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Rmd 2ec4242 XSun 2024-10-17 update
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We summarize the results here: https://sq-96.github.io/multigroup_ctwas_analysis/multi_group_6traits_15weights_ess.html

library(ggplot2)
library(ctwas)
library(dplyr)
library(tidyr)

traits <- c("aFib-ebi-a-GCST006414","LDL-ukb-d-30780_irnt","IBD-ebi-a-GCST004131", "SBP-ukb-a-360", "SCZ-ieu-b-5102", "WBC-ieu-b-30")
load("/project2/xinhe/shared_data/multigroup_ctwas/gwas/samplesize.rdata")

sum_pve_across <- function(ctwas_parameters, by = c("type", "context")){
  pve <- round(ctwas_parameters$group_pve, 4)
  pve <- as.data.frame(pve)
  SNP_pve <- pve["SNP",]
  pve$context <- sapply(rownames(pve), function(x) { unlist(strsplit(x, "[|]"))[1] })
  pve$type <- sapply(rownames(pve), function(x) { unlist(strsplit(x, "[|]"))[2] })
  pve <- na.omit(pve)
  pve <- pve[order(rownames(pve)),]
  
  # Aggregate based on the `by` argument
  df_pve <- aggregate(pve$pve, by=list(pve[[by]]), FUN=sum)
  colnames(df_pve) <- c(by, "total_pve")
  df_pve$total_pve <- round(df_pve$total_pve, 4)
  
  # Add context/type-specific columns
  other_var <- setdiff(c("type", "context"), by)
  for(j in unique(pve[[other_var]])){
    df_pve[,j] <- pve[pve[[other_var]] == j,]$pve
  }
  
  # Handle SNP row
  df_pve["SNP", ] <- 0
  df_pve["SNP", "total_pve"] <- SNP_pve
  df_pve["SNP", by] <- "SNP"
  
  row.names(df_pve) <- NULL
  return(df_pve)
}

Genetic Architecture

sum_pve_tissue_alltraits <- list()
sum_pve_modality_alltraits <- list()
for (i in 1:length(traits)) {
  
  trait <- traits[i]
  gwas_n <- samplesize[trait]
  
  ctwas_res <-readRDS(paste0("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/results/",trait,"/",trait,".ctwas.res.RDS"))
  param <- ctwas_res$param
  ctwas_parameters <- summarize_param(param, gwas_n)

  sum_pve_tissue <- sum_pve_across(ctwas_parameters, by="context")
  sum_pve_tissue_total <- sum_pve_tissue$total_pve
  names(sum_pve_tissue_total) <- sum_pve_tissue$context
  sum_pve_tissue_alltraits[[i]] <- sum_pve_tissue_total
  
  sum_pve_modality <- sum_pve_across(ctwas_parameters, by="type")
  sum_pve_modality_total <- sum_pve_modality$total_pve
  names(sum_pve_modality_total) <- sum_pve_modality$type
  sum_pve_modality_alltraits[[i]] <- sum_pve_modality_total
  
}
names(sum_pve_tissue_alltraits) <- traits
names(sum_pve_modality_alltraits) <- traits

Bubble plot: show %h2g explained by molecular QTLs of each tissue on each trait. Use union of five tissues across all traits.

Message: cTWAS is able to find the right tissues.

sum_pve_tissue_percentages <- lapply(sum_pve_tissue_alltraits, function(x) x / sum(x) * 100)
cluster_names <- names(sum_pve_tissue_percentages)

filtered_list <- lapply(sum_pve_tissue_percentages, function(x) x[!names(x) %in% c("SNP")])

# Calculate the sum of each vector in the filtered list
sum_values <- sapply(filtered_list, sum)
max_values <- sapply(filtered_list, max)
# Calculate the names of maximum values for each vector in the filtered list
max_names <- sapply(filtered_list, function(x) names(x)[which.max(x)])
df <- bind_rows(
  lapply(names(filtered_list), function(x) {
    data.frame(Trait = x, Tissue = names(filtered_list[[x]]), Expression = filtered_list[[x]], stringsAsFactors = FALSE)
  }),
  .id = "id"
) %>%
  dplyr::select(-id) %>%
  spread(Trait, Expression)


df_long <- reshape2::melt(df, id.vars = "Tissue", variable.name = "Trait", value.name = "Expression")

ggplot(df_long, aes(x = Trait, y = Tissue, size = Expression)) +
  geom_point(alpha = 0.5, color = "blue") +  # Using a fixed color for all bubbles
  scale_size(range = c(1, 20), name = "%h2g") +  # Customizing the size legend title
  labs(x = "Trait", y = "Tissue") +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 16, angle = 45, hjust = 1),
        axis.text.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        axis.title.y = element_text(size = 16),
        legend.text = element_text(size = 16),  # Increase legend text size
        legend.title = element_text(size = 18))

Version Author Date
2ec4242 XSun 2024-10-17

Contributions of top tissue vs. all tissues together.

Bar plot of %h2g: two bars, top tissue vs. all tissues together, per trait. Use the top tissue from joint analysis.

Message: genetics of complex traits involve multiple tissues.

# Create the data frame including names of maximum values
data <- data.frame(
  cluster = rep(cluster_names, times = 3),
  value = c(max_values, sum_values, max_values),
  type = c(rep("toptissue", times = length(cluster_names)), 
           rep("sum_alltissues", times = length(cluster_names)), 
           rep("max_name", times = length(cluster_names))),
  label = c(rep("", times = length(cluster_names) * 2), max_names)
)

ggplot(data[data$type != "max_name", ], aes(x = cluster, y = value, fill = type)) +
  geom_bar(stat = "identity", position = position_dodge(), width = 0.7) +
  geom_text(data = data[data$type == "max_name", ], aes(label = label, y = value + 2), 
            position = position_dodge(width = 0.7), vjust = -0.5, hjust=-0.01) +
  labs(x = "Traits", y = "%h2g", fill = "Group") +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 16, angle = 45, hjust = 1),
        axis.text.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        axis.title.y = element_text(size = 16),
        legend.text = element_text(size = 16),  # Increase legend text size
        legend.title = element_text(size = 18))

Version Author Date
2ec4242 XSun 2024-10-17

Contribution of different modalities: Bar plot of %h2g: eQTL, sQTL, stQTL, per trait.

Message: genetics involves multiple modalities

sum_pve_modality_percentages <- lapply(sum_pve_modality_alltraits, function(x) x / sum(x) * 100)
cluster_names <- names(sum_pve_modality_percentages)
filtered_list <- lapply(sum_pve_modality_percentages, function(x) x[!names(x) %in% c("SNP")])

# Calculate the sum of each vector in the filtered list
sum_values <- sapply(filtered_list, sum)
max_values <- sapply(filtered_list, max)
# Calculate the names of maximum values for each vector in the filtered list
max_names <- sapply(filtered_list, function(x) names(x)[which.max(x)])

df <- do.call(rbind, lapply(filtered_list, function(x) data.frame(Group = names(x), h2g = x)))
df$Trait <- rep(names(filtered_list), each = length(filtered_list[[1]]))

# Reshape data frame if necessary
df <- reshape2::melt(df, id.vars = c("Trait", "Group"))

ggplot(df, aes(x = Trait, y = value, fill = Group)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(x = "Trait", y = "%h2g") +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 16, angle = 45, hjust = 1),
        axis.text.y = element_text(size = 16),
        axis.title.x = element_text(size = 16),
        axis.title.y = element_text(size = 16),
        legend.text = element_text(size = 16),  # Increase legend text size
        legend.title = element_text(size = 18))

Version Author Date
2ec4242 XSun 2024-10-17

Gene Discovery

Number of high PIP genes: all tissues vs. best tissue eQTL (from single tissue analysis).

Bar plot.

Message: increased power from multi-omics multi-tissue analysis.

load("/project/xinhe/xsun/multi_group_ctwas/11.multi_group_1008/postprocess/compare_multi_single_genenum.rdata")

sum$num_multi <- as.numeric(sum$num_multi)
sum$num_single <- as.numeric(sum$num_single)
sum$overlap <- as.numeric(sum$overlap)
sum$overlap_adj <- as.numeric(sum$overlap) * 1.00   # Adjust the value to slightly offset behind the main bars

data_long <- pivot_longer(sum, cols = c(num_single, num_multi), names_to = "category", values_to = "count")

# Facet by trait, with tissues as the bars
# ggplot(data_long, aes(x = tissue_single, y = count, fill = category)) +
#   geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.8) +
#   geom_bar(data = sum, aes(x = tissue_single, y = overlap_adj), stat = "identity", position = position_dodge(width = 0.8), fill = "grey", alpha = 0.7, width = 0.8) +
#   facet_wrap(~ trait, nrow = 1, scales = "free_x") +  # Display all facets in one row with free scales on x
#   labs(x = "Tissue", y = "Number of Genes with PIP>0.8") +
#   scale_fill_manual(values = c("num_single" = "skyblue", "num_multi" = "orange")) +
#   theme_minimal() +
#   theme(axis.text.x = element_text(size = 12, angle = 45, vjust = 0.7, hjust = 0.6),  # Adjusted hjust here
#         axis.text.y = element_text(size = 12),
#         axis.title.x = element_text(size = 14),
#         axis.title.y = element_text(size = 14),
#         strip.background = element_blank(),
#         strip.text.x = element_text(size = 12, face = "bold"))

# ggplot(data_long, aes(x = trait, y = count, fill = category)) +
#   geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.8) +
#   geom_bar(data = data_long, aes(x = trait, y = overlap_adj), stat = "identity", position = position_dodge(width = 0.8), fill = "grey", alpha = 0.7, width = 0.8) +
#   facet_wrap(~ tissue_single, nrow = 1, scales = "free_x") +  # Swap to display facets by tissue_single
#   labs(x = "Trait", y = "Number of Genes with PIP>0.8") +
#   scale_fill_manual(values = c("num_single" = "skyblue", "num_multi" = "orange")) +
#   theme_minimal() +
#   theme(axis.text.x = element_text(size = 12, angle = 45, vjust = 0.7, hjust = 0.6),  # Adjusted hjust here
#         axis.text.y = element_text(size = 12),
#         axis.title.x = element_text(size = 14),
#         axis.title.y = element_text(size = 14),
#         strip.background = element_blank(),
#         strip.text.x = element_text(size = 12, face = "bold"))

ggplot(data_long, aes(x = trait, y = count, fill = category)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8), width = 0.8) +
  geom_bar(aes(x = trait, y = overlap_adj), stat = "identity", position = position_dodge(width = 0.8), fill = "grey", alpha = 0.7, width = 0.8) +
  # Add tissue labels only once above the blue bars (num_single)
  geom_text(data = subset(data_long, category == "num_single"), 
            aes(label = tissue_single), 
            position = position_dodge(width = 0.8), 
            vjust = -0.5, 
            hjust = 0.3,
            color = "black", 
            size = 4) +
  labs(x = "Trait", y = "Number of Genes with PIP>0.8") +
  scale_fill_manual(values = c("num_single" = "skyblue", "num_multi" = "orange")) +
  theme_minimal() +
  theme(axis.text.x = element_text(size = 12, angle = 45, vjust = 0.7, hjust = 0.6),  
        axis.text.y = element_text(size = 12),
        axis.title.x = element_text(size = 14),
        axis.title.y = element_text(size = 14),
        strip.background = element_blank(),
        strip.text.x = element_text(size = 12, face = "bold"))


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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] tidyr_1.3.0   dplyr_1.1.4   ctwas_0.4.15  ggplot2_3.5.1

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] GenomicRanges_1.48.0        fs_1.5.2                   
  [9] rstudioapi_0.13             farver_2.1.0               
 [11] ggrepel_0.9.1               bit64_4.0.5                
 [13] AnnotationDbi_1.58.0        fansi_1.0.3                
 [15] xml2_1.3.3                  codetools_0.2-18           
 [17] logging_0.10-108            cachem_1.0.6               
 [19] knitr_1.39                  jsonlite_1.8.0             
 [21] workflowr_1.7.0             Rsamtools_2.12.0           
 [23] dbplyr_2.1.1                png_0.1-7                  
 [25] readr_2.1.2                 compiler_4.2.0             
 [27] httr_1.4.3                  assertthat_0.2.1           
 [29] Matrix_1.5-3                fastmap_1.1.0              
 [31] lazyeval_0.2.2              cli_3.6.1                  
 [33] later_1.3.0                 htmltools_0.5.2            
 [35] prettyunits_1.1.1           tools_4.2.0                
 [37] gtable_0.3.0                glue_1.6.2                 
 [39] GenomeInfoDbData_1.2.8      reshape2_1.4.4             
 [41] rappdirs_0.3.3              Rcpp_1.0.12                
 [43] Biobase_2.56.0              jquerylib_0.1.4            
 [45] vctrs_0.6.5                 Biostrings_2.64.0          
 [47] rtracklayer_1.56.0          xfun_0.41                  
 [49] stringr_1.5.1               lifecycle_1.0.4            
 [51] irlba_2.3.5                 restfulr_0.0.14            
 [53] ensembldb_2.20.2            XML_3.99-0.14              
 [55] zlibbioc_1.42.0             zoo_1.8-10                 
 [57] scales_1.3.0                gggrid_0.2-0               
 [59] hms_1.1.1                   promises_1.2.0.1           
 [61] MatrixGenerics_1.8.0        ProtGenerics_1.28.0        
 [63] parallel_4.2.0              SummarizedExperiment_1.26.1
 [65] AnnotationFilter_1.20.0     LDlinkR_1.2.3              
 [67] yaml_2.3.5                  curl_4.3.2                 
 [69] memoise_2.0.1               sass_0.4.1                 
 [71] biomaRt_2.54.1              stringi_1.7.6              
 [73] RSQLite_2.3.1               highr_0.9                  
 [75] S4Vectors_0.34.0            BiocIO_1.6.0               
 [77] GenomicFeatures_1.48.3      BiocGenerics_0.42.0        
 [79] filelock_1.0.2              BiocParallel_1.30.3        
 [81] GenomeInfoDb_1.39.9         rlang_1.1.2                
 [83] pkgconfig_2.0.3             matrixStats_0.62.0         
 [85] bitops_1.0-7                evaluate_0.15              
 [87] lattice_0.20-45             purrr_1.0.2                
 [89] labeling_0.4.2              GenomicAlignments_1.32.0   
 [91] htmlwidgets_1.5.4           cowplot_1.1.1              
 [93] bit_4.0.4                   tidyselect_1.2.0           
 [95] plyr_1.8.7                  magrittr_2.0.3             
 [97] R6_2.5.1                    IRanges_2.30.0             
 [99] generics_0.1.2              DelayedArray_0.22.0        
[101] DBI_1.2.2                   pgenlibr_0.3.3             
[103] pillar_1.9.0                whisker_0.4                
[105] withr_2.5.0                 KEGGREST_1.36.3            
[107] RCurl_1.98-1.7              mixsqp_0.3-43              
[109] tibble_3.2.1                crayon_1.5.1               
[111] utf8_1.2.2                  BiocFileCache_2.4.0        
[113] plotly_4.10.0               tzdb_0.4.0                 
[115] rmarkdown_2.25              progress_1.2.2             
[117] grid_4.2.0                  data.table_1.14.2          
[119] blob_1.2.3                  git2r_0.30.1               
[121] digest_0.6.29               httpuv_1.6.5               
[123] stats4_4.2.0                munsell_0.5.0              
[125] viridisLite_0.4.0           bslib_0.3.1