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Rmd 2053ed2 XSun 2024-08-29 update
html 2053ed2 XSun 2024-08-29 update 
library(ctwas)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(RColorBrewer)
library(dplyr)
library(logging)
library(readr)
library(data.table)

load("/project2/xinhe/shared_data/multigroup_ctwas/weights/E_S_A_mapping_updated.RData")
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")

sum_pve_across_types <- function(ctwas_parameters) {
  # Round the group_pve values
  pve <- round(ctwas_parameters$group_pve, 4)
  pve <- as.data.frame(pve)

  # Extract SNP PVE for later use
  SNP_pve <- pve["SNP", ]

  # Add type and context columns
  pve$type <- sapply(rownames(pve), function(x) { unlist(strsplit(x, "[|]"))[1] })
  pve$context <- sapply(rownames(pve), function(x) { unlist(strsplit(x, "[|]"))[2] })

  # Remove rows with NA values and sort
  pve <- na.omit(pve)
  pve <- pve[order(rownames(pve)), ]

  # Aggregate PVE by type
  df_pve <- aggregate(pve$pve, by = list(pve$type), FUN = sum)
  colnames(df_pve) <- c("type", "total_pve")
  df_pve$total_pve <- round(df_pve$total_pve, 4)

  # Add context-specific columns
  for (context in unique(pve$context)) {
    context_pve <- aggregate(pve$pve, by = list(pve$type, pve$context), FUN = sum)
    context_pve <- context_pve[context_pve$Group.2 == context, ]
    colnames(context_pve)[3] <- context
    df_pve <- merge(df_pve, context_pve[, c("Group.1", context)], by.x = "type", by.y = "Group.1", all.x = TRUE)
  }

  # Insert SNP PVE
  SNP_row <- c("SNP", SNP_pve, rep(0, ncol(df_pve) - 2))
  df_pve <- rbind(df_pve, SNP_row)

  # Convert to numeric except for the type column
  df_pve[, -1] <- lapply(df_pve[, -1], as.numeric)

  # Sum all rows and add a sum_pve row
  sum_row <- colSums(df_pve[, -1], na.rm = TRUE)
  sum_row <- c("total_pve", sum_row)
  df_pve <- rbind(df_pve, sum_row)

  # Clean up row names and return
  row.names(df_pve) <- NULL
  return(df_pve)
}

palette <- c(brewer.pal(12, "Paired"), brewer.pal(12, "Set3"), brewer.pal(6, "Dark2"))

pve_pie_chart <- function(pve_vector, palette=NULL, title) {
  # Create data frame for plotting
  data <- data.frame(
    Group = names(pve_vector),
    Value = pve_vector
  )
  
  # Calculate percentages
  data$Percentage <- round(100 * data$Value / sum(data$Value), 1)
  
  # Set palette if not specified
  if (is.null(palette)) {
    palette <- brewer.pal(min(8, length(data$Group)), "Set3")
  }
  
  # Create pie chart
  ggplot(data, aes(x = "", y = Value, fill = Group)) +
    geom_bar(stat = "identity", width = 1, color = "white") +
    coord_polar(theta = "y") +  # This transforms the bar chart into a pie chart
    scale_fill_manual(values = palette, name = "Group") +
    labs(title = title, x = NULL, y = NULL) +
    theme_void() +
    theme(plot.title = element_text(hjust = 0.5), 
          legend.position = "right", 
          legend.title = element_text(face = "bold"), 
          legend.text = element_text(size = 12)) +
    geom_text(aes(label = paste(Percentage, "%", sep="")), position = position_stack(vjust = 0.5))
}

Settings

Weights:

single group: Liver, eQTL

multi group: Liver, Spleen, Adipose_Subcutaneous, Adrenal_Gland, Esophagus_Mucosa; eQTL + sQTL

  • drop_strand_ambig = TRUE,
  • scale_by_ld_variance = TRUE,
  • load_predictdb_LD = F,

Main function

  • niter_prefit = default,
  • niter = default,
  • pre-estimate L

Results

Single group

Parameters

results_dir_single <- paste0("/project/xinhe/xsun/multi_group_ctwas/xxxintalk/results_predictdb_main_single/",trait,"/")


finemap.res.single <- readRDS(paste0(results_dir_single,trait,".ctwas.res.RDS"))
snp_map.single <- readRDS(paste0(results_dir_single,trait,".snp_map.RDS"))
res.single <- finemap.res.single$finemap_res


param.single <- finemap.res.single$param
make_convergence_plots(param.single, gwas_n)

Version Author Date
2053ed2 XSun 2024-08-29 
ctwas_parameters.single <- summarize_param(param.single, gwas_n)


group_size.single <- data.frame(group = names(ctwas_parameters.single$group_size),
                         group_size = as.vector(ctwas_parameters.single$group_size))
group_size.single <- t(group_size.single)
DT::datatable(group_size.single,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Group size'),options = list(pageLength = 5) )

ctwas results

annotated_finemap_res.single <- anno_finemap_res_old(finemap_res = res.single,
                                          snp_map = snp_map.single,
                                          gene_annot = E_S_A_mapping,
                                          use_gene_pos = "mid",
                                          filter_protein_coding_genes = T,
                                          drop_unannotated_genes = T,
                                          filter_cs = T)
2024-09-05 14:58:26 INFO::Annotating ctwas finemapping result ...
2024-09-05 14:58:38 INFO::keep only protein coding genes
2024-09-05 14:58:38 INFO::keep only results in credible sets
2024-09-05 14:58:38 INFO::add gene_name and gene_type
2024-09-05 14:58:38 INFO::use gene mid positions
2024-09-05 14:58:38 INFO::add SNP positions
res_gene.single <- annotated_finemap_res.single[annotated_finemap_res.single$type != "SNP",]

combined_pip_by_context.single <- combine_gene_pips_old(finemap_res = annotated_finemap_res.single,
                                             by = "type", digits = 4)
highpip.single <- combined_pip_by_context.single[combined_pip_by_context.single$combined_pip > 0.8,]

DT::datatable(highpip.single,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes with PIP > 0.8'),options = list(pageLength = 5) )

Multi group

Parameters

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


param.multi <- finemap.res.multi$param
make_convergence_plots(param.multi, gwas_n,colors = palette)

Version Author Date
2053ed2 XSun 2024-08-29 
ctwas_parameters.multi <- summarize_param(param.multi, gwas_n)


group_size.multi <- data.frame(group = names(ctwas_parameters.multi$group_size),
                         group_size = as.vector(ctwas_parameters.multi$group_size))
group_size.multi <- t(group_size.multi)
DT::datatable(group_size.multi,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Group size'),options = list(pageLength = 5) )
para.multi <- sum_pve_across_types(ctwas_parameters.multi)
DT::datatable(para.multi,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Heritability contribution by contexts'),options = list(pageLength = 5) )
pve_vector.multi <- as.numeric(para.multi$total_pve[1:3])
names(pve_vector.multi) <- para.multi$type[1:3]
pve_pie_chart(pve_vector.multi, title = "Pie Chart of PVE across Types", palette)

Version Author Date
2053ed2 XSun 2024-08-29

ctwas results

annotated_finemap_res.multi <- anno_finemap_res_old(finemap_res = res.multi,
                                          snp_map = snp_map.multi,
                                          gene_annot = E_S_A_mapping,
                                          use_gene_pos = "mid",
                                          filter_protein_coding_genes = T,
                                          drop_unannotated_genes = T,
                                          filter_cs = T)
2024-09-05 14:59:00 INFO::Annotating ctwas finemapping result ...
2024-09-05 14:59:05 INFO::keep only protein coding genes
2024-09-05 14:59:05 INFO::keep only results in credible sets
2024-09-05 14:59:05 INFO::add gene_name and gene_type
2024-09-05 14:59:05 INFO::split PIPs for traits mapped to multiple genes
2024-09-05 14:59:05 INFO::use gene mid positions
2024-09-05 14:59:05 INFO::add SNP positions
res_gene.multi <- annotated_finemap_res.multi[annotated_finemap_res.multi$type != "SNP",]


combined_pip_by_context.multi <- combine_gene_pips_old(finemap_res = annotated_finemap_res.multi,
                                             by = "type", digits = 4)
highpip.multi <- combined_pip_by_context.multi[combined_pip_by_context.multi$combined_pip > 0.8,]

DT::datatable(highpip.multi,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Genes with PIP > 0.8'),options = list(pageLength = 5) )

Comparing old PIP and new PIP

combined_pip_by_context.multi.new <- combine_gene_pips_new(finemap_res = annotated_finemap_res.multi,
                                             by = "type", digits = 4)


merge_combined_pip <- merge(combined_pip_by_context.multi, combined_pip_by_context.multi.new, by = "gene_name")
colnames(merge_combined_pip) <- c("gene_name", "combined_pip_old","eQTL_pip","sQTL_pip","combined_pip_new","eQTL_pip.y","sQTL_pip.y")
merge_combined_pip <- merge_combined_pip[,c("gene_name", "eQTL_pip","sQTL_pip","combined_pip_old","combined_pip_new")]

ggplot(merge_combined_pip, aes(x = combined_pip_old, y = combined_pip_new)) +
  geom_point() +  # Scatter plot
  geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +  # Add y = x line
  labs(x = "Combined PIP Old", y = "Combined PIP New") +
  theme_minimal()

DT::datatable(merge_combined_pip,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Comparing old combined pip and new combined pip'),options = list(pageLength = 5) )

Comparing the single-group results and multi-group results

overlap <- highpip.multi[highpip.multi$gene_name %in% highpip.single$gene_name,]

sprintf("the number of genes reported by single group analysis: %s", nrow(highpip.single))
[1] "the number of genes reported by single group analysis: 33"
sprintf("the number of genes reported by multi group analysis: %s", nrow(highpip.multi))
[1] "the number of genes reported by multi group analysis: 65"
sprintf("the number of overlapped gene: %s", nrow(overlap))
[1] "the number of overlapped gene: 24"
DT::datatable(overlap,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','overlapped genes'),options = list(pageLength = 5) )
multi_unique <- combined_pip_by_context.multi[!combined_pip_by_context.multi$gene_name %in% overlap$gene_name,]
multi_unique <- multi_unique[multi_unique$combined_pip > 0.8,]
DT::datatable(multi_unique,caption = htmltools::tags$caption( style = 'caption-side: topleft; text-align = left; color:black;','Unique genes -- multi group'),options = list(pageLength = 5) )
unique_detail <- res_gene.multi[!res_gene.multi$gene_name %in%overlap$gene_name,]
unique_detail <- unique_detail[unique_detail$gene_name %in% highpip.multi$gene_name,]
save(unique_detail, file = "/project/xinhe/xsun/multi_group_ctwas/xxxintalk/unique_detail.rdata")

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] data.table_1.14.2         readr_2.1.2              
 [3] logging_0.10-108          dplyr_1.1.4              
 [5] RColorBrewer_1.1-3        ggplot2_3.5.1            
 [7] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.20.2         
 [9] AnnotationFilter_1.20.0   GenomicFeatures_1.48.3   
[11] AnnotationDbi_1.58.0      Biobase_2.56.0           
[13] GenomicRanges_1.48.0      GenomeInfoDb_1.39.9      
[15] IRanges_2.30.0            S4Vectors_0.34.0         
[17] BiocGenerics_0.42.0       ctwas_0.4.11             

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            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] compiler_4.2.0              httr_1.4.3                 
 [25] assertthat_0.2.1            Matrix_1.5-3               
 [27] fastmap_1.1.0               lazyeval_0.2.2             
 [29] cli_3.6.1                   later_1.3.0                
 [31] htmltools_0.5.2             prettyunits_1.1.1          
 [33] tools_4.2.0                 gtable_0.3.0               
 [35] glue_1.6.2                  GenomeInfoDbData_1.2.8     
 [37] rappdirs_0.3.3              Rcpp_1.0.12                
 [39] jquerylib_0.1.4             vctrs_0.6.5                
 [41] Biostrings_2.64.0           rtracklayer_1.56.0         
 [43] crosstalk_1.2.0             xfun_0.41                  
 [45] stringr_1.5.1               lifecycle_1.0.4            
 [47] irlba_2.3.5                 restfulr_0.0.14            
 [49] XML_3.99-0.14               zlibbioc_1.42.0            
 [51] zoo_1.8-10                  scales_1.3.0               
 [53] gggrid_0.2-0                hms_1.1.1                  
 [55] promises_1.2.0.1            MatrixGenerics_1.8.0       
 [57] ProtGenerics_1.28.0         parallel_4.2.0             
 [59] SummarizedExperiment_1.26.1 LDlinkR_1.2.3              
 [61] yaml_2.3.5                  curl_4.3.2                 
 [63] memoise_2.0.1               sass_0.4.1                 
 [65] biomaRt_2.54.1              stringi_1.7.6              
 [67] RSQLite_2.3.1               highr_0.9                  
 [69] BiocIO_1.6.0                filelock_1.0.2             
 [71] BiocParallel_1.30.3         rlang_1.1.2                
 [73] pkgconfig_2.0.3             matrixStats_0.62.0         
 [75] bitops_1.0-7                evaluate_0.15              
 [77] lattice_0.20-45             purrr_1.0.2                
 [79] labeling_0.4.2              GenomicAlignments_1.32.0   
 [81] htmlwidgets_1.5.4           cowplot_1.1.1              
 [83] bit_4.0.4                   tidyselect_1.2.0           
 [85] magrittr_2.0.3              R6_2.5.1                   
 [87] generics_0.1.2              DelayedArray_0.22.0        
 [89] DBI_1.2.2                   withr_2.5.0                
 [91] pgenlibr_0.3.3              pillar_1.9.0               
 [93] whisker_0.4                 KEGGREST_1.36.3            
 [95] RCurl_1.98-1.7              mixsqp_0.3-43              
 [97] tibble_3.2.1                crayon_1.5.1               
 [99] utf8_1.2.2                  BiocFileCache_2.4.0        
[101] plotly_4.10.0               tzdb_0.4.0                 
[103] rmarkdown_2.25              progress_1.2.2             
[105] grid_4.2.0                  blob_1.2.3                 
[107] git2r_0.30.1                digest_0.6.29              
[109] tidyr_1.3.0                 httpuv_1.6.5               
[111] munsell_0.5.0               viridisLite_0.4.0          
[113] bslib_0.3.1