1269 0.0000 0.0000 0.0000
Heart_Left_Ventricle Stomach
1 0.0014 0.0000
2 0.0087 0.0041
3 0.0028 0.0000
4 0.0000 0.0000sum_pve_across_contexts(ctwas_parameters) type total_pve apaQTL eQTL sQTL
1 Adrenal_Gland 0.0079 0.0000 0.0050 0.0029
2 Artery_Coronary 0.0109 0.0000 0.0044 0.0065
3 Brain_Cerebellum 0.0148 0.0000 0.0064 0.0084
4 Heart_Left_Ventricle 0.0129 0.0014 0.0087 0.0028
5 Stomach 0.0041 0.0000 0.0041 0.0000
6 SNP 0.1269 0.0000 0.0000 0.0000DT::datatable(combined_pip_by_types[combined_pip_by_types$combined_pip>0.8,])DT::datatable(combined_pip_by_contexts[combined_pip_by_contexts$combined_pip>0.8,])sum_pve_across_types(ctwas_parameters) type total_pve Adipose_Subcutaneous Artery_Tibial Brain_Cortex
1 apaQTL 0.0024 0.0005 0.0011 0.0004
2 eQTL 0.0163 0.0014 0.0054 0.0058
3 sQTL 0.0040 0.0000 0.0020 0.0000
4 SNP 0.0415 0.0000 0.0000 0.0000
Heart_Left_Ventricle Spleen
1 0.0004 0.0000
2 0.0001 0.0036
3 0.0019 0.0001
4 0.0000 0.0000sum_pve_across_contexts(ctwas_parameters) type total_pve apaQTL eQTL sQTL
1 Adipose_Subcutaneous 0.0019 0.0005 0.0014 0.0000
2 Artery_Tibial 0.0085 0.0011 0.0054 0.0020
3 Brain_Cortex 0.0062 0.0004 0.0058 0.0000
4 Heart_Left_Ventricle 0.0024 0.0004 0.0001 0.0019
5 Spleen 0.0037 0.0000 0.0036 0.0001
6 SNP 0.0415 0.0000 0.0000 0.0000DT::datatable(combined_pip_by_types[combined_pip_by_types$combined_pip>0.8,])DT::datatable(combined_pip_by_contexts[combined_pip_by_contexts$combined_pip>0.8,])sum_pve_across_types(ctwas_parameters) type total_pve Adipose_Subcutaneous Artery_Aorta Skin_Sun_Exposed_Lower_leg
1 apaQTL 0.0011 0.0000 0.0000 0.0006
2 eQTL 0.0353 0.0054 0.0065 0.0052
3 sQTL 0.0171 0.0075 0.0024 0.0010
4 SNP 0.0595 0.0000 0.0000 0.0000
Spleen Whole_Blood
1 0.0000 0.0005
2 0.0057 0.0125
3 0.0028 0.0034
4 0.0000 0.0000sum_pve_across_contexts(ctwas_parameters) type total_pve apaQTL eQTL sQTL
1 Adipose_Subcutaneous 0.0129 0e+00 0.0054 0.0075
2 Artery_Aorta 0.0089 0e+00 0.0065 0.0024
3 Skin_Sun_Exposed_Lower_leg 0.0068 6e-04 0.0052 0.0010
4 Spleen 0.0085 0e+00 0.0057 0.0028
5 Whole_Blood 0.0164 5e-04 0.0125 0.0034
6 SNP 0.0595 0e+00 0.0000 0.0000DT::datatable(combined_pip_by_types[combined_pip_by_types$combined_pip>0.8,])DT::datatable(combined_pip_by_contexts[combined_pip_by_contexts$combined_pip>0.8,])
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] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.4
[5] purrr_1.0.2 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[9] ggplot2_3.4.4 tidyverse_2.0.0 data.table_1.14.2 ctwas_0.2.1.9000
[13] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] colorspace_2.0-3 rjson_0.2.21
[3] ellipsis_0.3.2 rprojroot_2.0.3
[5] XVector_0.36.0 locuszoomr_0.3.0
[7] GenomicRanges_1.48.0 fs_1.5.2
[9] rstudioapi_0.14 farver_2.1.1
[11] DT_0.22 ggrepel_0.9.4
[13] bit64_4.0.5 AnnotationDbi_1.58.0
[15] fansi_1.0.3 xml2_1.3.3
[17] codetools_0.2-18 logging_0.10-108
[19] cachem_1.0.6 knitr_1.40
[21] jsonlite_1.8.8 Rsamtools_2.12.0
[23] dbplyr_2.5.0 png_0.1-7
[25] compiler_4.2.0 httr_1.4.4
[27] Matrix_1.5-3 fastmap_1.1.0
[29] lazyeval_0.2.2 cli_3.6.1
[31] later_1.3.0 htmltools_0.5.3
[33] prettyunits_1.1.1 tools_4.2.0
[35] gtable_0.3.1 glue_1.6.2
[37] GenomeInfoDbData_1.2.8 rappdirs_0.3.3
[39] Rcpp_1.0.9 Biobase_2.56.0
[41] jquerylib_0.1.4 vctrs_0.6.4
[43] Biostrings_2.64.0 rtracklayer_1.56.0
[45] crosstalk_1.2.0 xfun_0.32
[47] ps_1.7.1 timechange_0.3.0
[49] irlba_2.3.5 lifecycle_1.0.4
[51] restfulr_0.0.14 ensembldb_2.20.2
[53] XML_3.99-0.14 getPass_0.2-2
[55] zlibbioc_1.42.0 zoo_1.8-10
[57] scales_1.2.1 gggrid_0.2-0
[59] hms_1.1.2 promises_1.2.0.1
[61] MatrixGenerics_1.8.0 ProtGenerics_1.28.0
[63] parallel_4.2.0 SummarizedExperiment_1.26.1
[65] AnnotationFilter_1.20.0 LDlinkR_1.4.0
[67] yaml_2.3.5 curl_4.3.2
[69] memoise_2.0.1 sass_0.4.2
[71] biomaRt_2.52.0 stringi_1.7.8
[73] RSQLite_2.2.14 S4Vectors_0.34.0
[75] BiocIO_1.6.0 GenomicFeatures_1.48.3
[77] BiocGenerics_0.42.0 filelock_1.0.2
[79] BiocParallel_1.30.3 GenomeInfoDb_1.32.2
[81] rlang_1.1.1 pkgconfig_2.0.3
[83] matrixStats_0.62.0 bitops_1.0-7
[85] evaluate_0.16 lattice_0.20-45
[87] labeling_0.4.2 GenomicAlignments_1.32.0
[89] htmlwidgets_1.5.4 cowplot_1.1.1
[91] bit_4.0.4 processx_3.7.0
[93] tidyselect_1.2.1 plyr_1.8.7
[95] magrittr_2.0.3 R6_2.5.1
[97] IRanges_2.30.0 generics_0.1.3
[99] DelayedArray_0.22.0 DBI_1.1.3
[101] withr_2.5.0 pgenlibr_0.3.2
[103] pillar_1.9.0 whisker_0.4
[105] mixsqp_0.3-48 KEGGREST_1.36.2
[107] RCurl_1.98-1.7 crayon_1.5.1
[109] utf8_1.2.2 BiocFileCache_2.4.0
[111] plotly_4.10.0 tzdb_0.3.0
[113] rmarkdown_2.16 progress_1.2.2
[115] grid_4.2.0 blob_1.2.3
[117] callr_3.7.2 git2r_0.30.1
[119] digest_0.6.29 httpuv_1.6.5
[121] stats4_4.2.0 munsell_0.5.0
[123] viridisLite_0.4.1 bslib_0.4.0