LDL - Liver
sheng Qian
2022-10-18
Last updated: 2023-03-30
Checks: 5 2
Knit directory: cTWAS_analysis/
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| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config_b38.R | code/ctwas_config_b38.R |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 4c76b6a | sq-96 | 2023-03-30 | update |
| html | 4c76b6a | sq-96 | 2023-03-30 | update |
Weight QC
[1] 12714[1] 10901
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1070 768 652 417 494 611 548 408 405 434 634 629 195 365 354 526
17 18 19 20 21 22
663 160 859 306 114 289 [1] 0.8365Load ctwas results
Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| 4c76b6a | sq-96 | 2023-03-30 |
#estimated group prior
estimated_group_prior <- estimated_group_prior_all[,ncol(group_prior_rec)]
print(estimated_group_prior) SNP gene
0.0001507 0.0096062 #estimated group prior variance
estimated_group_prior_var <- estimated_group_prior_var_all[,ncol(group_prior_var_rec)]
print(estimated_group_prior_var) SNP gene
13.73 41.07 #estimated enrichment
estimated_enrichment <- estimated_enrichment_all[ncol(group_prior_var_rec)]
print(estimated_enrichment)[1] 63.74#report sample size
print(sample_size)[1] 343621#report group size
print(group_size) SNP gene
8696847 10901 #estimated group PVE
estimated_group_pve <- estimated_group_pve_all[,ncol(group_prior_rec)]
print(estimated_group_pve) SNP gene
0.05237 0.01252 #total PVE
sum(estimated_group_pve)[1] 0.06488#attributable PVE
estimated_group_pve/sum(estimated_group_pve) SNP gene
0.8071 0.1929 Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| 4c76b6a | sq-96 | 2023-03-30 |
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z num_eqtl
4435 PSRC1 1_67 1.0000 1668.11 4.854e-03 -41.687 1
12008 HPR 16_38 1.0000 158.39 4.609e-04 -17.963 2
3721 INSIG2 2_69 1.0000 68.27 1.987e-04 -8.983 3
5991 FADS1 11_34 0.9995 163.37 4.752e-04 12.926 2
7997 TMEM150A 2_54 0.9994 849.53 2.471e-03 4.079 1
5563 ABCG8 2_27 0.9980 310.09 9.006e-04 -20.294 1
10657 TRIM39 6_24 0.9975 71.45 2.074e-04 8.840 3
12687 RP4-781K5.7 1_121 0.9967 202.33 5.869e-04 -15.108 1
7410 ABCA1 9_53 0.9920 70.21 2.027e-04 7.982 1
1999 PRKD2 19_33 0.9871 29.98 8.613e-05 5.072 2
1597 PLTP 20_28 0.9827 62.58 1.790e-04 -5.732 1
8531 TNKS 8_12 0.9820 74.84 2.139e-04 11.039 2
9390 GAS6 13_62 0.9779 71.01 2.021e-04 -8.924 1
5544 CNIH4 1_114 0.9761 40.67 1.155e-04 6.146 2
7040 INHBB 2_70 0.9651 73.84 2.074e-04 -8.519 1
11790 CYP2A6 19_28 0.9590 31.93 8.910e-05 5.407 1
3247 KDSR 18_35 0.9570 24.60 6.851e-05 -4.526 1
6391 TTC39B 9_13 0.9516 23.08 6.391e-05 -4.334 3
6093 CSNK1G3 5_75 0.9469 83.85 2.311e-04 9.116 1
1114 SRRT 7_62 0.9386 32.69 8.929e-05 5.425 2
2092 SP4 7_19 0.9349 101.60 2.764e-04 10.693 1
8579 STAT5B 17_25 0.9298 30.76 8.324e-05 5.426 2
6778 PKN3 9_66 0.9277 47.46 1.281e-04 -6.621 1
3300 C10orf88 10_77 0.9269 36.76 9.916e-05 -6.788 2
3562 ACVR1C 2_94 0.9266 25.82 6.963e-05 -4.687 2
4704 DDX56 7_32 0.9161 58.85 1.569e-04 9.642 2
8865 FUT2 19_33 0.9022 104.08 2.733e-04 -11.927 1
6957 USP1 1_39 0.8889 253.52 6.558e-04 16.258 1
6220 PELO 5_31 0.8758 70.31 1.792e-04 8.288 2
9062 KLHDC7A 1_13 0.8541 21.86 5.434e-05 4.124 1
5415 SYTL1 1_19 0.8315 21.89 5.296e-05 -3.963 1
8931 CRACR2B 11_1 0.8189 21.62 5.151e-05 -3.990 1GO enrichment analysis for genes with PIP>0.8
#number of genes for gene set enrichment
length(genes)[1] 32DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
5 Blood Platelet Disorders 0.01102 2/16 16/9703
23 Hypercholesterolemia, Familial 0.01102 2/16 18/9703
30 Leukemia, T-Cell, Chronic 0.01102 1/16 1/9703
39 Opisthorchiasis 0.01102 1/16 1/9703
47 Tangier Disease 0.01102 1/16 1/9703
63 Caliciviridae Infections 0.01102 1/16 1/9703
69 Infections, Calicivirus 0.01102 1/16 1/9703
83 Opisthorchis felineus Infection 0.01102 1/16 1/9703
84 Opisthorchis viverrini Infection 0.01102 1/16 1/9703
97 Enteropathy-Associated T-Cell Lymphoma 0.01102 1/16 1/9703WebGestalt enrichment analysis for genes with PIP>0.5
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...
description size overlap FDR database
1 Dyslipidaemia 84 7 2.712e-05 disease_GLAD4U
2 Coronary Artery Disease 153 8 4.279e-05 disease_GLAD4U
3 Coronary Disease 171 8 6.796e-05 disease_GLAD4U
4 Arteriosclerosis 173 7 9.859e-04 disease_GLAD4U
5 Myocardial Ischemia 181 7 1.068e-03 disease_GLAD4U
6 Arterial Occlusive Diseases 174 6 9.988e-03 disease_GLAD4U
7 Cardiovascular Diseases 282 7 1.392e-02 disease_GLAD4U
8 Hypercholesterolemia 60 4 1.499e-02 disease_GLAD4U
9 Heart Diseases 227 6 2.944e-02 disease_GLAD4U
10 Cholesterol metabolism 31 3 3.711e-02 pathway_KEGG
userId
1 PSRC1;ABCG8;INSIG2;TTC39B;ABCA1;FADS1;PLTP
2 PSRC1;ABCG8;INSIG2;TTC39B;ABCA1;FADS1;FUT2;PLTP
3 PSRC1;ABCG8;INSIG2;TTC39B;ABCA1;FADS1;FUT2;PLTP
4 PSRC1;ABCG8;TTC39B;ABCA1;FADS1;HPR;PLTP
5 PSRC1;ABCG8;INSIG2;TTC39B;ABCA1;FADS1;PLTP
6 PSRC1;ABCG8;TTC39B;ABCA1;FADS1;PLTP
7 PSRC1;ABCG8;TTC39B;ABCA1;FADS1;GAS6;PLTP
8 ABCG8;INSIG2;ABCA1;PLTP
9 PSRC1;ABCG8;TTC39B;ABCA1;FADS1;PLTP
10 ABCG8;ABCA1;PLTP
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] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.1 cowplot_1.1.1
[5] ggplot2_3.4.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.4 sass_0.4.4 vroom_1.6.0 bit64_4.0.5
[5] jsonlite_1.8.4 foreach_1.5.2 bslib_0.4.1 assertthat_0.2.1
[9] getPass_0.2-2 highr_0.9 doRNG_1.8.2 blob_1.2.3
[13] yaml_2.3.6 pillar_1.8.1 RSQLite_2.2.19 lattice_0.20-44
[17] glue_1.6.2 digest_0.6.31 promises_1.2.0.1 colorspace_2.0-3
[21] htmltools_0.5.4 httpuv_1.6.7 Matrix_1.3-3 plyr_1.8.8
[25] pkgconfig_2.0.3 scales_1.2.1 svglite_2.1.0 processx_3.8.0
[29] whisker_0.4.1 later_1.3.0 tzdb_0.3.0 git2r_0.30.1
[33] tibble_3.1.8 generics_0.1.3 farver_2.1.0 ellipsis_0.3.2
[37] cachem_1.0.6 withr_2.5.0 cli_3.4.1 crayon_1.5.2
[41] magrittr_2.0.3 memoise_2.0.1 evaluate_0.19 ps_1.7.2
[45] apcluster_1.4.10 fs_1.5.2 fansi_1.0.3 doParallel_1.0.17
[49] tools_4.1.0 data.table_1.14.6 hms_1.1.2 lifecycle_1.0.3
[53] stringr_1.5.0 munsell_0.5.0 rngtools_1.5.2 callr_3.7.3
[57] compiler_4.1.0 jquerylib_0.1.4 systemfonts_1.0.4 rlang_1.0.6
[61] grid_4.1.0 iterators_1.0.14 rstudioapi_0.14 rjson_0.2.21
[65] igraph_1.3.5 labeling_0.4.2 rmarkdown_2.19 gtable_0.3.1
[69] codetools_0.2-18 DBI_1.1.3 curl_4.3.2 reshape2_1.4.4
[73] R6_2.5.1 knitr_1.41 dplyr_1.0.10 fastmap_1.1.0
[77] bit_4.0.5 utf8_1.2.2 rprojroot_2.0.3 readr_2.1.3
[81] stringi_1.7.8 parallel_4.1.0 Rcpp_1.0.9 vctrs_0.5.1
[85] tidyselect_1.2.0 xfun_0.35