Last updated: 2024-01-25

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Knit directory: multigroup_ctwas_analysis/

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Overview

The model is \(y=X*w\)

y is an n-dimensional vector representing gene-trait associations (n = number of genes), which can be:

  • z-scores computed by MAGMA
  • a binary vector indicating gene-trait relationships (genes with FDR < 0.05 as per MAGMA are marked 1).

If y is a z-score vector, it can be fitted using linear Susie

If y is a binarized vector, the model can be fitted using logistic Susie

Functions

library(gseasusie)
library(tidyverse)
library(GO.db)


color_sign = function(value) {
  if (value > 0) {
    color <- "#008000"
  } else if (value < 0) {
    color <- "#e00000"
  } else {
    color <- "#777"
  }
  list(color = color)
}


getGOTermName <- function(go_id) {
  result <- try(Term(GOTERM[[go_id]]), silent = TRUE)
  if (class(result) == "try-error") {
    return(NA)
  } else {
    return(result)
  }
}

getKEGGPathwayName <- function(kegg_id) {
  result <- try(KEGGREST::keggGet(kegg_id)[[1]]$NAME, silent = TRUE)
  if (class(result) == "try-error") {
    return(NA)
  } else {
    return(result)
  }
}

interactive_table_linear_susie <- function(fit, ora) {
  
  fit <- fit
  ora <- ora
  
  res <- gseasusie:::get_gene_set_summary(fit) %>%
    dplyr::left_join(ora)
  
  coef <- susieR::coef.susie(fit)
  coef <- as.data.frame(cbind(names(coef), coef))
  colnames(coef) <- c("pwy", "beta")
  rownames(coef) <- seq(1,nrow(coef),by=1)
  
  res_updated <- res %>%
    # Ensure 'beta' in 'res' is of type double
    mutate(beta = as.numeric(beta)) %>%
    left_join(coef %>%
                # Ensure 'beta' in 'coef' is of type double
                mutate(beta = as.numeric(beta)), 
              by = c("geneSet" = "pwy")) %>%
    # Combine the two 'beta' columns, choosing 'coef' values first
    mutate(beta = coalesce(beta.y, beta.x)) %>%
    # Explicitly use `select` from `dplyr`
    dplyr::select(-beta.y, -beta.x)
  
  res <- res_updated
  
  csdat <- gseasusie:::get_credible_set_summary(fit) %>%
    dplyr::left_join(ora) %>%
    dplyr::filter(in_cs, active_cs) %>%
    dplyr::select(geneSet, component, in_cs) %>%
    distinct()
  
  dt <- res %>% 
    dplyr::filter(overlap > 0) %>%
    dplyr::mutate(
      logOddsRatio = log(oddsRatio),
      nlog10pFishersExact = -log10(pFishersExact)
    ) %>%
    dplyr::left_join(csdat) %>%
    dplyr::arrange(dplyr::desc(nlog10pFishersExact)) %>%
    dplyr::mutate(
      fisherRank = dplyr::row_number(),
      in_cs = dplyr::if_else(is.na(in_cs), FALSE, in_cs)) %>%
    dplyr::select(geneSet, beta, pip, overlap, geneSetSize, logOddsRatio, nlog10pFishersExact, in_cs, component, fisherRank) %>%
    dplyr::mutate(dplyr::across(!where(is.numeric) , as.factor))
  
  
  if(grepl("GO",dt$geneSet[1])) {
      dt <- dt %>%
      dplyr::filter(!is.na(component)) %>% #### only show CS
      dplyr::mutate(goTermName = sapply(as.character(geneSet), getGOTermName)) %>% # extract Go term names
      dplyr::select(geneSet, goTermName, everything()) # re-order
  }else if (grepl("hsa",dt$geneSet[1])) {
      dt <- dt %>%
      dplyr::filter(!is.na(component)) %>% #### only show CS
      dplyr::mutate(KEGGPathwayName = sapply(as.character(geneSet), getKEGGPathwayName)) %>% # extract Go term names
      dplyr::select(geneSet, KEGGPathwayName, everything()) # re-order
  }
  
  # dt %>%
  #   dplyr::select(-c(in_cs)) %>%
  #   reactable::reactable(
  #     filterable=TRUE,
  #     minRows=1,
  #     columns = list(
  #       pip = reactable::colDef(format = reactable::colFormat(digits = 3)),
  #       logOddsRatio = reactable::colDef(style= function(value){color_sign(value)},
  #                                        format = reactable::colFormat(digits = 3)),
  #       beta = reactable::colDef(style= function(value){color_sign(value)},
  #                                format = reactable::colFormat(digits = 3)),
  #       nlog10pFishersExact = reactable::colDef(format = reactable::colFormat(digits = 3))
  #     ),
  #     rowStyle = function(index){
  #       if(dt$in_cs[index] == TRUE){
  #         list(background = "#e5f5e0")
  #       }
  #     },
  #     defaultSorted = list(nlog10pFishersExact='desc')
  #   )
  # 
  DT::datatable(dt,options = list(pageLength=10))
  
}

interactive_table_logistic_susie <- function(fit, ora) {
  
  fit <- fit
  ora <- ora
  
  res <- gseasusie:::get_gene_set_summary(fit) %>%
    dplyr::left_join(ora)
  
  csdat <- gseasusie:::get_credible_set_summary(fit) %>%
    dplyr::left_join(ora) %>%
    dplyr::filter(in_cs, active_cs) %>%
    dplyr::select(geneSet, component, in_cs) %>%
    distinct()
  
  dt <- res %>% 
    dplyr::filter(overlap > 0) %>%
    dplyr::mutate(
      logOddsRatio = log(oddsRatio),
      nlog10pFishersExact = -log10(pFishersExact)
    ) %>%
    dplyr::left_join(csdat) %>%
    dplyr::arrange(dplyr::desc(nlog10pFishersExact)) %>%
    dplyr::mutate(
      fisherRank = dplyr::row_number(),
      in_cs = dplyr::if_else(is.na(in_cs), FALSE, in_cs)) %>%
    dplyr::select(geneSet, beta, pip, overlap, geneSetSize, logOddsRatio, nlog10pFishersExact, in_cs, component, fisherRank) %>%
    dplyr::mutate(dplyr::across(!where(is.numeric) , as.factor))
  
  
  if(grepl("GO",dt$geneSet[1])) {
      dt <- dt %>%
      dplyr::filter(!is.na(component)) %>% #### only show CS
      dplyr::mutate(goTermName = sapply(as.character(geneSet), getGOTermName)) %>% # extract Go term names
      dplyr::select(geneSet, goTermName, everything()) # re-order
  }else if (grepl("hsa",dt$geneSet[1])) {
      dt <- dt %>%
      dplyr::filter(!is.na(component)) %>% #### only show CS
      dplyr::mutate(KEGGPathwayName = sapply(as.character(geneSet), getKEGGPathwayName)) %>% # extract Go term names
      dplyr::select(geneSet, KEGGPathwayName, everything()) # re-order
  }
  
  # dt %>%
  #   dplyr::select(-c(in_cs)) %>%
  #   reactable::reactable(
  #     filterable=TRUE,
  #     minRows=10,
  #     columns = list(
  #       pip = reactable::colDef(format = reactable::colFormat(digits = 3)),
  #       logOddsRatio = reactable::colDef(style= function(value){color_sign(value)},
  #                                        format = reactable::colFormat(digits = 3)),
  #       beta = reactable::colDef(style= function(value){color_sign(value)},
  #                                format = reactable::colFormat(digits = 3)),
  #       nlog10pFishersExact = reactable::colDef(format = reactable::colFormat(digits = 3))
  #     ),
  #     rowStyle = function(index){
  #       if(dt$in_cs[index] == TRUE){
  #         list(background = "#e5f5e0")
  #       }
  #     },
  #     defaultSorted = list(nlog10pFishersExact='desc')
  #   )
  DT::datatable(dt,options = list(pageLength=10))
}

aFib-ebi-a-GCST006414

Go Biological Process

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/aFib-ebi-a-GCST006414_gobp.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Molecular Function

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/aFib-ebi-a-GCST006414_gomf.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Cellular Component

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/aFib-ebi-a-GCST006414_gocc.rdata")

gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

KEGG

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/aFib-ebi-a-GCST006414_kegg.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

IBD-ebi-a-GCST004131

Go Biological Process

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/IBD-ebi-a-GCST004131_gobp.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Molecular Function

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/IBD-ebi-a-GCST004131_gomf.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Cellular Component

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/IBD-ebi-a-GCST004131_gocc.rdata")

gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

KEGG

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/IBD-ebi-a-GCST004131_kegg.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

LDL-ukb-d-30780_irnt

Go Biological Process

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/LDL-ukb-d-30780_irnt_gobp.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Molecular Function

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/LDL-ukb-d-30780_irnt_gomf.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Cellular Component

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/LDL-ukb-d-30780_irnt_gocc.rdata")

gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

KEGG

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/LDL-ukb-d-30780_irnt_kegg.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

SBP-ukb-a-360

Go Biological Process

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/SBP-ukb-a-360_gobp.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Molecular Function

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/SBP-ukb-a-360_gomf.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Cellular Component

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/SBP-ukb-a-360_gocc.rdata")

gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

KEGG

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/SBP-ukb-a-360_kegg.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

SCZ-ieu-b-5102

Go Biological Process

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/SCZ-ieu-b-5102_gobp.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Molecular Function

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/SCZ-ieu-b-5102_gomf.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Cellular Component

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/SCZ-ieu-b-5102_gocc.rdata")

gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

KEGG

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/SCZ-ieu-b-5102_kegg.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

WBC-ieu-b-30

Go Biological Process

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/WBC-ieu-b-30_gobp.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Molecular Function

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/WBC-ieu-b-30_gomf.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

Go Cellular Component

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/WBC-ieu-b-30_gocc.rdata")

gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

KEGG

Modelling z-scores using Susie

load("/project/xinhe/xsun/ctwas/4.multi_tissue_process/results/susie/WBC-ieu-b-30_kegg.rdata")
gseasusie::enrichment_volcano(linear.fit, linear.ora)

interactive_table_linear_susie(fit = linear.fit, ora = linear.ora)

Modelling binarized y using logistic Susie

gseasusie::enrichment_volcano(logistic.fit, logistic.ora)

interactive_table_logistic_susie(logistic.fit, logistic.ora)

sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.4 (Ootpa)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el8-x86_64/lib/libopenblas_skylakexp-r0.3.13.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] GO.db_3.16.0         AnnotationDbi_1.60.2 IRanges_2.32.0      
 [4] S4Vectors_0.36.2     Biobase_2.58.0       BiocGenerics_0.44.0 
 [7] lubridate_1.9.2      forcats_1.0.0        stringr_1.5.0       
[10] dplyr_1.1.2          purrr_1.0.1          readr_2.1.4         
[13] tidyr_1.3.0          tibble_3.2.1         ggplot2_3.4.2       
[16] tidyverse_2.0.0      gseasusie_0.0.0.9000

loaded via a namespace (and not attached):
 [1] bitops_1.0-7           matrixStats_1.2.0      fs_1.5.2              
 [4] bit64_4.0.5            httr_1.4.7             rprojroot_2.0.3       
 [7] GenomeInfoDb_1.34.9    data.tree_1.1.0        tools_4.2.0           
[10] bslib_0.3.1            DT_0.22                irlba_2.3.5           
[13] utf8_1.2.2             R6_2.5.1               DBI_1.1.2             
[16] colorspace_2.0-3       withr_2.5.0            tidyselect_1.2.0      
[19] curl_4.3.2             bit_4.0.4              compiler_4.2.0        
[22] git2r_0.30.1           VEB.Boost_0.0.0.9039   cli_3.6.2             
[25] labeling_0.4.2         sass_0.4.1             scales_1.2.0          
[28] mvtnorm_1.2-4          mixsqp_0.3-48          digest_0.6.29         
[31] spatstat.utils_3.0-1   rmarkdown_2.21         XVector_0.38.0        
[34] pkgconfig_2.0.3        htmltools_0.5.7        highr_0.9             
[37] fastmap_1.1.0          htmlwidgets_1.6.2      susieR_0.12.40        
[40] rlang_1.1.2            rstudioapi_0.14        RSQLite_2.3.1         
[43] jquerylib_0.1.4        generics_0.1.3         farver_2.1.0          
[46] jsonlite_1.8.7         crosstalk_1.2.0        mr.ash.alpha_0.1-43   
[49] RCurl_1.98-1.12        magrittr_2.0.3         GenomeInfoDbData_1.2.9
[52] Matrix_1.6-1.1         Rcpp_1.0.11            munsell_0.5.0         
[55] fansi_1.0.3            lifecycle_1.0.4        stringi_1.7.6         
[58] yaml_2.3.5             zlibbioc_1.44.0        plyr_1.8.7            
[61] grid_4.2.0             blob_1.2.3             parallel_4.2.0        
[64] promises_1.2.0.1       crayon_1.5.1           lattice_0.20-45       
[67] Biostrings_2.66.0      hms_1.1.3              KEGGREST_1.38.0       
[70] knitr_1.42             pillar_1.9.0           glue_1.6.2            
[73] evaluate_0.15          emulator_1.2-21        png_0.1-7             
[76] vctrs_0.6.1            tzdb_0.3.0             httpuv_1.6.5          
[79] gtable_0.3.0           reshape_0.8.9          cachem_1.0.6          
[82] xfun_0.38              later_1.3.0            memoise_2.0.1         
[85] workflowr_1.7.1        timechange_0.2.0       ellipsis_0.3.2