T2D - Adipose Visceral Omentum
sheng Qian
2021-2-6
Last updated: 2022-02-13
Checks: 6 1
Knit directory: cTWAS_analysis/
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| 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] 12185
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1194 835 712 475 563 699 615 440 443 493 756 683 238 409 386 560
17 18 19 20 21 22
761 189 944 358 140 292 [1] 8998[1] 0.7384489Load 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
0.0025614131 0.0001731345 gene snp
4.847456 1.566893 [1] 337159[1] 12185 7535010 gene snp
0.0004487292 0.0060627844 [1] 0.00382862 0.10984297Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
genename region_tag susie_pip mu2 PVE z
3645 CCND2 12_4 0.9946137 28.50621 8.409285e-05 5.657050
4051 DAW1 2_134 0.2557105 38.53316 2.922459e-05 3.995461
3945 KLHL7 7_20 0.2114448 35.56377 2.230335e-05 3.787363
7075 NUS1 6_78 0.1835593 34.68276 1.888232e-05 3.716370
14409 RP6-65G23.5 14_33 0.1742867 32.72766 1.691782e-05 3.369949
2121 NIPAL2 8_67 0.1635638 35.32732 1.713812e-05 -3.401950
8191 AGGF1 5_45 0.1619237 32.70607 1.570739e-05 -3.452706
13545 RP3-473L9.4 12_67 0.1553368 31.93831 1.471471e-05 -3.298735
8858 CCDC88B 11_36 0.1460633 31.69211 1.372959e-05 -3.360511
14565 LINC01126 2_27 0.1455878 33.69123 1.454812e-05 4.291921
6837 NPAS3 14_8 0.1353112 34.30489 1.376749e-05 -3.716549
9597 ARV1 1_118 0.1341710 31.39587 1.249385e-05 3.273664
12323 KCTD11 17_6 0.1333512 30.95776 1.224424e-05 3.073744
8090 YEATS2 3_112 0.1302892 31.31955 1.210289e-05 -3.202339
32 MTMR7 8_18 0.1291267 31.32164 1.199570e-05 -3.429825
6807 ABCB9 12_75 0.1274801 31.46364 1.189643e-05 3.159926
13809 IKBKE 1_105 0.1256552 30.94571 1.153310e-05 3.103061
14290 RP11-535A5.1 18_11 0.1253370 30.32443 1.127294e-05 -2.997627
11993 PHACTR4 1_19 0.1200516 31.70775 1.129012e-05 -3.507332
2524 HPS1 10_62 0.1167168 30.48916 1.055465e-05 3.123886
num_eqtl
3645 1
4051 2
3945 3
7075 1
14409 1
2121 3
8191 1
13545 1
8858 2
14565 2
6837 1
9597 2
12323 1
8090 1
32 1
6807 1
13809 2
14290 1
11993 4
2524 3Genes with largest effect sizes
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
genename region_tag susie_pip mu2 PVE z
4051 DAW1 2_134 0.25571048 38.53316 2.922459e-05 3.995461
3945 KLHL7 7_20 0.21144483 35.56377 2.230335e-05 3.787363
2121 NIPAL2 8_67 0.16356382 35.32732 1.713812e-05 -3.401950
7075 NUS1 6_78 0.18355933 34.68276 1.888232e-05 3.716370
6837 NPAS3 14_8 0.13531118 34.30489 1.376749e-05 -3.716549
14565 LINC01126 2_27 0.14558776 33.69123 1.454812e-05 4.291921
14409 RP6-65G23.5 14_33 0.17428667 32.72766 1.691782e-05 3.369949
8191 AGGF1 5_45 0.16192374 32.70607 1.570739e-05 -3.452706
13545 RP3-473L9.4 12_67 0.15533683 31.93831 1.471471e-05 -3.298735
11993 PHACTR4 1_19 0.12005158 31.70775 1.129012e-05 -3.507332
8858 CCDC88B 11_36 0.14606327 31.69211 1.372959e-05 -3.360511
6807 ABCB9 12_75 0.12748008 31.46364 1.189643e-05 3.159926
9597 ARV1 1_118 0.13417100 31.39587 1.249385e-05 3.273664
32 MTMR7 8_18 0.12912666 31.32164 1.199570e-05 -3.429825
8090 YEATS2 3_112 0.13028920 31.31955 1.210289e-05 -3.202339
13443 AP001257.1 11_34 0.09367297 31.20250 8.668999e-06 3.366387
12319 ZNF888 19_36 0.10353254 31.03883 9.531198e-06 -3.029676
12323 KCTD11 17_6 0.13335121 30.95776 1.224424e-05 3.073744
13809 IKBKE 1_105 0.12565524 30.94571 1.153310e-05 3.103061
2524 HPS1 10_62 0.11671675 30.48916 1.055465e-05 3.123886
num_eqtl
4051 2
3945 3
2121 3
7075 1
6837 1
14565 2
14409 1
8191 1
13545 1
11993 4
8858 2
6807 1
9597 2
32 1
8090 1
13443 2
12319 1
12323 1
13809 2
2524 3Genes with highest PVE
genename region_tag susie_pip mu2 PVE z
3645 CCND2 12_4 0.9946137 28.50621 8.409285e-05 5.657050
4051 DAW1 2_134 0.2557105 38.53316 2.922459e-05 3.995461
3945 KLHL7 7_20 0.2114448 35.56377 2.230335e-05 3.787363
7075 NUS1 6_78 0.1835593 34.68276 1.888232e-05 3.716370
2121 NIPAL2 8_67 0.1635638 35.32732 1.713812e-05 -3.401950
14409 RP6-65G23.5 14_33 0.1742867 32.72766 1.691782e-05 3.369949
8191 AGGF1 5_45 0.1619237 32.70607 1.570739e-05 -3.452706
13545 RP3-473L9.4 12_67 0.1553368 31.93831 1.471471e-05 -3.298735
14565 LINC01126 2_27 0.1455878 33.69123 1.454812e-05 4.291921
6837 NPAS3 14_8 0.1353112 34.30489 1.376749e-05 -3.716549
8858 CCDC88B 11_36 0.1460633 31.69211 1.372959e-05 -3.360511
9597 ARV1 1_118 0.1341710 31.39587 1.249385e-05 3.273664
12323 KCTD11 17_6 0.1333512 30.95776 1.224424e-05 3.073744
8090 YEATS2 3_112 0.1302892 31.31955 1.210289e-05 -3.202339
32 MTMR7 8_18 0.1291267 31.32164 1.199570e-05 -3.429825
6807 ABCB9 12_75 0.1274801 31.46364 1.189643e-05 3.159926
13809 IKBKE 1_105 0.1256552 30.94571 1.153310e-05 3.103061
11993 PHACTR4 1_19 0.1200516 31.70775 1.129012e-05 -3.507332
14290 RP11-535A5.1 18_11 0.1253370 30.32443 1.127294e-05 -2.997627
2524 HPS1 10_62 0.1167168 30.48916 1.055465e-05 3.123886
num_eqtl
3645 1
4051 2
3945 3
7075 1
2121 3
14409 1
8191 1
13545 1
14565 2
6837 1
8858 2
9597 2
12323 1
8090 1
32 1
6807 1
13809 2
11993 4
14290 1
2524 3Genes with largest z scores
genename region_tag susie_pip mu2 PVE z
3645 CCND2 12_4 0.99461370 28.50621 8.409285e-05 5.657050
14565 LINC01126 2_27 0.14558776 33.69123 1.454812e-05 4.291921
4051 DAW1 2_134 0.25571048 38.53316 2.922459e-05 3.995461
3945 KLHL7 7_20 0.21144483 35.56377 2.230335e-05 3.787363
6837 NPAS3 14_8 0.13531118 34.30489 1.376749e-05 -3.716549
7075 NUS1 6_78 0.18355933 34.68276 1.888232e-05 3.716370
7378 AP3S2 15_41 0.10383473 28.94874 8.915332e-06 -3.581700
1715 RBX1 22_17 0.08266243 26.75251 6.559005e-06 -3.521311
11993 PHACTR4 1_19 0.12005158 31.70775 1.129012e-05 -3.507332
12306 PPP1CB 2_17 0.07525356 26.87607 5.998712e-06 3.490303
9476 DNAJB7 22_17 0.07386308 25.78841 5.649595e-06 3.462008
5668 CNOT6L 4_52 0.10237070 29.01722 8.810423e-06 3.460483
8191 AGGF1 5_45 0.16192374 32.70607 1.570739e-05 -3.452706
13095 ARPIN 15_41 0.10007519 28.63082 8.498172e-06 -3.432049
32 MTMR7 8_18 0.12912666 31.32164 1.199570e-05 -3.429825
2121 NIPAL2 8_67 0.16356382 35.32732 1.713812e-05 -3.401950
14409 RP6-65G23.5 14_33 0.17428667 32.72766 1.691782e-05 3.369949
13443 AP001257.1 11_34 0.09367297 31.20250 8.668999e-06 3.366387
8858 CCDC88B 11_36 0.14606327 31.69211 1.372959e-05 -3.360511
10661 SH2D7 15_36 0.07646632 26.65208 6.044585e-06 -3.348970
num_eqtl
3645 1
14565 2
4051 2
3945 3
6837 1
7075 1
7378 1
1715 1
11993 4
12306 1
9476 1
5668 1
8191 1
13095 2
32 1
2121 3
14409 1
13443 2
8858 2
10661 1Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
[1] 8.206812e-05 genename region_tag susie_pip mu2 PVE z
3645 CCND2 12_4 0.99461370 28.50621 8.409285e-05 5.657050
14565 LINC01126 2_27 0.14558776 33.69123 1.454812e-05 4.291921
4051 DAW1 2_134 0.25571048 38.53316 2.922459e-05 3.995461
3945 KLHL7 7_20 0.21144483 35.56377 2.230335e-05 3.787363
6837 NPAS3 14_8 0.13531118 34.30489 1.376749e-05 -3.716549
7075 NUS1 6_78 0.18355933 34.68276 1.888232e-05 3.716370
7378 AP3S2 15_41 0.10383473 28.94874 8.915332e-06 -3.581700
1715 RBX1 22_17 0.08266243 26.75251 6.559005e-06 -3.521311
11993 PHACTR4 1_19 0.12005158 31.70775 1.129012e-05 -3.507332
12306 PPP1CB 2_17 0.07525356 26.87607 5.998712e-06 3.490303
9476 DNAJB7 22_17 0.07386308 25.78841 5.649595e-06 3.462008
5668 CNOT6L 4_52 0.10237070 29.01722 8.810423e-06 3.460483
8191 AGGF1 5_45 0.16192374 32.70607 1.570739e-05 -3.452706
13095 ARPIN 15_41 0.10007519 28.63082 8.498172e-06 -3.432049
32 MTMR7 8_18 0.12912666 31.32164 1.199570e-05 -3.429825
2121 NIPAL2 8_67 0.16356382 35.32732 1.713812e-05 -3.401950
14409 RP6-65G23.5 14_33 0.17428667 32.72766 1.691782e-05 3.369949
13443 AP001257.1 11_34 0.09367297 31.20250 8.668999e-06 3.366387
8858 CCDC88B 11_36 0.14606327 31.69211 1.372959e-05 -3.360511
10661 SH2D7 15_36 0.07646632 26.65208 6.044585e-06 -3.348970
num_eqtl
3645 1
14565 2
4051 2
3945 3
6837 1
7075 1
7378 1
1715 1
11993 4
12306 1
9476 1
5668 1
8191 1
13095 2
32 1
2121 3
14409 1
13443 2
8858 2
10661 1Sensitivity, specificity and precision for silver standard genes
[1] 72[1] 33[1] 4.606075[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
0.9999177 0.9999177 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