Last updated: 2025-01-07

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Knit directory: multigroup_ctwas_analysis/

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Introduction

We used the LDL genes reported by multi-group analysis after postprocess to do some downstream analysiss.

library(ctwas)
library(dplyr)
library(EnsDb.Hsapiens.v86)
library(pheatmap)
ens_db <- EnsDb.Hsapiens.v86

mapping_predictdb <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/weights/mapping_files/PredictDB_mapping.RDS")
mapping_munro <- readRDS("/project2/xinhe/shared_data/multigroup_ctwas/weights/mapping_files/Munro_mapping.RDS")
mapping_two <- rbind(mapping_predictdb,mapping_munro)


plot_heatmap_byomics <- function(heatmap_data, main) {

  rownames(heatmap_data) <- heatmap_data$gene_name
  heatmap_data <- heatmap_data %>% dplyr::select(-gene_name, -combined_pip)

  if(nrow(heatmap_data) ==1){

    heatmap_data <- rbind(heatmap_data,rep(0,ncol(heatmap_data)))
    rownames(heatmap_data)[2] <- "fake_gene_for_plotting"

  }

  heatmap_matrix <- as.matrix(heatmap_data)

  p <- pheatmap(heatmap_matrix,
                cluster_rows = F,   # Cluster the rows (genes)
                cluster_cols = F,   # Cluster the columns (QTL types)
                color = colorRampPalette(c("white", "red"))(50), # Color gradient
                display_numbers = TRUE, # Display numbers in cells
                main = main,labels_row = rownames(heatmap_data), silent = T)

  return(p)
}

plot_heatmap_bytissue <- function(heatmap_data, main, tissues) {
  
  rownames(heatmap_data) <- heatmap_data$gene_name
  heatmap_data <- heatmap_data %>% dplyr::select(-gene_name, -combined_pip)
  
  pip_types <- c("|eQTL_pip", "|sQTL_pip", "|stQTL_pip")

  combinations <- expand.grid(pip_types, tissues)
  order <- paste0(combinations$Var2, combinations$Var1)
  
  heatmap_data <- heatmap_data[,order]
  
  if(nrow(heatmap_data) ==1){
    
    heatmap_data <- rbind(heatmap_data,rep(0,ncol(heatmap_data)))
    rownames(heatmap_data)[2] <- "fake_gene_for_plotting"
    
  }
  
  heatmap_matrix <- as.matrix(heatmap_data)
  
  p <- pheatmap(heatmap_matrix,
                cluster_rows = F,   # Cluster the rows (genes)
                cluster_cols = F,   # Cluster the columns (QTL types)
                color = colorRampPalette(c("white", "red"))(50), # Color gradient
                display_numbers = TRUE, # Display numbers in cells
                main = main,labels_row = rownames(heatmap_data), silent = T)
  
  return(p)
}


get_ctwas_file <- function(trait, tissue = NULL, folder_results) {
  # Build file paths
  if (is.null(tissue)) {
    file_ctwas_res_origin <- paste0(folder_results, "/", trait, "/", trait, ".finemap_regions_res.RDS")
    file_ctwas_res_regionmerge <- paste0(folder_results, "/", trait, "/", trait, ".regionmerge_finemap_regions_res.RDS")
    file_ctwas_res_ldmismatch <- paste0(folder_results, "/", trait, "/", trait, ".ldmismatch_finemap_regions_res.RDS")
  } else {
    file_ctwas_res_origin <- paste0(folder_results, "/", trait, "/", trait, "_", tissue, ".finemap_regions_res.RDS")
    file_ctwas_res_regionmerge <- paste0(folder_results, "/", trait, "/", trait, "_", tissue, ".regionmerge_finemap_regions_res.RDS")
    file_ctwas_res_ldmismatch <- paste0(folder_results, "/", trait, "/", trait, "_", tissue, ".ldmismatch_finemap_regions_res.RDS")
  }
  
  # Determine which file exists
  file_ctwas_result <- if (file.exists(file_ctwas_res_ldmismatch)) {
    file_ctwas_res_ldmismatch
  } else if (file.exists(file_ctwas_res_regionmerge)) {
    file_ctwas_res_regionmerge
  } else {
    file_ctwas_res_origin
  }
  
  return(file_ctwas_result)
}

Results

Single eQTL analysis results

trait <- "LDL-ukb-d-30780_irnt"
tissue <- "Liver"

folder_single_results <- "/project/xinhe/shengqian/single_tissue_screen/processed_weights/expression_weights/"

file_ctwas_result <- get_ctwas_file(trait, tissue, folder_single_results)

ctwas_res_single_post <- readRDS(file_ctwas_result)

z_gene_single <-readRDS(paste0(folder_single_results,"/",trait,"/",trait,"_",tissue,".z_gene.RDS"))

susie_alpha_res_single_post <- ctwas_res_single_post$susie_alpha_res
susie_alpha_res_single_post <- anno_susie_alpha_res(susie_alpha_res_single_post,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)
2025-01-07 12:45:41 INFO::Annotating susie alpha result ...
2025-01-07 12:45:41 INFO::Map molecular traits to genes
combined_pip_by_group_single <- combine_gene_pips(susie_alpha_res_single_post, 
                                             group_by = "gene_name",
                                             by = "group",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)
combined_pip_sig_single <- subset(combined_pip_by_group_single, combined_pip > 0.8)

DT::datatable(combined_pip_sig_single,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with PIP > 0.8 in single eQTL analysis, cs filtered'),options = list(pageLength = 10) )
z_gene_single <- z_gene_single %>%
  mutate(molecular_id = sub("\\|.*", "", id)) %>%  # Extract ENSG ID from id
  left_join(mapping_two %>% dplyr::select(molecular_id, gene_name), by = "molecular_id")

Multi-group analysis results

tissues <- c("Liver","Spleen","Esophagus_Gastroesophageal_Junction","Esophagus_Muscularis","Esophagus_Mucosa")

folder_multi_results <- "/project/xinhe/shengqian/ctwas_GWAS_analysis/results/"
folder_multi_post <- paste0("/project/xinhe/xsun/multi_group_ctwas/13.post_processing_0103/results_region_merge/",trait,"/")

file_ctwas_result <- get_ctwas_file(trait, tissue = NULL, folder_multi_results)


ctwas_res_multi_post <- readRDS(file_ctwas_result)


susie_alpha_res_multi_post <- ctwas_res_multi_post$susie_alpha_res
susie_alpha_res_multi_post <- anno_susie_alpha_res(susie_alpha_res_multi_post,
                                        mapping_table = mapping_two,
                                        map_by = "molecular_id",
                                        drop_unmapped = TRUE)
2025-01-07 12:45:48 INFO::Annotating susie alpha result ...
2025-01-07 12:45:48 INFO::Map molecular traits to genes
2025-01-07 12:45:49 INFO::Split PIPs for molecular traits mapped to multiple genes
combined_pip_by_group_multi <- combine_gene_pips(susie_alpha_res_multi_post, 
                                             group_by = "gene_name",
                                             by = "group",
                                             method = "combine_cs",
                                             filter_cs = TRUE,
                                             include_cs_id = F)
combined_pip_sig_multi <- subset(combined_pip_by_group_multi, combined_pip > 0.8)

DT::datatable(combined_pip_sig_multi,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with PIP > 0.8 in multi-group analysis, cs filtered'),options = list(pageLength = 10) )
z_gene_multi <- readRDS(paste0(folder_multi_results,"/",trait,"/",trait,".z_gene.RDS"))
z_gene_multi <- z_gene_multi %>%
  mutate(molecular_id = sub("\\|.*", "", id)) %>%  # Extract ENSG ID from id
  left_join(mapping_two %>% dplyr::select(molecular_id, gene_name), by = "molecular_id")

Comparing with silver standard genes

We followed the analysis in ctwas paper. The silver standard genes for LDL are:

LDL_silver <- readxl::read_excel("/project/xinhe/xsun/multi_group_ctwas/data/LDL_silver.xlsx")
LDL_silver_known <- LDL_silver[LDL_silver$annotation == "known",]
LDL_silver_bystand <- LDL_silver[LDL_silver$annotation != "known",]

DT::datatable(LDL_silver,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','The silver standard genes for LDL (from ctwas paper, table S2)'),options = list(pageLength = 10) )
stats <- data.frame(analysis = c("ctwas paper","ctwasV2 - single eQTL","ctwasV2 - multigroup"),
                    num_gene_pip08 = c(35, nrow(combined_pip_sig_single),nrow(combined_pip_sig_multi)),
                    num_gene_known_imputable = c("46 of 69 known",sum(LDL_silver_known$genename %in% z_gene_single$gene_name),sum(LDL_silver_known$genename %in% z_gene_multi$gene_name)),
                    num_gene_known_pip08 = c(6,sum(LDL_silver_known$genename %in% combined_pip_sig_single$gene_name),sum(LDL_silver_known$genename %in% combined_pip_sig_multi$gene_name)),
                   num_gene_bystander_imputable = c("539 of 539 bystander",sum(LDL_silver_bystand$genename %in% z_gene_single$gene_name),sum(LDL_silver_bystand$genename %in% z_gene_multi$gene_name)),
                    num_gene_bystander_pip08 = c(2,sum(LDL_silver_bystand$genename %in% combined_pip_sig_single$gene_name),sum(LDL_silver_bystand$genename %in% combined_pip_sig_multi$gene_name)))


DT::datatable(stats,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;',''),options = list(pageLength = 10) )

PIP partition for the top genes

plot_heatmap_bytissue(heatmap_data = combined_pip_sig_multi, main = "PIP partition for genes with PIP > 0.8 from multi-group analysis",tissues = tissues)

plot_heatmap_byomics(heatmap_data = combined_pip_sig_multi, main = "PIP partition for genes with PIP > 0.8 from multi-group analysis")


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] pheatmap_1.0.12           EnsDb.Hsapiens.v86_2.99.0
 [3] ensembldb_2.20.2          AnnotationFilter_1.20.0  
 [5] GenomicFeatures_1.48.3    AnnotationDbi_1.58.0     
 [7] Biobase_2.56.0            GenomicRanges_1.48.0     
 [9] GenomeInfoDb_1.39.9       IRanges_2.30.0           
[11] S4Vectors_0.34.0          BiocGenerics_0.42.0      
[13] dplyr_1.1.4               ctwas_0.4.20.9001        

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] DT_0.22                     ggrepel_0.9.1              
 [11] bit64_4.0.5                 fansi_1.0.3                
 [13] xml2_1.3.3                  codetools_0.2-18           
 [15] logging_0.10-108            cachem_1.0.6               
 [17] knitr_1.39                  jsonlite_1.8.0             
 [19] workflowr_1.7.0             Rsamtools_2.12.0           
 [21] dbplyr_2.1.1                png_0.1-7                  
 [23] readr_2.1.2                 compiler_4.2.0             
 [25] httr_1.4.3                  assertthat_0.2.1           
 [27] Matrix_1.5-3                fastmap_1.1.0              
 [29] lazyeval_0.2.2              cli_3.6.1                  
 [31] later_1.3.0                 htmltools_0.5.2            
 [33] prettyunits_1.1.1           tools_4.2.0                
 [35] gtable_0.3.0                glue_1.6.2                 
 [37] GenomeInfoDbData_1.2.8      rappdirs_0.3.3             
 [39] Rcpp_1.0.12                 cellranger_1.1.0           
 [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 RColorBrewer_1.1-3         
 [63] LDlinkR_1.2.3               yaml_2.3.5                 
 [65] curl_4.3.2                  memoise_2.0.1              
 [67] ggplot2_3.5.1               sass_0.4.1                 
 [69] biomaRt_2.54.1              stringi_1.7.6              
 [71] RSQLite_2.3.1               highr_0.9                  
 [73] BiocIO_1.6.0                filelock_1.0.2             
 [75] BiocParallel_1.30.3         rlang_1.1.2                
 [77] pkgconfig_2.0.3             matrixStats_0.62.0         
 [79] bitops_1.0-7                evaluate_0.15              
 [81] lattice_0.20-45             purrr_1.0.2                
 [83] GenomicAlignments_1.32.0    htmlwidgets_1.5.4          
 [85] cowplot_1.1.1               bit_4.0.4                  
 [87] tidyselect_1.2.0            magrittr_2.0.3             
 [89] R6_2.5.1                    generics_0.1.2             
 [91] DelayedArray_0.22.0         DBI_1.2.2                  
 [93] withr_2.5.0                 pgenlibr_0.3.3             
 [95] pillar_1.9.0                KEGGREST_1.36.3            
 [97] RCurl_1.98-1.7              mixsqp_0.3-43              
 [99] tibble_3.2.1                crayon_1.5.1               
[101] utf8_1.2.2                  BiocFileCache_2.4.0        
[103] plotly_4.10.0               tzdb_0.4.0                 
[105] rmarkdown_2.25              progress_1.2.2             
[107] readxl_1.4.0                grid_4.2.0                 
[109] data.table_1.14.2           blob_1.2.3                 
[111] git2r_0.30.1                digest_0.6.29              
[113] tidyr_1.3.0                 httpuv_1.6.5               
[115] munsell_0.5.0               viridisLite_0.4.0          
[117] bslib_0.3.1