Multi-tissue analysis, merging the LD blocks

workflowr

• Summary
• Checks
• Past versions

Last updated: 2024-01-05

Checks: 6 1

Knit directory: multigroup_ctwas_analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

---

R Markdown file: uncommitted changes

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20231112)

The command set.seed(20231112) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 2add2b1

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 2add2b1. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Unstaged changes:
    Deleted:    analysis/multi_tissue.Rmd
    Modified:   analysis/multi_tissue_ldmerge.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/multi_tissue_ldmerge.Rmd) and HTML (docs/multi_tissue_ldmerge.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	97049b2	XSun	2023-12-17	update
html	97049b2	XSun	2023-12-17	update
Rmd	18adde1	Xiaotong Sun	2023-12-08	update
html	18adde1	Xiaotong Sun	2023-12-08	update
Rmd	c547601	XSun	2023-12-05	updadate
html	c547601	XSun	2023-12-05	updadate
Rmd	7327c5c	XSun	2023-12-04	update
html	7327c5c	XSun	2023-12-04	update
Rmd	90d301d	XSun	2023-12-03	update

---

Overview

Traits

aFib, IBD, LDL, SBP, SCZ, WBC

Tissues

We used the top 5 tissues decided here.

Settings

We merged the LD blocks if there are genes expanding the blocks.

Functions used

suppressMessages(library(ctwas))
suppressMessages(library(data.table))
suppressMessages(library(RSQLite))
suppressMessages(library(DT))
suppressMessages(library(tools))
suppressMessages(library(dplyr))
suppressMessages(library(tidyr))

thin <- 0.1

process_data <- function(weight, outputdir, outname, z_snp, z_gene, display_datatable = TRUE) {
  # Splitting and constructing weight file paths
  weight <- unlist(strsplit(weight, split=","))
  weight <- paste0("/project2/xinhe/shared_data/multigroup_ctwas/weights/predictdb_nolnc/mashr_", weight, "_nolnc.db")

  # Reading cTWAS results
  ctwas_res <- fread(paste0(outputdir, outname, ".susieIrss.txt"))

  # Initialize gene_info dataframe
  gene_info <- data.frame(gene=character(), genename=character(), gene_type=character(), weight=character())
  for (i in seq_along(weight)) {
    sqlite <- dbDriver("SQLite")
    db <- dbConnect(sqlite, weight[i])
    query <- function(...) dbGetQuery(db, ...)
    gene_info_current <- query("SELECT gene, genename, gene_type FROM extra")
    dbDisconnect(db)

    gene_info_current$weight <- weight[i]
    gene_info <- rbind(gene_info, gene_info_current)
  }

  # Processing gene_info
  gene_info$weight <- sapply(gene_info$weight, function(x) rev(unlist(strsplit(file_path_sans_ext(x), "/")))[1])
  gene_info$id <- paste(gene_info$gene, gene_info$weight, sep="|")

  # Adding gene names to cTWAS results
  ctwas_res$genename[ctwas_res$type != "SNP"] <- gene_info$genename[match(ctwas_res$id[ctwas_res$type != "SNP"], gene_info$id)]

  # Adding z values
  ctwas_res$z[ctwas_res$type == "SNP"] <- z_snp$z[match(ctwas_res$id[ctwas_res$type == "SNP"], z_snp$id)]
  ctwas_res$z[ctwas_res$type != "SNP"] <- z_gene$z[match(ctwas_res$id[ctwas_res$type != "SNP"], z_gene$id)]

  # Sorting and displaying datatable if required
  ctwas_res <- ctwas_res[order(-ctwas_res$susie_pip),]
  if (display_datatable) {
    datatable(ctwas_res[ctwas_res$type != "SNP" & ctwas_res$susie_pip > 0.8,])
  }
  
  return(ctwas_res)
}

process_gene_data <- function(ctwas_res, weight = NULL) {
  # Aggregate susie_pip
  df_gene <- aggregate(ctwas_res$susie_pip[ctwas_res$type != "SNP"], 
                       by = list(ctwas_res$genename[ctwas_res$type != "SNP"]), 
                       FUN = sum)
  colnames(df_gene) <- c("genename", "combined_pip")

  # Optional part for dropping duplicated gene names
  # Uncomment this part if needed
  # df_gene <- df_gene[!(df_gene$genename %in% names(which(table(ctwas_res$genename) > length(weight)))),]

  # Collect tissue-level results
  all_tissue_names <- unique(ctwas_res$type[ctwas_res$type != "SNP"])

  df_gene_pips <- matrix(NA, nrow = nrow(df_gene), ncol = length(all_tissue_names))
  colnames(df_gene_pips) <- all_tissue_names

  ctwas_gene_res <- ctwas_res[ctwas_res$type != "SNP",]

  for (i in 1:nrow(df_gene)) {
    gene <- df_gene$genename[i]
    ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename == gene,]
    df_gene_pips[i, ctwas_gene_res_subset$type] <- ctwas_gene_res_subset$susie_pip
  }

  df_gene <- cbind(df_gene, df_gene_pips)

  # Sort by combined PIP
  df_gene <- df_gene[order(-df_gene$combined_pip),]
  df_gene <- df_gene[, apply(df_gene, 2, function(x) {!all(is.na(x))})]  # Drop genes not imputed in any tissue

  # Determine number of rows to display
  n_rows <- max(sum(df_gene$combined_pip > 0.8), 20)

  # Select top rows based on the criteria
  df_to_display <- head(df_gene, n_rows)
  
  # Return a datatable
 # return(DT::datatable(df_to_display,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with combined pip > 0.8, 0 means pip=0; NA means no such gene in this tissue'),options = list(pageLength = 10) ))
    datatable_display <- DT::datatable(df_to_display, 
                                     caption = htmltools::tags$caption(style = 'caption-side: left; text-align: left; color:black; font-size:150%;', 
                                     'Genes with combined pip > 0.8, 0 means pip=0; NA means no such gene in this tissue'), 
                                     options = list(pageLength = 10))
  return(df_to_display)
}

process_ctwas_results <- function(ctwas_parameters, ctwas_res) {
    # Process parameters
    para <- cbind(ctwas_parameters$group_size,
                  ctwas_parameters$group_prior,
                  ctwas_parameters$group_prior_var,
                  c(NA, ctwas_parameters$enrichment),
                  ctwas_parameters$group_pve)
    para <- as.data.frame(cbind(rownames(para), para))
    colnames(para) <- c("group","group_size","group_prior","group_prior_var","enrichment","group_pve")

    # Filter ctwas results for genes and high PIP
    ctwas_res_gene <- ctwas_res[ctwas_res$type != "SNP",]
    ctwas_res_gene_pip08 <- ctwas_res_gene[ctwas_res_gene$susie_pip > 0.8,]

    # Create summary table
    summary <- as.data.frame(table(ctwas_res_gene_pip08$type))
    colnames(summary) <- c("weights", "#of genes with susiepip >0.8")

    # Merge summary with parameters
    summary <- merge(summary, para, by.x = "weights", by.y = "group", all.y = TRUE)

    return(summary)
}

attributable_pip <- function(df, combined_pip_col) {
  # Identify the relevant columns (those ending with '_nolnc')
  relevant_cols <- names(df)[grepl("_nolnc$", names(df))]

  # Create and rearrange columns dynamically
  df <- df %>%
    mutate(across(all_of(relevant_cols), 
                  ~ .x / df[[combined_pip_col]] *100, 
                  .names = "{.col}_Attributable_PIP (%)")) %>%
    {
      # Building the new column order
      new_order <- unlist(lapply(relevant_cols, function(col) {
        c(col, paste0(col, "_Attributable_PIP (%)"))
      }))
      select(., all_of(c("genename", combined_pip_col, new_order)))
    }

  df
}


plot_stacked_histogram <- function(df, genename_col, combined_pip_col, attributable_cols) {
  
  df[[genename_col]] <- factor(df[[genename_col]], levels = df[[genename_col]])
  
  long_df_attributable <- df %>%
    select(all_of(c(genename_col, combined_pip_col, attributable_cols))) %>%
    pivot_longer(
      cols = all_of(attributable_cols),
      names_to = "Tissue",
      values_to = "Proportion"
    ) %>%
    mutate(Tissue = gsub("_Attributable_PIP \\(%\\)", "", Tissue))  # Modify Tissue names
  
  
  # Calculate the absolute values for each segment
  long_df_attributable <- long_df_attributable %>%
    mutate(Absolute_Value = Proportion / 100 * long_df_attributable[[combined_pip_col]])
  
  # Create the plot
  p <- ggplot(long_df_attributable, aes_string(x = genename_col, y = "Absolute_Value", fill = "Tissue")) +
    geom_bar(stat = "identity") +
    theme_minimal() +
    labs(title = "Stacked Histogram of Combined PIP with Tissue Contributions",
         x = "Gene Name",
         y = "Combined PIP",
         fill = "Tissue") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))
  
  return(p)
}

aFib

outputdir <- "/project/xinhe/xsun/ctwas/3.multi_tissue/results/aFib-ebi-a-GCST006414/"
outname <- "aFib-ebi-a-GCST006414_ctwas"
gwas_n <- 1030836
weight <- "Heart_Atrial_Appendage,Esophagus_Muscularis,Brain_Cerebellum,Muscle_Skeletal,Thyroid"

load(paste0(outputdir,outname,"_z_snp.Rd"))
load(paste0(outputdir,outname,"_z_gene.Rd"))

ctwas_parameters <- ctwas:::ctwas_summarize_parameters(outputdir = outputdir,
                                               outname = outname,
                                               gwas_n = gwas_n,
                                               thin = thin)

ctwas_parameters$convergence_plot

Past versions of unnamed-chunk-2-1.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08
7327c5c	XSun	2023-12-04

ctwas_res <- process_data(weight = weight,outputdir = outputdir,outname = outname,z_snp = z_snp,z_gene = z_gene)
para <- process_ctwas_results(ctwas_parameters = ctwas_parameters,ctwas_res = ctwas_res)
DT::datatable(para,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Parameters'))

df_gene <- process_gene_data(ctwas_res = ctwas_res)

df_gene <- attributable_pip(df_gene, "combined_pip")

DT::datatable(df_gene,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with combined pip > 0.8, 0 means pip=0; NA means no such gene in this tissue'),options = list(pageLength = 10) )

attributable_columns <- colnames(df_gene)[seq(4,12,by=2)]

plot_stacked_histogram(df_gene, "genename", "combined_pip", attributable_columns)

Past versions of unnamed-chunk-2-4.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

table_show <- ctwas_res[ctwas_res$genename %in% df_gene$genename,]
table_show <- rbind(table_show,ctwas_res[ctwas_res$type!="SNP" & ctwas_res$susie_pip > 0.8,])
table_show <- table_show[!duplicated(table_show$id),]
DT::datatable(table_show,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Detailed table for genes with susiepip > 0.8 & combined pip >0.8'),options = list(pageLength = 5))

IBD

outputdir <- "/project/xinhe/xsun/ctwas/3.multi_tissue/results/IBD-ebi-a-GCST004131/"
outname <- "IBD-ebi-a-GCST004131_ctwas"
gwas_n <- 59957
weight <- "Whole_Blood,Cells_Cultured_fibroblasts,Adipose_Subcutaneous,Colon_Transverse,Esophagus_Mucosa"

load(paste0(outputdir,outname,"_z_snp.Rd"))
load(paste0(outputdir,outname,"_z_gene.Rd"))

ctwas_parameters <- ctwas:::ctwas_summarize_parameters(outputdir = outputdir,
                                               outname = outname,
                                               gwas_n = gwas_n,
                                               thin = thin)


ctwas_parameters$convergence_plot

Past versions of unnamed-chunk-3-1.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08
7327c5c	XSun	2023-12-04

ctwas_res <- process_data(weight = weight,outputdir = outputdir,outname = outname,z_snp = z_snp,z_gene = z_gene)
para <- process_ctwas_results(ctwas_parameters = ctwas_parameters,ctwas_res = ctwas_res)
DT::datatable(para,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Parameters'))

df_gene <- process_gene_data(ctwas_res = ctwas_res)

df_gene <- attributable_pip(df_gene, "combined_pip")

DT::datatable(df_gene,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with combined pip > 0.8, 0 means pip=0; NA means no such gene in this tissue'),options = list(pageLength = 10) )

attributable_columns <- colnames(df_gene)[seq(4,12,by=2)]

plot_stacked_histogram(df_gene, "genename", "combined_pip", attributable_columns)

Past versions of unnamed-chunk-3-4.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

table_show <- ctwas_res[ctwas_res$genename %in% df_gene$genename,]
table_show <- rbind(table_show,ctwas_res[ctwas_res$type!="SNP" & ctwas_res$susie_pip > 0.8,])
table_show <- table_show[!duplicated(table_show$id),]
DT::datatable(table_show,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Detailed table for genes with susiepip > 0.8 & combined pip >0.8'),options = list(pageLength = 5))

LDL

outputdir <- "/project/xinhe/xsun/ctwas/3.multi_tissue/results/LDL-ukb-d-30780_irnt/"
outname <- "LDL-ukb-d-30780_irnt_ctwas"
gwas_n <- 343621
weight <- "Liver,Lung,Spleen,Colon_Transverse,Skin_Not_Sun_Exposed_Suprapubic"

load(paste0(outputdir,outname,"_z_snp.Rd"))
load(paste0(outputdir,outname,"_z_gene.Rd"))

ctwas_parameters <- ctwas:::ctwas_summarize_parameters(outputdir = outputdir,
                                               outname = outname,
                                               gwas_n = gwas_n,
                                               thin = thin)

ctwas_parameters$convergence_plot

Past versions of unnamed-chunk-4-1.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08
7327c5c	XSun	2023-12-04

ctwas_res <- process_data(weight = weight,outputdir = outputdir,outname = outname,z_snp = z_snp,z_gene = z_gene)
para <- process_ctwas_results(ctwas_parameters = ctwas_parameters,ctwas_res = ctwas_res)
DT::datatable(para,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Parameters'))

df_gene <- process_gene_data(ctwas_res = ctwas_res)

df_gene <- attributable_pip(df_gene, "combined_pip")

DT::datatable(df_gene,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with combined pip > 0.8, 0 means pip=0; NA means no such gene in this tissue'),options = list(pageLength = 10) )

attributable_columns <- colnames(df_gene)[seq(4,12,by=2)]

plot_stacked_histogram(df_gene, "genename", "combined_pip", attributable_columns)

Past versions of unnamed-chunk-4-4.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

table_show <- ctwas_res[ctwas_res$genename %in% df_gene$genename,]
table_show <- rbind(table_show,ctwas_res[ctwas_res$type!="SNP" & ctwas_res$susie_pip > 0.8,])
table_show <- table_show[!duplicated(table_show$id),]
DT::datatable(table_show,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Detailed table for genes with susiepip > 0.8 & combined pip >0.8'),options = list(pageLength = 5))

SBP

outputdir <- "/project/xinhe/xsun/ctwas/3.multi_tissue/results/SBP-ukb-a-360/"
outname <- "SBP-ukb-a-360_ctwas"
gwas_n <- 317754
weight <- "Artery_Tibial,Spleen,Skin_Sun_Exposed_Lower_leg,Adipose_Subcutaneous,Brain_Cortex"

load(paste0(outputdir,outname,"_z_snp.Rd"))
load(paste0(outputdir,outname,"_z_gene.Rd"))

ctwas_parameters <- ctwas:::ctwas_summarize_parameters(outputdir = outputdir,
                                               outname = outname,
                                               gwas_n = gwas_n,
                                               thin = thin)


ctwas_parameters$convergence_plot

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

ctwas_res <- process_data(weight = weight,outputdir = outputdir,outname = outname,z_snp = z_snp,z_gene = z_gene)
para <- process_ctwas_results(ctwas_parameters = ctwas_parameters,ctwas_res = ctwas_res)
DT::datatable(para,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Parameters'))

df_gene <- process_gene_data(ctwas_res = ctwas_res)

df_gene <- attributable_pip(df_gene, "combined_pip")

DT::datatable(df_gene,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with combined pip > 0.8, 0 means pip=0; NA means no such gene in this tissue'),options = list(pageLength = 10) )

attributable_columns <- colnames(df_gene)[seq(4,12,by=2)]

plot_stacked_histogram(df_gene, "genename", "combined_pip", attributable_columns)

Past versions of unnamed-chunk-5-4.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

table_show <- ctwas_res[ctwas_res$genename %in% df_gene$genename,]
table_show <- rbind(table_show,ctwas_res[ctwas_res$type!="SNP" & ctwas_res$susie_pip > 0.8,])
table_show <- table_show[!duplicated(table_show$id),]
DT::datatable(table_show,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Detailed table for genes with susiepip > 0.8 & combined pip >0.8'),options = list(pageLength = 5))

SCZ

outputdir <- "/project/xinhe/xsun/ctwas/3.multi_tissue/results/SCZ-ieu-b-5102/"
outname <- "SCZ-ieu-b-5102_ctwas"
gwas_n <- 127906
weight <- "Adrenal_Gland,Brain_Cerebellar_Hemisphere,Brain_Anterior_cingulate_cortex_BA24,Lung,Heart_Left_Ventricle"

load(paste0(outputdir,outname,"_z_snp.Rd"))
load(paste0(outputdir,outname,"_z_gene.Rd"))

ctwas_parameters <- ctwas:::ctwas_summarize_parameters(outputdir = outputdir,
                                               outname = outname,
                                               gwas_n = gwas_n,
                                               thin = thin)

ctwas_parameters$convergence_plot

Past versions of unnamed-chunk-6-1.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

ctwas_res <- process_data(weight = weight,outputdir = outputdir,outname = outname,z_snp = z_snp,z_gene = z_gene)
para <- process_ctwas_results(ctwas_parameters = ctwas_parameters,ctwas_res = ctwas_res)
DT::datatable(para,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Parameters'))

df_gene <- process_gene_data(ctwas_res = ctwas_res)

df_gene <- attributable_pip(df_gene, "combined_pip")

DT::datatable(df_gene,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with combined pip > 0.8, 0 means pip=0; NA means no such gene in this tissue'),options = list(pageLength = 10) )

attributable_columns <- colnames(df_gene)[seq(4,12,by=2)]

plot_stacked_histogram(df_gene, "genename", "combined_pip", attributable_columns)

Past versions of unnamed-chunk-6-4.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

table_show <- ctwas_res[ctwas_res$genename %in% df_gene$genename,]
table_show <- rbind(table_show,ctwas_res[ctwas_res$type!="SNP" & ctwas_res$susie_pip > 0.8,])
table_show <- table_show[!duplicated(table_show$id),]
DT::datatable(table_show,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Detailed table for genes with susiepip > 0.8 & combined pip >0.8'),options = list(pageLength = 5))

WBC

outputdir <- "/project/xinhe/xsun/ctwas/3.multi_tissue/results/WBC-ieu-b-30/"
outname <- "WBC-ieu-b-30_ctwas"
gwas_n <- 563946
weight <- "Whole_Blood,Adipose_Subcutaneous,Esophagus_Muscularis,Colon_Transverse,Spleen"

load(paste0(outputdir,outname,"_z_snp.Rd"))
load(paste0(outputdir,outname,"_z_gene.Rd"))

ctwas_parameters <- ctwas:::ctwas_summarize_parameters(outputdir = outputdir,
                                               outname = outname,
                                               gwas_n = gwas_n,
                                               thin = thin)


ctwas_parameters$convergence_plot

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

ctwas_res <- process_data(weight = weight,outputdir = outputdir,outname = outname,z_snp = z_snp,z_gene = z_gene)
para <- process_ctwas_results(ctwas_parameters = ctwas_parameters,ctwas_res = ctwas_res)
DT::datatable(para,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Parameters'))

df_gene <- process_gene_data(ctwas_res = ctwas_res)

df_gene <- attributable_pip(df_gene, "combined_pip")

DT::datatable(df_gene,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Genes with combined pip > 0.8, 0 means pip=0; NA means no such gene in this tissue'),options = list(pageLength = 10) )

attributable_columns <- colnames(df_gene)[seq(4,12,by=2)]

plot_stacked_histogram(df_gene, "genename", "combined_pip", attributable_columns)

Past versions of unnamed-chunk-7-4.png

Version	Author	Date
18adde1	Xiaotong Sun	2023-12-08

table_show <- ctwas_res[ctwas_res$genename %in% df_gene$genename,]
table_show <- rbind(table_show,ctwas_res[ctwas_res$type!="SNP" & ctwas_res$susie_pip > 0.8,])
table_show <- table_show[!duplicated(table_show$id),]
DT::datatable(table_show,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Detailed table for genes with susiepip > 0.8 & combined pip >0.8'),options = list(pageLength = 5))

Literature supports

dat <- readxl::read_excel("/project/xinhe/xsun/ctwas/3.multi_tissue/data/trait-celltype_pair2.xlsx")

New names:
* `` -> `...1`

DT::datatable(dat,caption = htmltools::tags$caption( style = 'caption-side: left; text-align: left; color:black;  font-size:150% ;','Literature supports'),options = list(pageLength = 11))

Session information

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] tools     stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] cowplot_1.1.1     ggplot2_3.3.5     tidyr_1.3.0       dplyr_1.1.4      
[5] DT_0.22           RSQLite_2.3.1     data.table_1.14.2 ctwas_0.1.40     

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3      lattice_0.20-45   rprojroot_2.0.3   digest_0.6.29    
 [5] foreach_1.5.2     utf8_1.2.2        cellranger_1.1.0  R6_2.5.1         
 [9] evaluate_0.15     highr_0.9         pillar_1.9.0      rlang_1.1.2      
[13] readxl_1.4.0      rstudioapi_0.13   whisker_0.4       jquerylib_0.1.4  
[17] blob_1.2.3        Matrix_1.5-3      rmarkdown_2.14    labeling_0.4.2   
[21] stringr_1.5.1     htmlwidgets_1.5.4 bit_4.0.4         munsell_0.5.0    
[25] compiler_4.2.0    httpuv_1.6.5      xfun_0.30         pkgconfig_2.0.3  
[29] htmltools_0.5.2   tidyselect_1.2.0  tibble_3.2.1      workflowr_1.7.0  
[33] logging_0.10-108  codetools_0.2-18  fansi_1.0.3       withr_2.5.0      
[37] later_1.3.0       grid_4.2.0        jsonlite_1.8.0    gtable_0.3.0     
[41] lifecycle_1.0.4   DBI_1.1.2         git2r_0.30.1      magrittr_2.0.3   
[45] scales_1.2.0      cli_3.6.1         stringi_1.7.6     cachem_1.0.6     
[49] farver_2.1.0      fs_1.5.2          promises_1.2.0.1  pgenlibr_0.3.3   
[53] bslib_0.3.1       generics_0.1.2    vctrs_0.6.5       iterators_1.0.14 
[57] bit64_4.0.5       glue_1.6.2        purrr_1.0.2       crosstalk_1.2.0  
[61] fastmap_1.1.0     yaml_2.3.5        colorspace_2.0-3  memoise_2.0.1    
[65] knitr_1.39        sass_0.4.1