Comparing different PIP combining functions
XSun
2024-09-13
Last updated: 2024-09-13
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Knit directory: multigroup_ctwas_analysis/
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library(ctwas)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(RColorBrewer)
library(dplyr)
library(tidyr)
library(gridExtra)
load("/project2/xinhe/shared_data/multigroup_ctwas/weights/E_S_A_mapping_updated.RData")
colnames(E_S_A_mapping)[1] <- "molecular_id"
ens_db <- EnsDb.Hsapiens.v86
trait <- "LDL-ukb-d-30780_irnt"
gwas_n <- 343621
source("/project/xinhe/xsun/r_functions/combine_pip_old_ctwas.R")
source("/project/xinhe/xsun/r_functions/anno_finemap_res_old_ctwas.R")
palette <- c(brewer.pal(12, "Paired"), brewer.pal(12, "Set3"), brewer.pal(6, "Dark2"))LDL – eQTL + sQTL from predictdb
results_dir_multi <- paste0("/project/xinhe/xsun/multi_group_ctwas/xxxintalk/results_predictdb_main_multi/",trait,"/")
finemap.res.multi <- readRDS(paste0(results_dir_multi,trait,".ctwas.res.RDS"))
snp_map.multi <- readRDS(paste0(results_dir_multi,trait,".snp_map.RDS"))
res.multi <- finemap.res.multi$finemap_res
res.multi <- res.multi %>%
separate(id, into = c("molecular_id", "expression_info"), sep = "\\|", remove = FALSE)
res.multi <- anno_finemap_res(res.multi,
snp_map = snp_map.multi,
mapping_table = E_S_A_mapping,
add_gene_annot = TRUE,
map_by = "molecular_id",
drop_unmapped = TRUE,
add_position = TRUE,
use_gene_pos = "mid")2024-09-13 16:25:08 INFO::Annotating fine-mapping result ...
2024-09-13 16:25:08 INFO::Map molecular traits to genes
2024-09-13 16:25:08 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-09-13 16:25:13 INFO::Add gene positions
2024-09-13 16:25:13 INFO::Add SNP positionscombined_pip_cs <- combine_gene_pips(finemap_res = res.multi,
group_by = "gene_name",
by = "type",
method = "combine_cs",
filter_cs = TRUE)2024-09-13 16:25:26 INFO::Limit gene results to credible setscombined_pip_all <- combine_gene_pips(finemap_res = res.multi,
group_by = "gene_name",
by = "type",
method = "combine_all",
filter_cs = TRUE)2024-09-13 16:25:27 INFO::Limit gene results to credible setscombined_pip_sum <- combine_gene_pips(finemap_res = res.multi,
group_by = "gene_name",
by = "type",
method = "sum",
filter_cs = TRUE)2024-09-13 16:25:27 INFO::Limit gene results to credible setsmerged_pip <- full_join(combined_pip_cs, combined_pip_all, by = "gene_name", suffix = c(".cs", ".all"))
merged_pip <- full_join(merged_pip, combined_pip_sum, by = "gene_name")
merged_pip <- merged_pip[,c("gene_name","eQTL_pip.cs", "sQTL_pip.cs","combined_pip.cs","combined_pip.all","combined_pip")]
colnames(merged_pip) <- c("gene_name","eQTL_pip", "sQTL_pip","combined_pip.cs", "combined_pip.all","combined_pip.sum")
DT::datatable(merged_pip,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes with PIP > 0.8'),options = list(pageLength = 10) )sprintf("setting 1: adding up signals, we have %s genes with combined_pip > 0.8", sum(merged_pip$combined_pip.sum > 0.8))[1] "setting 1: adding up signals, we have 64 genes with combined_pip > 0.8"sprintf("setting 2: combining by CS, we have %s genes with combined_pip > 0.8", sum(merged_pip$combined_pip.cs > 0.8))[1] "setting 2: combining by CS, we have 62 genes with combined_pip > 0.8"sprintf("setting 3: combining all molecular signals with in a gene, we have %s genes with combined_pip > 0.8", sum(merged_pip$combined_pip.all > 0.8))[1] "setting 3: combining all molecular signals with in a gene, we have 37 genes with combined_pip > 0.8"print("comparing combined_pip.cs and combined_pip.sum, the two missed genes are PSRC1 and ASGR1")[1] "comparing combined_pip.cs and combined_pip.sum, the two missed genes are PSRC1 and ASGR1"p1 <- ggplot(merged_pip, aes(x = combined_pip.sum, y = combined_pip.cs)) +
geom_point() + # Scatter plot
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") + # Add y = x line
labs(x = "Combined PIP -- adding up all signals", y = "Combined by CS") +
theme_minimal()
p2 <- ggplot(merged_pip, aes(x = combined_pip.sum, y = combined_pip.all)) +
geom_point() + # Scatter plot
geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") + # Add y = x line
labs(x = "Combined PIP -- adding up all signals", y = "Combined by gene") +
theme_minimal()
grid.arrange(p1, p2, ncol = 2)
sessionInfo()R version 4.2.0 (2022-04-22)
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] C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] gridExtra_2.3 tidyr_1.3.0
[3] dplyr_1.1.4 RColorBrewer_1.1-3
[5] ggplot2_3.5.1 EnsDb.Hsapiens.v86_2.99.0
[7] ensembldb_2.20.2 AnnotationFilter_1.20.0
[9] GenomicFeatures_1.48.3 AnnotationDbi_1.58.0
[11] Biobase_2.56.0 GenomicRanges_1.48.0
[13] GenomeInfoDb_1.39.9 IRanges_2.30.0
[15] S4Vectors_0.34.0 BiocGenerics_0.42.0
[17] ctwas_0.4.12
loaded via a namespace (and not attached):
[1] colorspace_2.0-3 rjson_0.2.21
[3] ellipsis_0.3.2 rprojroot_2.0.3
[5] XVector_0.36.0 locuszoomr_0.2.1
[7] fs_1.5.2 rstudioapi_0.13
[9] farver_2.1.0 DT_0.22
[11] ggrepel_0.9.1 bit64_4.0.5
[13] fansi_1.0.3 xml2_1.3.3
[15] codetools_0.2-18 logging_0.10-108
[17] cachem_1.0.6 knitr_1.39
[19] jsonlite_1.8.0 workflowr_1.7.0
[21] Rsamtools_2.12.0 dbplyr_2.1.1
[23] png_0.1-7 readr_2.1.2
[25] compiler_4.2.0 httr_1.4.3
[27] assertthat_0.2.1 Matrix_1.5-3
[29] fastmap_1.1.0 lazyeval_0.2.2
[31] cli_3.6.1 later_1.3.0
[33] htmltools_0.5.2 prettyunits_1.1.1
[35] tools_4.2.0 gtable_0.3.0
[37] glue_1.6.2 GenomeInfoDbData_1.2.8
[39] rappdirs_0.3.3 Rcpp_1.0.12
[41] jquerylib_0.1.4 vctrs_0.6.5
[43] Biostrings_2.64.0 rtracklayer_1.56.0
[45] crosstalk_1.2.0 xfun_0.41
[47] stringr_1.5.1 lifecycle_1.0.4
[49] irlba_2.3.5 restfulr_0.0.14
[51] XML_3.99-0.14 zlibbioc_1.42.0
[53] zoo_1.8-10 scales_1.3.0
[55] gggrid_0.2-0 hms_1.1.1
[57] promises_1.2.0.1 MatrixGenerics_1.8.0
[59] ProtGenerics_1.28.0 parallel_4.2.0
[61] SummarizedExperiment_1.26.1 LDlinkR_1.2.3
[63] yaml_2.3.5 curl_4.3.2
[65] memoise_2.0.1 sass_0.4.1
[67] biomaRt_2.54.1 stringi_1.7.6
[69] RSQLite_2.3.1 highr_0.9
[71] BiocIO_1.6.0 filelock_1.0.2
[73] BiocParallel_1.30.3 rlang_1.1.2
[75] pkgconfig_2.0.3 matrixStats_0.62.0
[77] bitops_1.0-7 evaluate_0.15
[79] lattice_0.20-45 purrr_1.0.2
[81] labeling_0.4.2 GenomicAlignments_1.32.0
[83] htmlwidgets_1.5.4 cowplot_1.1.1
[85] bit_4.0.4 tidyselect_1.2.0
[87] magrittr_2.0.3 R6_2.5.1
[89] generics_0.1.2 DelayedArray_0.22.0
[91] DBI_1.2.2 withr_2.5.0
[93] pgenlibr_0.3.3 pillar_1.9.0
[95] KEGGREST_1.36.3 RCurl_1.98-1.7
[97] mixsqp_0.3-43 tibble_3.2.1
[99] crayon_1.5.1 utf8_1.2.2
[101] BiocFileCache_2.4.0 plotly_4.10.0
[103] tzdb_0.4.0 rmarkdown_2.25
[105] progress_1.2.2 grid_4.2.0
[107] data.table_1.14.2 blob_1.2.3
[109] git2r_0.30.1 digest_0.6.29
[111] httpuv_1.6.5 munsell_0.5.0
[113] viridisLite_0.4.0 bslib_0.3.1