Last updated: 2024-06-18

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Knit directory: fsusie-experiments/analysis/

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File Version Author Date Message
Rmd 0990217 Peter Carbonetto 2024-06-18 Made a few minor improvements to the rosmap analysis (rosmap.Rmd).
Rmd ace5ba7 Peter Carbonetto 2024-06-18 Another small fix to the distance-to-TSS plot.
Rmd aaf41ba Peter Carbonetto 2024-06-18 Small fix to compute_weighted_distance_to_tss and a fix to the distance-to-TSS plot.
Rmd 41c402c Peter Carbonetto 2024-06-18 Small fix to the rosmap analysis.
Rmd 0d322c4 Peter Carbonetto 2024-06-18 Small edit to rosmap.Rmd.
Rmd eed04e6 Peter Carbonetto 2024-05-03 Small edit to rosmap.Rmd.
Rmd 885d9a8 Peter Carbonetto 2024-05-03 Added steps to rosmap.Rmd to assess overlap in ‘high-confidence’ causal SNPs.
Rmd f7c26c8 Peter Carbonetto 2024-05-03 A few tweaks to some of the histograms in rosmap.Rmd.
Rmd b8e144c Peter Carbonetto 2024-05-03 Updated the distance-to-tss plot in the rosmap analysis.
Rmd 2520ca9 Peter Carbonetto 2024-05-03 A few adjustments to some of the plots of the susie results in the rosmap analysis.
Rmd 0ac5e4f Peter Carbonetto 2024-05-02 Switched to a different set of susie results in the rosmap analysis.
Rmd f4a0d35 Peter Carbonetto 2024-04-30 Small change to one of the code chunks in rosmap.Rmd.
Rmd 72b0109 Peter Carbonetto 2024-04-30 Working on overlap calculations in rosmap analysis.
html b5b3427 Peter Carbonetto 2024-04-29 Rebuilt the rosmap analysis with the various updates mentioned in previous commits.
Rmd 43aa3e2 Peter Carbonetto 2024-04-29 workflowr::wflow_publish("rosmap.Rmd", view = FALSE, verbose = TRUE)
Rmd 286a00d Peter Carbonetto 2024-04-29 Implemented functions get_cs_sizes_by_region and cs_sizes_histogram for rosmap analysis.
Rmd 9b6b1ee Peter Carbonetto 2024-04-29 Merge branch ‘main’ of github.com:stephenslab/fsusie-experiments into main
Rmd ed3a021 Peter Carbonetto 2024-04-29 Implemented functions region_sizes_histogram and num_snps_histogram for rosmap analysis.
Rmd 1f5e58e Peter Carbonetto 2024-04-29 Working on identifying overlap in eqtl and mqtl susie/fsusie results.
Rmd 357ccec Peter Carbonetto 2024-04-27 Added histogram of CS sizes for fsusie mqtl results.
Rmd 9f4070b Peter Carbonetto 2024-04-27 Added a couple plots for fine-mapping of mqtl data to rosmap analysis.
Rmd 259ebed Peter Carbonetto 2024-04-26 Added a couple to-dos to the rosmap.Rmd.
html ef4a009 Peter Carbonetto 2024-04-26 Working on the rosmap analysis.
Rmd b316ab6 Peter Carbonetto 2024-04-26 workflowr::wflow_publish("rosmap.Rmd", verbose = TRUE, view = FALSE)
Rmd cc246fe Peter Carbonetto 2024-04-26 workflowr::wflow_publish("rosmap.Rmd", verbose = TRUE)
Rmd 95e4aba Peter Carbonetto 2024-04-26 Fixed the distance-to-TSS plot in the rosmap analysis.
Rmd 366e27c Peter Carbonetto 2024-04-26 Added a couple todos to rosmap.Rmd.
Rmd e691fcd Peter Carbonetto 2024-04-26 Added distance-to-TSS plot in the rosmap analysis.
Rmd 4fb83bd Peter Carbonetto 2024-04-25 Added step to rosmap.Rmd to get TSS for each ‘region’ in susie results.
Rmd f8799a1 Peter Carbonetto 2024-04-25 Added plot to rosmap.Rmd for CS sizes.
Rmd 4ac6873 Peter Carbonetto 2024-04-25 Added a couple simple plots to the rosmap analysis.
Rmd 5ba241b Peter Carbonetto 2024-04-25 Wrote function get_gene_annotations used in the rosmap analysis.
Rmd b1b38f0 Peter Carbonetto 2024-04-25 Added steps to rosmap analysis to prepare the gene annotations into a convenient data frame.
html 142928b Peter Carbonetto 2024-04-25 First build of rosmap analysis; added gene annotation files.
Rmd a4e3d76 Peter Carbonetto 2024-04-25 workflowr::wflow_publish("analysis/rosmap.Rmd", verbose = TRUE)

Here we take a close look at a selection of the susie and fsusie fine-mapping results for the ROSMAP data.

To build the workflowr page, I ran these commands on midway2:

sinteractive -c 4 --mem=24G --time=20:00:00
module load R/4.1.0-no-openblas
module load pandoc/3.0.1
R
> .libPaths()[1]
# [1] "/home/pcarbo/R_libs_4_10_no_openblas"
> getwd()
# [1] "../analysis"
> workflowr::wflow_build("rosmap.Rmd",local=TRUE,view=FALSE,verbose=TRUE)

Load the packages as well as some additional custom functions used in the analysis below.

library(data.table)
library(ggplot2)
library(cowplot)
source("../code/rosmap_functions.R")
setDTthreads(1)

Load the susie fine-mapping results on the DLPFC bulk RNA-seq data.

datadir <- file.path("/project2/mstephens/fungen_xqtl/ftp_fgc_xqtl",
                     "analysis_result/finemapping_twas/prepared_results")
load(file.path(datadir,"susie_dlpfc_dejager_eQTL.RData"))                    
susie <- list(regions = regions,cs = cs,pips = pips)
susie$cs <- transform(susie$cs,pos = get_pos_from_id(id))
rm(regions,cs,pips)

Load the fsusie fine-mapping results on the DLPFC methylation data.

load(file.path(datadir,"fsusie_ROSMAP_DLPFC_mQTL.RData"))
fsusie_mqtl <- list(regions = regions,cs = cs,pips = pips)
rm(regions,cs,pips)

Load the fsusie fine-mapping results on the DLPFC histone acetylation (H3K9ac ChIP-seq) data.

load(file.path(datadir,"fsusie_ROSMAP_DLPFC_haQTL.RData"))
fsusie_haqtl <- list(regions = regions,cs = cs,pips = pips)
rm(regions,cs,pips)

Load the gene annotations. Specifically I extract here only the annotated gene transcripts for protein-coding genes as defined in the Ensembl/Havana database.

gene_file <-
  file.path("../data/genome_annotations",
    "Homo_sapiens.GRCh38.103.chr.reformatted.collapse_only.gene.gtf.gz")
genes <- get_gene_annotations(gene_file)

SuSiE fine-mapping of RNA-seq

Region sizes in base-pairs and SNPs:

p1 <- region_sizes_histogram(susie$regions,susie$pips,max_mb = 12)
p2 <- num_snps_histogram(susie$regions)
plot_grid(p1,p2)

Version Author Date
ef4a009 Peter Carbonetto 2024-04-26 
# 54 regions are larger than 12 Mb.

Number of CSs per region:

num_cs <- susie$regions$num_cs
table(CSs = num_cs)
num_cs <- num_cs[num_cs > 0]
pdat <- data.frame(num_cs = factor(num_cs))
ggplot(pdat,aes(num_cs)) +
  geom_bar(color = "white",fill = "darkblue",width = 0.5) +
  labs(x = "number of CSs",y = "number of regions") +
  theme_cowplot(font_size = 10)

# CSs
#    0    1    2    3    4    5    6    7    8    9   10   13 
# 4937 2508  725  144   47   18   10    9    3    1    1    1

CS sizes:

susie_cs_sizes <- get_cs_sizes_by_region(susie$cs)
cs_sizes_histogram(susie_cs_sizes,x_breaks = c(1:5,10,100,1000))

Version Author Date
ef4a009 Peter Carbonetto 2024-04-26

Plot showing distance between the susie SNP and the gene’s TSS, in which the distances are weighted by the PIPs (for SNPs in CSs only):

susie$regions <- add_tss_to_regions(susie$regions,genes)
bin_size <- 25000
bins   <- c(-Inf,seq(-1e6,1e6,bin_size),Inf)
counts <- compute_weighted_distance_to_tss(susie$regions,susie$cs,bins)
n      <- length(bins)
bins   <- bins[seq(2,n-2)]
counts <- counts[seq(2,n-2)]
pdat <- data.frame(pos = (bins + bin_size/2)/1e6,count = counts)
ggplot(pdat,aes(x = pos,y = count)) +
  geom_point(color = "darkblue") +
  geom_line(color = "darkblue") +
  labs(x = "distance to TSS (Mb)",
       y = "SNPs weighted by PIP") +
  theme_cowplot(font_size = 10)

fSuSiE fine-mapping of haQTLs

Region sizes in base-pairs and SNPs:

p1 <- region_sizes_histogram(fsusie_haqtl$regions,fsusie_haqtl$pips,
                             max_mb = 10)
p2 <- num_snps_histogram(fsusie_haqtl$regions,max_snps = 50000)
plot_grid(p1,p2)

Version Author Date
b5b3427 Peter Carbonetto 2024-04-29 
# 8 regions are larger than 10 Mb.
# 8 regions have more than 50000 SNPs.

Number of CSs per region:

num_cs <- fsusie_haqtl$regions$num_cs
table(CSs = num_cs)
num_cs <- num_cs[num_cs > 0]
pdat <- data.frame(num_cs = factor(num_cs))
ggplot(pdat,aes(num_cs)) +
  geom_bar(color = "white",fill = "darkblue",width = 0.5) +
  labs(x = "number of CSs",y = "number of regions") +
  theme_cowplot(font_size = 10)

# CSs
#   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19 
#  85 133 196 186 171 171  92  74  48  45  19  20   8  10   9   8   7   1   4   4 
#  20 
#  14

CS sizes:

fsusie_haqtl_cs_sizes <- get_cs_sizes_by_region(fsusie_haqtl$cs)
cs_sizes_histogram(fsusie_haqtl_cs_sizes,x_breaks = c(1:5,10,100,1000)) +
  scale_y_continuous(trans = "log10")

Version Author Date
b5b3427 Peter Carbonetto 2024-04-29

fSuSiE fine-mapping of mQTLs

Region sizes in base-pairs and SNPs:

p1 <- region_sizes_histogram(fsusie_mqtl$regions,fsusie_mqtl$pips,
                             max_mb = 10)
p2 <- num_snps_histogram(fsusie_mqtl$regions,max_snps = 50000)
plot_grid(p1,p2)

Version Author Date
b5b3427 Peter Carbonetto 2024-04-29 
# 8 regions are larger than 10 Mb.
# 5 regions have more than 50000 SNPs.

Number of CSs per region:

num_cs <- fsusie_mqtl$regions$num_cs
table(CSs = num_cs)
num_cs <- num_cs[num_cs > 0]
pdat <- data.frame(num_cs = factor(num_cs))
ggplot(pdat,aes(num_cs)) +
  geom_bar(color = "white",fill = "darkblue",width = 0.5) +
  labs(x = "number of CSs",y = "number of regions") +
  theme_cowplot(font_size = 10)

# CSs
#   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
#   3   6   2   5  10  19  10  13  16  15  26  23  54  12  45 163 871

CS sizes:

fsusie_mqtl_cs_sizes <- get_cs_sizes_by_region(fsusie_mqtl$cs)
cs_sizes_histogram(fsusie_mqtl_cs_sizes,x_breaks = c(1:5,10,100,1000)) +
  scale_y_continuous(trans = "log10")

Version Author Date
b5b3427 Peter Carbonetto 2024-04-29

Examine overlap among expression, methylation and histone acetylation SNPs

TO DO: Determine overlap between expression, methylation and histone acetylation SNPs simply by extracting the SNPs with PIP > 0.75 that are also in CSs.

highconf_susie <- get_highconf_snps(susie$pips,level = 0.6)
highconf_mqtl  <- get_highconf_snps(fsusie_mqtl$pips,level = 0.6)
highconf_haqtl <- get_highconf_snps(fsusie_haqtl$pips,level = 0.6)
ids <- unique(c(highconf_susie$id,highconf_mqtl$id,highconf_haqtl$id))
dat <- data.frame(id = ids,
                  eqtl  = is.element(ids,highconf_susie$id),
                  mqtl  = is.element(ids,highconf_mqtl$id),
                  haqtl = is.element(ids,highconf_haqtl$id))
table(dat[c("eqtl","mqtl")])
table(dat[c("eqtl","haqtl")])
table(dat[c("mqtl","haqtl")])

TO DO NEXT: Put together a data frame containing more information about these high-confidence, overlapping SNPs.


sessionInfo()
# R version 4.1.0 (2021-05-18)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: CentOS Linux 7 (Core)
# 
# Matrix products: default
# BLAS:   /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRblas.so
# LAPACK: /software/R-4.1.0-no-openblas-el7-x86_64/lib64/R/lib/libRlapack.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] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_1.1.1     ggplot2_3.5.1     data.table_1.14.0
# 
# loaded via a namespace (and not attached):
#  [1] tidyselect_1.1.1  xfun_0.24         bslib_0.4.2       purrr_0.3.4      
#  [5] colorspace_2.0-2  vctrs_0.6.5       generics_0.1.0    htmltools_0.5.5  
#  [9] yaml_2.2.1        utf8_1.2.1        rlang_1.1.1       R.oo_1.24.0      
# [13] jquerylib_0.1.4   later_1.2.0       pillar_1.6.1      glue_1.4.2       
# [17] withr_2.5.0       DBI_1.1.1         R.utils_2.10.1    lifecycle_1.0.3  
# [21] stringr_1.4.0     munsell_0.5.0     gtable_0.3.0      workflowr_1.7.1.1
# [25] R.methodsS3_1.8.1 evaluate_0.14     labeling_0.4.2    knitr_1.33       
# [29] fastmap_1.1.0     httpuv_1.6.1      fansi_0.5.0       highr_0.9        
# [33] Rcpp_1.0.12       promises_1.2.0.1  scales_1.3.0      cachem_1.0.5     
# [37] jsonlite_1.7.2    farver_2.1.0      fs_1.5.0          digest_0.6.27    
# [41] stringi_1.6.2     dplyr_1.0.7       rprojroot_2.0.2   grid_4.1.0       
# [45] cli_3.6.1         tools_4.1.0       magrittr_2.0.1    sass_0.4.0       
# [49] tibble_3.1.2      crayon_1.4.1      whisker_0.4       pkgconfig_2.0.3  
# [53] ellipsis_0.3.2    assertthat_0.2.1  rmarkdown_2.9     R6_2.5.0         
# [57] git2r_0.28.0      compiler_4.1.0