Last updated: 2025-03-20

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Knit directory: fsusie-experiments/analysis/

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Rmd 1c8eeb7 Peter Carbonetto 2025-03-20 wflow_publish("rosmap_overview.Rmd", view = FALSE)
Rmd acfadd1 Peter Carbonetto 2025-03-20 Made a few improvements to the code and text of the rosmap_analysis.
Rmd c102af9 Peter Carbonetto 2025-03-20 Added a scatterplot comparing number of CSs per TAD (susie vs. fsusie).
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Rmd c69e187 Peter Carbonetto 2025-03-20 Created plot showing TAD sizes from the methylation fine-mapping results.
Rmd 2a5c706 Peter Carbonetto 2025-03-20 Added code to the rosmap_overview analysis to load the methylation SNP results.
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Rmd bc6d0a1 Peter Carbonetto 2025-03-20 Started working on rosmap_overview analysis.

ADD SOME TEXT HERE GIVING AN OVERVIEW OF THIS ANALYSIS.

Note: If you would like to run this analysis on your computer, you will first need to download the fine-mapping outputs. They can be downloaded from here. Once you have downloaded the files, copy them to the “outputs” subdirectory.

Load some packges used in the code below:

library(data.table)
library(ggplot2)
library(cowplot)

Methylation fine-mapping

First I define a helper function for loading the enrichment results:

# The "n" argument specifies the number of "meta data" columns.
# Columns after that are treated as the enrichment results. These
# columns contain only binary data (0 or 1) indicating whether or not
# the genomic feature (genetic variant or molecular trait location)
# is assigned that specific annotation.
read_enrichment_results <- function (filename, n) {
  out <- fread(filename,sep = "\t",stringsAsFactors = FALSE,header = TRUE)
  class(out) <- "data.frame"
  out <- transform(out,chr = factor(chr))
  cols <- seq(n + 1,ncol(out))
  for (i in cols)
    out[[i]] <- factor(out[[i]])
  return(out)
}

Next I load methylation SNP results generated by SuSiE-topPC and fSuSiE:

methyl_snps_susie_file <-
  "../outputs/ROSMAP_mQTL_cs_snp_toppc1_annotation.tsv.gz"
methyl_snps_fsusie_file <- "../outputs/ROSMAP_mQTL_cs_snp_annotation.tsv.gz"
methyl_snps_susie  <- read_enrichment_results(methyl_snps_susie_file,n = 6)
methyl_snps_fsusie <- read_enrichment_results(methyl_snps_fsusie_file,n = 7)
methyl_snps_susie$region <-
  sapply(strsplit(methyl_snps_susie$cs,":",fixed = TRUE),"[[",2)
methyl_snps_susie  <- transform(methyl_snps_susie,
                                region = factor(region),
                                cs     = factor(cs),
                                pc     = factor(pc))
methyl_snps_fsusie <- transform(methyl_snps_fsusie,
                                cs     = factor(cs),
                                region = factor(region),
                                study  = factor(study))

This is the number of fine-mapping regions (TADs) that contained at least one CS in each of the analyses:

nlevels(methyl_snps_susie$region)
nlevels(methyl_snps_fsusie$region)
# [1] 1236
# [1] 1327

This is a function we will use below to get the sizes of the TADs (in Mb):

get_tad_sizes <- function (tads) {
  tads <- strsplit(tads,"_",fixed = TRUE)
  pos0 <- as.numeric(sapply(tads,"[[",2))
  pos1 <- as.numeric(sapply(tads,"[[",3))
  return((pos1 - pos0)/1e6)
}

This plot summarizes the sizes of the TADs that were analyzed by SuSiE-topPC and fSuSiE:

plot_tad_sizes <- function (tads) {
  tad_size <- get_tad_sizes(tads)
  pdat <- data.frame(tad_size = tad_size)
  return(ggplot(pdat,aes(x = tad_size)) +
         geom_histogram(color = "white",fill = "darkblue",bins = 48) +
         labs(x = "size (Mb)",y = "number of TADs") +
         theme_cowplot(font_size = 10))
}
tads <- levels(methyl_snps_fsusie$region)
plot_tad_sizes(tads) +
  scale_x_continuous(limits = c(2,9),breaks = 1:10) +
  scale_y_continuous(breaks = seq(0,100,10))

Some more useful statistics on the TAD sizes:

tad_size <- get_tad_sizes(tads)
range(tad_size)
mean(tad_size)
median(tad_size)
sum(tad_size > 9)
# [1]  2.320952 34.727189
# [1] 4.54465
# [1] 4.154266
# [1] 19

These histograms summarize the number of CSs per TAD:

get_cs_vs_tad_size <- function (dat) {
  tads <- levels(dat$region)
  out <- data.frame(tad      = tads,
                    tad_size = get_tad_sizes(tads),
                    num_cs   = tapply(dat$cs,dat$region,
                                      function (x) length(unique(x))))
  rownames(out) <- NULL
  return(out)
}
pdat1 <- get_cs_vs_tad_size(methyl_snps_susie)
pdat2 <- get_cs_vs_tad_size(methyl_snps_fsusie)
pdat1 <- transform(pdat1,num_cs = factor(num_cs,1:16))
pdat2 <- transform(pdat2,num_cs = factor(num_cs,1:20))
p1 <- ggplot(pdat1,aes(x = num_cs)) +
  geom_histogram(stat = "count",color = "white",fill = "darkblue") +
  scale_x_discrete(drop = FALSE) +
  labs(x = "number of CSs",y = "number of TADs",title = "SuSiE-topPC") +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
p2 <- ggplot(pdat2,aes(x = num_cs)) +
  geom_histogram(stat = "count",color = "white",fill = "darkblue") +
  scale_x_discrete(drop = FALSE) +
  labs(x = "number of CSs",y = "number of TADs",title = "fSuSiE") +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
plot_grid(p1,p2,nrow = 1,ncol = 2)

Compare discovery of causal SNPs (number of CSs) in the SuSiE-topPC and fSuSiE analyses:

dat1 <- get_cs_vs_tad_size(methyl_snps_susie)
dat2 <- get_cs_vs_tad_size(methyl_snps_fsusie)
dat1 <- dat1[c(1,3)]
dat2 <- dat2[c(1,3)]
names(dat1) <- c("tad","num_cs_susie")
names(dat2) <- c("tad","num_cs_fsusie")
dat <- merge(dat1,dat2,all = TRUE)
rows <- which(is.na(dat$num_cs_susie))
dat[rows,"num_cs_susie"] <- 0
pdat <- melt(with(dat,table(num_cs_susie,num_cs_fsusie)))
rows <- which(pdat$value == 0)
pdat[rows,"value"] <- NA
ggplot(pdat,aes(x = num_cs_susie,y = num_cs_fsusie,size = value)) +
  geom_point(color = "white",fill = "darkblue",shape = 21) +
  geom_abline(intercept = 0,slope = 1,color = "black",linetype = "dotted") +
  scale_x_continuous(breaks = seq(0,20,2),limits = c(0,20)) +
  scale_y_continuous(breaks = seq(0,20,2),limits = c(0,20)) +
  scale_size(breaks = c(1,10,100)) +
  labs(x = "SuSiE-topPC",y = "fSuSiE",size = "number of TADs") +
  theme_cowplot(font_size = 10)

Compare the sizes of the CSs in the SuSiE-topPC and fSuSiE analyses:

bins <- c(0,1,2,5,10,20,Inf)
cs_size_susie  <- as.numeric(table(methyl_snps_susie$cs))
cs_size_fsusie <- as.numeric(table(methyl_snps_fsusie$cs))
cs_size_susie  <- cut(cs_size_susie,bins)
cs_size_fsusie <- cut(cs_size_fsusie,bins)
levels(cs_size_susie) <- bins[-1]
levels(cs_size_fsusie) <- bins[-1]
p1 <- ggplot(data.frame(cs_size = cs_size_susie),aes(x = cs_size)) +
  geom_histogram(stat = "count",color = "white",fill = "darkblue",
                 width = 0.65) +
  labs(x = "CS size",y = "number of CSs",title = "SuSiE-topPC") +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
p2 <- ggplot(data.frame(cs_size = cs_size_fsusie),aes(x = cs_size)) +
  geom_histogram(stat = "count",color = "white",fill = "darkblue",
                 width = 0.65) +
  labs(x = "CS size",y = "number of CSs",title = "fSuSiE") +
  theme_cowplot(font_size = 10) +
  theme(plot.title = element_text(size = 10,face = "plain"))
plot_grid(p1,p2,nrow = 1,ncol = 2)

H3K27ac fine-mapping

Load the H3K27ac SNP results generated by SuSiE-topPC and fSuSiE:

ha_snps_susie_file <- "../outputs/ROSMAP_haQTL_cs_snp_toppc1_annotation.tsv.gz"
ha_snps_fsusie_file <- "../outputs/ROSMAP_haQTL_cs_snp_annotation.tsv.gz"
ha_snps_susie  <- read_enrichment_results(ha_snps_susie_file,n = 6)
ha_snps_fsusie <- read_enrichment_results(ha_snps_fsusie_file,n = 7)
ha_snps_susie$region <-
  sapply(strsplit(ha_snps_susie$cs,":",fixed = TRUE),"[[",2)
ha_snps_susie  <- transform(ha_snps_susie,
                            region = factor(region),
                            cs     = factor(cs),
                            pc     = factor(pc))
ha_snps_fsusie <- transform(ha_snps_fsusie,
                            cs     = factor(cs),
                            region = factor(region),
                            study  = factor(study))

sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS Sonoma 14.7.1
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
# LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# time zone: America/Chicago
# tzcode source: internal
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_1.1.3     ggplot2_3.5.0     data.table_1.15.2
# 
# loaded via a namespace (and not attached):
#  [1] sass_0.4.9        utf8_1.2.4        generics_0.1.3    stringi_1.8.3    
#  [5] digest_0.6.34     magrittr_2.0.3    evaluate_0.23     grid_4.3.3       
#  [9] fastmap_1.1.1     plyr_1.8.9        R.oo_1.26.0       rprojroot_2.0.4  
# [13] workflowr_1.7.1   jsonlite_1.8.8    R.utils_2.12.3    whisker_0.4.1    
# [17] promises_1.2.1    fansi_1.0.6       scales_1.3.0      jquerylib_0.1.4  
# [21] cli_3.6.4         rlang_1.1.5       R.methodsS3_1.8.2 munsell_0.5.0    
# [25] withr_3.0.0       cachem_1.0.8      yaml_2.3.8        tools_4.3.3      
# [29] reshape2_1.4.4    dplyr_1.1.4       colorspace_2.1-0  httpuv_1.6.14    
# [33] vctrs_0.6.5       R6_2.5.1          lifecycle_1.0.4   git2r_0.33.0     
# [37] stringr_1.5.1     fs_1.6.5          pkgconfig_2.0.3   pillar_1.9.0     
# [41] bslib_0.6.1       later_1.3.2       gtable_0.3.4      glue_1.8.0       
# [45] Rcpp_1.0.12       xfun_0.42         tibble_3.2.1      tidyselect_1.2.1 
# [49] highr_0.10        knitr_1.45        farver_2.1.1      htmltools_0.5.8.1
# [53] rmarkdown_2.26    labeling_0.4.3    compiler_4.3.3