White Blood - WholeBlood methylation
sheng Qian
2022-10-18
Last updated: 2023-02-15
Checks: 5 2
Knit directory: cTWAS_analysis/
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| absolute | relative |
|---|---|
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/ | data |
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config_b38.R | code/ctwas_config_b38.R |
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/mqtl/WholeBlood.db | data/mqtl/WholeBlood.db |
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/G_list.RData | data/G_list.RData |
| /project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/locus_plot.R | code/locus_plot.R |
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 213f0e4 | sq-96 | 2023-02-15 | update |
| html | 213f0e4 | sq-96 | 2023-02-15 | update |
| Rmd | ada1828 | sq-96 | 2023-02-12 | update |
| html | 5575f7f | sq-96 | 2023-01-26 | update |
| html | 684d806 | sq-96 | 2023-01-26 | update |
| Rmd | 363ce6a | sq-96 | 2023-01-26 | update |
Weight QC
[1] 13069[1] 11858
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1206 901 655 485 593 787 624 536 368 631 707 583 336 351 390 545
17 18 19 20 21 22
707 160 529 312 179 273 [1] 1Load ctwas results
Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| 684d806 | sq-96 | 2023-01-26 |
#estimated group prior
estimated_group_prior <- estimated_group_prior_all[,ncol(group_prior_rec)]
print(estimated_group_prior) SNP gene
0.0002296 0.0136450 #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
18.16 22.91 #estimated enrichment
estimated_enrichment <- estimated_enrichment_all[ncol(group_prior_var_rec)]
print(estimated_enrichment)[1] 59.42#report sample size
print(sample_size)[1] 350470#report group size
print(group_size) SNP gene
8696600 11858 #estimated group PVE
estimated_group_pve <- estimated_group_pve_all[,ncol(group_prior_rec)]
print(estimated_group_pve) SNP gene
0.10348 0.01058 #total PVE
sum(estimated_group_pve)[1] 0.1141#attributable PVE
estimated_group_pve/sum(estimated_group_pve) SNP gene
0.90726 0.09274 Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| 684d806 | sq-96 | 2023-01-26 |
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z num_eqtl
11419 <NA> 6_24 0.9871 163.38 4.602e-04 -1.928 1
11586 ACVRL1 12_32 0.9860 54.44 1.532e-04 -7.281 1
11669 NLRC5 16_30 0.9791 45.92 1.283e-04 -6.699 1
11679 MYO1C 17_2 0.9771 44.76 1.248e-04 6.588 1
11654 ITGAL 16_24 0.9681 152.53 4.213e-04 12.478 1
11858 <NA> 21_18 0.9601 62.06 1.700e-04 -7.930 1
11355 AC034220.3 5_79 0.9584 131.36 3.592e-04 -13.657 1
11521 RP11-351M16.3 10_20 0.9545 125.03 3.405e-04 -11.496 1
11788 CTC-503J8.4 19_6 0.9470 23.06 6.230e-05 -4.440 1
11323 CTD-2330K9.3 3_35 0.9463 67.85 1.832e-04 -8.414 1
11470 <NA> 6_34 0.9461 24.08 6.500e-05 -4.669 1
11534 CD6 11_34 0.9456 25.48 6.875e-05 -4.279 1
11831 <NA> 20_38 0.9364 26.24 7.012e-05 3.735 1
11555 AP000908.1 11_67 0.9357 77.08 2.058e-04 -8.003 1
11332 <NA> 4_40 0.9142 34.54 9.008e-05 -7.206 1
11272 LAPTM5 1_20 0.9132 38.28 9.975e-05 6.601 1
11507 <NA> 8_83 0.9092 29.76 7.722e-05 5.231 1
11326 <NA> 4_8 0.9029 25.77 6.638e-05 4.861 1
11318 EOMES 3_20 0.8851 60.43 1.526e-04 7.702 1
11505 RP11-136O12.2 8_83 0.8820 35.71 8.986e-05 5.719 1
11338 <NA> 5_35 0.8709 40.59 1.009e-04 -5.997 1
11502 CCT6A 7_40 0.8686 49.07 1.216e-04 -5.881 1
11703 <NA> 17_23 0.8549 65.49 1.597e-04 12.877 1
11701 PSMD3 17_23 0.8543 612.47 1.493e-03 -37.218 1
11794 LSM4 19_15 0.8142 24.47 5.684e-05 3.864 1GO enrichment analysis for genes with PIP>0.8
#number of genes for gene set enrichment
length(genes)[1] 25DisGeNET enrichment analysis for genes with PIP>0.5
Warning in disease_enrichment(entities = genes, vocabulary = "HGNC", database =
"CURATED"): Removing duplicates from input list. Description
10 Epistaxis
19 Telangiectasis
20 Pulmonary Arteriovenous Fistulas
22 Congenital pulmonary arteriovenous malformation
26 Arteriovenous malformation of liver
34 HEREDITARY HEMORRHAGIC TELANGIECTASIA-RELATED PULMONARY ARTERIAL HYPERTENSION
35 OSLER-RENDU-WEBER SYNDROME 2
37 Pulmonary arteriovenous malformation
16 Liver Cirrhosis, Experimental
25 Sensorineural hearing loss, bilateral
FDR Ratio BgRatio
10 0.004947 1/8 1/9703
19 0.004947 1/8 1/9703
20 0.004947 1/8 1/9703
22 0.004947 1/8 1/9703
26 0.004947 1/8 1/9703
34 0.004947 1/8 1/9703
35 0.004947 1/8 1/9703
37 0.004947 1/8 1/9703
16 0.007416 4/8 774/9703
25 0.007416 1/8 3/9703WebGestalt 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="")
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="")
| Version | Author | Date |
|---|---|---|
| 213f0e4 | sq-96 | 2023-02-15 |
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