BMI - Brain Cortex
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] 11472
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1144 832 640 455 556 662 528 439 419 459 689 634 234 371 365 512
17 18 19 20 21 22
717 176 886 353 119 282 [1] 8962[1] 0.7812064Load 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
0.0067252380 0.0002911334 gene snp
25.11067 17.26460 [1] 336107[1] 11472 7535010 gene snp
0.005764048 0.112682042 [1] 0.3711252 15.5332734Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
genename region_tag susie_pip mu2 PVE z
8994 NEGR1 1_46 1.0000000 17779.39337 5.289802e-02 -10.657317
13051 RP11-490G2.2 1_60 1.0000000 33092.91269 9.845946e-02 5.044019
10446 GSAP 7_49 1.0000000 32937.38554 9.799673e-02 5.259703
751 MAPK6 15_21 1.0000000 35089.11318 1.043986e-01 -4.600218
7741 PPM1M 3_36 0.9999999 247.29833 7.357726e-04 4.675362
3673 FLT3 13_7 0.9522768 33.57224 9.511870e-05 -5.359706
979 PIK3C3 18_23 0.9326376 52.81973 1.465654e-04 6.864417
8027 CASP7 10_71 0.8054467 25.18208 6.034633e-05 4.610051
9305 ASPHD1 16_24 0.7995340 119.95832 2.853578e-04 -11.937656
3823 PREX1 20_30 0.7871489 34.78058 8.145469e-05 5.633918
1486 RASSF7 11_1 0.7686828 22.53360 5.153474e-05 3.476827
5622 C18orf8 18_12 0.7674687 60.95167 1.391774e-04 7.723439
5021 DCAF7 17_37 0.7484079 28.87254 6.429036e-05 5.436897
10039 KCNB2 8_53 0.7451725 66.52058 1.474807e-04 -8.225507
12703 CTC-467M3.3 5_52 0.7200174 90.77823 1.944676e-04 9.482167
2989 IFT57 3_67 0.7135486 44.78834 9.508478e-05 -5.822399
13296 AARSD1 17_25 0.7088165 33.53734 7.072694e-05 5.541598
4728 YWHAQ 2_6 0.6955237 26.36178 5.455179e-05 4.910669
6022 ECE2 3_113 0.6950195 29.98887 6.201255e-05 -5.304782
10781 SF3B3 16_37 0.6810625 35.85913 7.266231e-05 -6.852259
num_eqtl
8994 2
13051 1
10446 1
751 1
7741 3
3673 1
979 2
8027 2
9305 1
3823 1
1486 1
5622 3
5021 1
10039 1
12703 1
2989 2
13296 1
4728 1
6022 1
10781 1Genes with largest effect sizes
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
genename region_tag susie_pip mu2 PVE z
9 SEMA3F 3_35 0.000000e+00 72069.90 0.000000e+00 7.681163
10678 SLC38A3 3_35 0.000000e+00 67358.48 0.000000e+00 6.725828
7734 CAMKV 3_35 0.000000e+00 52493.18 0.000000e+00 -10.226037
7917 CCDC171 9_13 0.000000e+00 50279.36 0.000000e+00 8.023170
1462 MAST3 19_14 0.000000e+00 42245.44 0.000000e+00 6.952564
31 RBM5 3_35 0.000000e+00 42132.93 0.000000e+00 12.473227
41 RBM6 3_35 0.000000e+00 40748.94 0.000000e+00 12.536042
751 MAPK6 15_21 1.000000e+00 35089.11 1.043986e-01 -4.600218
8150 LEO1 15_21 2.603458e-03 34887.56 2.702362e-04 4.602678
13051 RP11-490G2.2 1_60 1.000000e+00 33092.91 9.845946e-02 5.044019
10446 GSAP 7_49 1.000000e+00 32937.39 9.799673e-02 5.259703
6317 CNNM2 10_66 0.000000e+00 31589.44 0.000000e+00 -5.980953
5488 MFAP1 15_16 7.668311e-04 23546.53 5.372161e-05 4.302998
12490 HYPK 15_16 6.638464e-06 23445.18 4.630668e-07 4.322039
7732 RNF123 3_35 0.000000e+00 23052.76 0.000000e+00 -10.957103
11029 MRPL21 11_38 0.000000e+00 22517.24 0.000000e+00 4.215024
1379 WDR76 15_16 0.000000e+00 21366.60 0.000000e+00 4.686940
11910 CKMT1A 15_16 0.000000e+00 21087.36 0.000000e+00 4.129652
9680 DHFR2 3_59 0.000000e+00 17795.96 0.000000e+00 3.265951
8994 NEGR1 1_46 1.000000e+00 17779.39 5.289802e-02 -10.657317
num_eqtl
9 1
10678 1
7734 2
7917 2
1462 2
31 1
41 1
751 1
8150 1
13051 1
10446 1
6317 2
5488 1
12490 1
7732 1
11029 2
1379 2
11910 1
9680 3
8994 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z
751 MAPK6 15_21 1.000000000 35089.11318 1.043986e-01 -4.600218
13051 RP11-490G2.2 1_60 1.000000000 33092.91269 9.845946e-02 5.044019
10446 GSAP 7_49 1.000000000 32937.38554 9.799673e-02 5.259703
8994 NEGR1 1_46 1.000000000 17779.39337 5.289802e-02 -10.657317
10925 TTC30B 2_107 0.349013321 762.39335 7.916688e-04 -3.137443
7741 PPM1M 3_36 0.999999902 247.29833 7.357726e-04 4.675362
3029 SPCS1 3_36 0.516680612 361.49123 5.557025e-04 -5.066891
9305 ASPHD1 16_24 0.799534046 119.95832 2.853578e-04 -11.937656
8150 LEO1 15_21 0.002603458 34887.55835 2.702362e-04 4.602678
12703 CTC-467M3.3 5_52 0.720017429 90.77823 1.944676e-04 9.482167
10039 KCNB2 8_53 0.745172489 66.52058 1.474807e-04 -8.225507
979 PIK3C3 18_23 0.932637599 52.81973 1.465654e-04 6.864417
5622 C18orf8 18_12 0.767468679 60.95167 1.391774e-04 7.723439
6868 GPR61 1_67 0.582831591 80.07469 1.388548e-04 8.755235
8172 MC4R 18_34 0.344096083 130.49766 1.335995e-04 13.311794
5343 G3BP2 4_51 0.360464481 121.37899 1.301753e-04 -2.133639
7387 TAL1 1_29 0.666871581 48.91502 9.705253e-05 -6.744974
3673 FLT3 13_7 0.952276779 33.57224 9.511870e-05 -5.359706
2989 IFT57 3_67 0.713548587 44.78834 9.508478e-05 -5.822399
5424 SUOX 12_35 0.519005045 58.30476 9.003224e-05 -5.806919
num_eqtl
751 1
13051 1
10446 1
8994 2
10925 1
7741 3
3029 1
9305 1
8150 1
12703 1
10039 1
979 2
5622 3
6868 1
8172 1
5343 1
7387 2
3673 1
2989 2
5424 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z
8172 MC4R 18_34 0.344096083 130.49766 1.335995e-04 13.311794
41 RBM6 3_35 0.000000000 40748.93779 0.000000e+00 12.536042
31 RBM5 3_35 0.000000000 42132.93474 0.000000e+00 12.473227
9305 ASPHD1 16_24 0.799534046 119.95832 2.853578e-04 -11.937656
6407 TAOK2 16_24 0.051557591 125.43653 1.924151e-05 11.848686
7736 MST1R 3_35 0.000000000 6793.57015 0.000000e+00 -11.803377
9306 KCTD13 16_24 0.033410814 112.45110 1.117823e-05 11.490673
9304 SEZ6L2 16_24 0.020286967 110.70293 6.681880e-06 -11.407378
7732 RNF123 3_35 0.000000000 23052.75535 0.000000e+00 -10.957103
8634 INO80E 16_24 0.023303794 98.35711 6.819536e-06 10.794453
8994 NEGR1 1_46 1.000000000 17779.39337 5.289802e-02 -10.657317
10707 CLN3 16_23 0.089642016 68.18511 1.818543e-05 10.452595
7734 CAMKV 3_35 0.000000000 52493.17731 0.000000e+00 -10.226037
12221 RP11-196G11.6 16_24 0.336709511 81.08744 8.123280e-05 10.127704
11002 SULT1A2 16_23 0.040749579 64.25940 7.790803e-06 -10.075303
12241 NPIPB7 16_23 0.029321556 65.40877 5.706179e-06 10.037986
8290 ZNF646 16_24 0.081104970 77.81042 1.877620e-05 -10.000364
2891 COL4A3BP 5_44 0.022153965 72.01389 4.746682e-06 -9.828145
10737 SKOR1 15_31 0.542610035 52.50453 8.476314e-05 -9.635319
8993 C1QTNF4 11_29 0.008762151 87.67096 2.285541e-06 9.563515
num_eqtl
8172 1
41 1
31 1
9305 1
6407 1
7736 2
9306 1
9304 1
7732 1
8634 2
8994 2
10707 1
7734 2
12221 1
11002 2
12241 1
8290 1
2891 1
10737 1
8993 1Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
| Version | Author | Date |
|---|---|---|
| e6bc169 | sq-96 | 2022-02-13 |
[1] 0.02222803 genename region_tag susie_pip mu2 PVE z
8172 MC4R 18_34 0.344096083 130.49766 1.335995e-04 13.311794
41 RBM6 3_35 0.000000000 40748.93779 0.000000e+00 12.536042
31 RBM5 3_35 0.000000000 42132.93474 0.000000e+00 12.473227
9305 ASPHD1 16_24 0.799534046 119.95832 2.853578e-04 -11.937656
6407 TAOK2 16_24 0.051557591 125.43653 1.924151e-05 11.848686
7736 MST1R 3_35 0.000000000 6793.57015 0.000000e+00 -11.803377
9306 KCTD13 16_24 0.033410814 112.45110 1.117823e-05 11.490673
9304 SEZ6L2 16_24 0.020286967 110.70293 6.681880e-06 -11.407378
7732 RNF123 3_35 0.000000000 23052.75535 0.000000e+00 -10.957103
8634 INO80E 16_24 0.023303794 98.35711 6.819536e-06 10.794453
8994 NEGR1 1_46 1.000000000 17779.39337 5.289802e-02 -10.657317
10707 CLN3 16_23 0.089642016 68.18511 1.818543e-05 10.452595
7734 CAMKV 3_35 0.000000000 52493.17731 0.000000e+00 -10.226037
12221 RP11-196G11.6 16_24 0.336709511 81.08744 8.123280e-05 10.127704
11002 SULT1A2 16_23 0.040749579 64.25940 7.790803e-06 -10.075303
12241 NPIPB7 16_23 0.029321556 65.40877 5.706179e-06 10.037986
8290 ZNF646 16_24 0.081104970 77.81042 1.877620e-05 -10.000364
2891 COL4A3BP 5_44 0.022153965 72.01389 4.746682e-06 -9.828145
10737 SKOR1 15_31 0.542610035 52.50453 8.476314e-05 -9.635319
8993 C1QTNF4 11_29 0.008762151 87.67096 2.285541e-06 9.563515
num_eqtl
8172 1
41 1
31 1
9305 1
6407 1
7736 2
9306 1
9304 1
7732 1
8634 2
8994 2
10707 1
7734 2
12221 1
11002 2
12241 1
8290 1
2891 1
10737 1
8993 1Sensitivity, specificity and precision for silver standard genes
[1] 41[1] 26[1] 4.593514[1] 8[1] 255[1] genename region_tag susie_pip mu2 PVE z num_eqtl
<0 rows> (or 0-length row.names) ctwas TWAS
0.02439024 0.12195122 ctwas TWAS
0.9993884 0.9781583 ctwas TWAS
0.12500000 0.01960784 
| 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