WhiteBlood - WholeBlood gene expression
sheng Qian
2023-2-1
Last updated: 2023-02-15
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
[1] 12623[1] 11198
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1137 750 626 401 487 626 562 394 412 432 686 659 194 368 335 553
17 18 19 20 21 22
731 163 925 315 131 311 [1] 0.7637Load ctwas results
Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| 213f0e4 | sq-96 | 2023-02-15 |
#estimated group prior
estimated_group_prior <- estimated_group_prior_all[,ncol(group_prior_rec)]
print(estimated_group_prior) SNP gene
0.0002005 0.0230977 #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
17.23 20.04 #estimated enrichment
estimated_enrichment <- estimated_enrichment_all[ncol(group_prior_var_rec)]
print(estimated_enrichment)[1] 115.2#report sample size
print(sample_size)[1] 350470#report group size
print(group_size) SNP gene
8696600 11198 #estimated group PVE
estimated_group_pve <- estimated_group_pve_all[,ncol(group_prior_rec)]
print(estimated_group_pve) SNP gene
0.08573 0.01479 #total PVE
sum(estimated_group_pve)[1] 0.1005#attributable PVE
estimated_group_pve/sum(estimated_group_pve) SNP gene
0.8529 0.1471 Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z num_eqtl
9507 FES 15_43 1.0000 75.21 2.146e-04 -8.837 3
7481 TAGAP 6_103 1.0000 71.50 2.040e-04 -8.435 2
893 ARHGAP15 2_85 1.0000 51.47 1.469e-04 9.193 3
3646 BAZ2B 2_96 0.9999 73.40 2.094e-04 11.102 2
5966 VLDLR 9_3 0.9999 54.07 1.543e-04 7.817 4
1640 KIAA0391 14_9 0.9997 47.50 1.355e-04 7.370 2
7070 LAPTM5 1_20 0.9991 69.47 1.980e-04 9.117 3
10398 SLC22A4 5_79 0.9990 144.87 4.129e-04 13.754 2
5767 MED12L 3_93 0.9979 32.98 9.392e-05 -5.427 2
5908 CREB5 7_24 0.9978 368.79 1.050e-03 -20.722 1
5665 CNIH4 1_114 0.9977 102.42 2.915e-04 -9.422 2
7272 ATXN7 3_43 0.9966 48.09 1.367e-04 -3.630 3
4571 CD101 1_72 0.9959 39.88 1.133e-04 6.274 3
2611 ALDH2 12_67 0.9940 140.78 3.993e-04 -15.815 3
2818 SLC12A7 5_2 0.9933 41.51 1.177e-04 6.027 4
2131 ATP13A1 19_15 0.9932 41.79 1.184e-04 6.167 2
9863 LAMP1 13_62 0.9919 39.52 1.119e-04 -6.303 1
1102 SLC25A24 1_67 0.9914 35.48 1.004e-04 5.941 3
736 HDHD5 22_1 0.9910 25.04 7.079e-05 4.132 3
2312 LIPA 10_57 0.9886 41.77 1.178e-04 6.386 4
10100 SELL 1_83 0.9875 25.36 7.145e-05 3.904 3
5360 NLRC5 16_31 0.9859 44.70 1.258e-04 6.576 2
8044 TTC39C 18_12 0.9851 40.30 1.133e-04 5.211 1
9899 KIF18B 17_26 0.9822 26.92 7.545e-05 5.374 1
1603 SPTLC2 14_36 0.9803 23.87 6.677e-05 -4.039 2
6064 PTPRJ 11_29 0.9791 67.95 1.898e-04 -9.818 2
8108 TET2 4_69 0.9753 24.58 6.839e-05 -5.284 2
412 ARAP2 4_30 0.9741 66.74 1.855e-04 -8.262 2
6686 HIST1H2BD 6_20 0.9706 62.56 1.732e-04 9.575 1
4658 OSTF1 9_35 0.9700 21.78 6.028e-05 4.248 3
9410 DDX60L 4_109 0.9694 21.89 6.056e-05 4.461 5
9272 ZFPM1 16_54 0.9655 36.82 1.014e-04 -4.645 1
171 UQCRC1 3_34 0.9654 29.53 8.135e-05 -5.030 1
2844 CPEB4 5_104 0.9652 123.69 3.407e-04 12.452 2
3293 KLF12 13_36 0.9636 39.69 1.091e-04 -6.340 1
811 ACAP1 17_6 0.9635 62.98 1.732e-04 7.733 2
1426 POLR2E 19_2 0.9625 34.89 9.582e-05 -5.383 5
3323 NEK6 9_64 0.9573 25.83 7.057e-05 5.706 2
9755 UBOX5 20_5 0.9563 27.79 7.582e-05 -4.863 1
1160 ADD1 4_4 0.9540 33.19 9.035e-05 -7.073 1
3758 ATXN1 6_13 0.9531 65.48 1.781e-04 8.173 1
1273 GLG1 16_40 0.9503 24.89 6.748e-05 4.683 2
9287 CITED4 1_25 0.9451 27.11 7.311e-05 -4.750 2
2410 MLX 17_25 0.9418 56.88 1.529e-04 7.850 2
4385 TBC1D14 4_8 0.9394 28.71 7.694e-05 6.255 1
4883 HS6ST1 2_75 0.9379 20.23 5.413e-05 -4.140 1
10114 PAQR9 3_87 0.9354 21.31 5.689e-05 -4.082 2
982 CDC14A 1_61 0.9274 19.52 5.166e-05 3.825 2
10454 ELANE 19_2 0.9255 24.67 6.516e-05 -4.552 2
11564 CD302 2_96 0.9255 33.18 8.761e-05 -6.789 4
4103 AP1M2 19_9 0.9234 39.13 1.031e-04 5.099 4
1408 MYO9B 19_14 0.9084 28.49 7.385e-05 5.238 1
1145 ACHE 7_62 0.9074 36.67 9.493e-05 -3.852 1
8131 RNF181 2_54 0.9056 35.98 9.296e-05 -5.029 1
574 CA11 19_33 0.8993 33.13 8.502e-05 -5.574 2
380 RAI14 5_23 0.8939 19.21 4.899e-05 3.788 1
2053 CCDC9 19_33 0.8920 38.30 9.748e-05 6.874 3
9299 CCR8 3_28 0.8914 21.87 5.563e-05 -2.931 1
1386 ITPR3 6_28 0.8913 40.33 1.026e-04 6.228 5
11657 RNF139-AS1 8_82 0.8843 22.67 5.719e-05 4.450 2
5598 RORC 1_74 0.8837 20.27 5.111e-05 4.101 1
162 TRAF3IP3 1_106 0.8836 24.47 6.170e-05 4.756 2
4670 ADAM19 5_93 0.8827 22.67 5.708e-05 4.089 2
6935 CPSF4 7_61 0.8817 52.18 1.313e-04 -7.253 2
208 PPP5C 19_32 0.8812 25.24 6.346e-05 -4.940 2
5834 TNFAIP8 5_72 0.8764 54.49 1.363e-04 7.624 1
2437 SLC9A3R1 17_42 0.8750 47.02 1.174e-04 -7.630 1
2447 RAB34 17_18 0.8747 23.97 5.983e-05 -4.508 2
5078 DTNB 2_15 0.8737 22.06 5.498e-05 -4.590 2
7233 EOMES 3_20 0.8598 55.96 1.373e-04 7.596 1
8952 UBE2O 17_43 0.8587 27.63 6.769e-05 -5.502 2
755 JMJD6 17_43 0.8561 25.17 6.147e-05 4.742 1
6143 MTMR12 5_22 0.8390 20.75 4.966e-05 -4.003 1
12096 LINC01970 17_47 0.8361 31.57 7.533e-05 -5.271 1
1074 REST 4_41 0.8315 96.36 2.286e-04 9.019 1
9085 GPR4 19_32 0.8299 20.77 4.919e-05 4.252 1
8907 LRRC25 19_15 0.8298 27.13 6.425e-05 -4.768 1
2813 NPR3 5_22 0.8297 21.29 5.041e-05 4.146 1
3430 SMAD9 13_13 0.8271 22.52 5.315e-05 -4.407 2
11105 MEG3 14_52 0.8256 33.96 8.000e-05 5.342 1
4448 ZBED3 5_45 0.8219 19.79 4.641e-05 3.802 2
10656 RCSD1 1_82 0.8196 22.26 5.205e-05 4.345 3
323 RABEP1 17_5 0.8177 61.95 1.445e-04 8.751 2
10280 C20orf96 20_1 0.8123 19.78 4.584e-05 -3.889 2
6513 PXYLP1 3_86 0.8073 31.62 7.283e-05 7.219 2
1768 KLF5 13_35 0.8028 23.54 5.392e-05 -4.513 1GO enrichment analysis for genes with PIP>0.8
#number of genes for gene set enrichment
length(genes)[1] 86DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
11 Refractory anaemia with excess blasts 0.05522 1/45 1/9703
19 Malignant neoplasm of urinary bladder 0.05522 4/45 141/9703
20 Bladder Neoplasm 0.05522 4/45 140/9703
28 Cholesterol Ester Storage Disease 0.05522 1/45 1/9703
49 Freckles 0.05522 1/45 1/9703
70 Melanosis 0.05522 1/45 1/9703
71 Chloasma 0.05522 1/45 1/9703
112 Wolman Disease 0.05522 1/45 1/9703
133 Cyclic neutropenia 0.05522 1/45 1/9703
134 Cerebellar Gait Ataxia 0.05522 1/45 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...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!NULLLoading required package: S4VectorsLoading required package: stats4Loading required package: BiocGenerics
Attaching package: 'BiocGenerics'The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind, colnames,
dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which.max, which.min
Attaching package: 'S4Vectors'The following objects are masked from 'package:base':
expand.grid, I, unnameLoading required package: IRangesLoading required package: GenomicRangesLoading required package: GenomeInfoDbLoading required package: grida <- locus_plot(region_tag="17_34", return_table=T,
focus=NULL,
label_genes=NULL,
rerun_ctwas=F,
rerun_load_only=F,
label_panel="both",
legend_side="left",
legend_panel="")
| Version | Author | Date |
|---|---|---|
| 213f0e4 | sq-96 | 2023-02-15 |
a <- locus_plot(region_tag="19_32", return_table=T,
focus=NULL,
label_genes=NULL,
rerun_ctwas=F,
rerun_load_only=F,
label_panel="both",
legend_side="left",
legend_panel="")
| Version | Author | Date |
|---|---|---|
| 213f0e4 | sq-96 | 2023-02-15 |
a <- locus_plot(region_tag="12_1", return_table=T,
focus=NULL,
label_genes=NULL,
rerun_ctwas=F,
rerun_load_only=F,
label_panel="both",
legend_side="left",
legend_panel="")
a <- locus_plot(region_tag="1_20", return_table=T,
focus=NULL,
label_genes=NULL,
rerun_ctwas=F,
rerun_load_only=F,
label_panel="both",
legend_side="left",
legend_panel="")
| Version | Author | Date |
|---|---|---|
| 213f0e4 | sq-96 | 2023-02-15 |
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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] Gviz_1.38.4 GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[4] IRanges_2.28.0 S4Vectors_0.32.4 BiocGenerics_0.40.0
[7] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.1
[10] cowplot_1.1.1 ggplot2_3.4.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] backports_1.2.1 Hmisc_4.7-2
[3] BiocFileCache_2.2.1 systemfonts_1.0.4
[5] plyr_1.8.8 igraph_1.3.5
[7] lazyeval_0.2.2 splines_4.1.0
[9] BiocParallel_1.28.3 digest_0.6.31
[11] ensembldb_2.18.4 foreach_1.5.2
[13] htmltools_0.5.4 fansi_1.0.3
[15] checkmate_2.1.0 magrittr_2.0.3
[17] memoise_2.0.1 BSgenome_1.62.0
[19] cluster_2.1.2 doParallel_1.0.17
[21] tzdb_0.3.0 Biostrings_2.62.0
[23] readr_2.1.3 matrixStats_0.63.0
[25] vroom_1.6.0 svglite_2.1.0
[27] prettyunits_1.1.1 jpeg_0.1-10
[29] colorspace_2.0-3 blob_1.2.3
[31] rappdirs_0.3.3 xfun_0.35
[33] dplyr_1.0.10 callr_3.7.3
[35] crayon_1.5.2 RCurl_1.98-1.9
[37] jsonlite_1.8.4 VariantAnnotation_1.40.0
[39] survival_3.2-11 iterators_1.0.14
[41] glue_1.6.2 gtable_0.3.1
[43] zlibbioc_1.40.0 XVector_0.34.0
[45] DelayedArray_0.20.0 apcluster_1.4.10
[47] scales_1.2.1 DBI_1.1.3
[49] rngtools_1.5.2 Rcpp_1.0.9
[51] htmlTable_2.4.1 progress_1.2.2
[53] foreign_0.8-81 bit_4.0.5
[55] Formula_1.2-4 htmlwidgets_1.6.0
[57] httr_1.4.4 RColorBrewer_1.1-3
[59] ellipsis_0.3.2 pkgconfig_2.0.3
[61] XML_3.99-0.13 farver_2.1.0
[63] nnet_7.3-16 sass_0.4.4
[65] dbplyr_2.2.1 deldir_1.0-6
[67] utf8_1.2.2 tidyselect_1.2.0
[69] labeling_0.4.2 rlang_1.0.6
[71] reshape2_1.4.4 later_1.3.0
[73] AnnotationDbi_1.56.2 munsell_0.5.0
[75] tools_4.1.0 cachem_1.0.6
[77] cli_3.4.1 generics_0.1.3
[79] RSQLite_2.2.19 evaluate_0.19
[81] stringr_1.5.0 fastmap_1.1.0
[83] yaml_2.3.6 processx_3.8.0
[85] knitr_1.41 bit64_4.0.5
[87] fs_1.5.2 AnnotationFilter_1.18.0
[89] KEGGREST_1.34.0 doRNG_1.8.2
[91] whisker_0.4.1 xml2_1.3.3
[93] biomaRt_2.50.3 compiler_4.1.0
[95] rstudioapi_0.14 filelock_1.0.2
[97] curl_4.3.2 png_0.1-8
[99] tibble_3.1.8 bslib_0.4.1
[101] stringi_1.7.8 highr_0.9
[103] ps_1.7.2 GenomicFeatures_1.46.5
[105] lattice_0.20-44 ProtGenerics_1.26.0
[107] Matrix_1.3-3 vctrs_0.5.1
[109] pillar_1.8.1 lifecycle_1.0.3
[111] jquerylib_0.1.4 data.table_1.14.6
[113] bitops_1.0-7 httpuv_1.6.7
[115] rtracklayer_1.54.0 R6_2.5.1
[117] BiocIO_1.4.0 latticeExtra_0.6-30
[119] promises_1.2.0.1 gridExtra_2.3
[121] codetools_0.2-18 dichromat_2.0-0.1
[123] assertthat_0.2.1 SummarizedExperiment_1.24.0
[125] rprojroot_2.0.3 rjson_0.2.21
[127] withr_2.5.0 GenomicAlignments_1.30.0
[129] Rsamtools_2.10.0 GenomeInfoDbData_1.2.7
[131] parallel_4.1.0 hms_1.1.2
[133] rpart_4.1-15 rmarkdown_2.19
[135] MatrixGenerics_1.6.0 git2r_0.30.1
[137] biovizBase_1.42.0 getPass_0.2-2
[139] Biobase_2.54.0 base64enc_0.1-3
[141] interp_1.1-3 restfulr_0.0.15