Examine ROSMAP RNA-seq and proteomics fine-mapping results
William Denault, Hao Sun, Angjing Liu, Peter Carbonetto, Gao Wang
Last updated: 2025-05-01
Checks: 7 0
Knit directory:
fsusie-experiments/analysis/
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Untracked files:
Untracked: .DS_Store
Untracked: analysis/cs_sizes_rnaseq.pdf
Untracked: analysis/rosmap_overview_cache/
Untracked: data/12864_2013_7006_MOESM2_ESM.csv.gz
Untracked: data/IlluminaHumanMethylation450K.rda
Untracked: data/ROSMAP_NIA_WGS.leftnorm.bcftools_qc.plink_qc.afreq.gz
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Untracked: data/analysis_result/ROSMAP_haQTL.chr3_43915257_48413435.fsusie_mixture_normal_top_pc_weights.rds
Untracked: data/analysis_result/ROSMAP_mQTL.chr3_43915257_48413435.fsusie_mixture_normal_top_pc_weights.rds
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Untracked: outputs/CR1_CR2_obj.RData
Untracked: outputs/ROSMAP_DLPFC_mega_eQTL.cs_only.tsv.gz
Untracked: outputs/ROSMAP_DLPFC_pQTL.cs_only.tsv.gz
Untracked: outputs/ROSMAP_haQTL_cs_effect_ha_peak_annotation.tsv.gz
Untracked: outputs/ROSMAP_haQTL_cs_snp_annotation.tsv.gz
Untracked: outputs/ROSMAP_haQTL_cs_snp_toppc1_annotation.tsv.gz
Untracked: outputs/ROSMAP_haQTL_qtl_snp_qval0.05.tsv.gz
Untracked: outputs/ROSMAP_haQTL_qtl_snp_qval0.05_annotation.tsv.gz
Untracked: outputs/ROSMAP_mQTL_cs_effect_cpg_annotation.tsv.gz
Untracked: outputs/ROSMAP_mQTL_cs_snp_annotation.tsv.gz
Untracked: outputs/ROSMAP_mQTL_cs_snp_toppc1_annotation.tsv.gz
Untracked: outputs/ROSMAP_mQTL_qtl_snp_qval0.05.tsv.gz
Untracked: outputs/ROSMAP_mQTL_qtl_snp_qval0.05_annotation.tsv.gz
Unstaged changes:
Modified: analysis/rosmap_overview.Rmd
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click on the hyperlinks in the table below to view the files as they
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 5a65993 | Peter Carbonetto | 2025-05-01 | workflowr::wflow_publish("rosmap_rnaseq_protein.Rmd") |
| Rmd | 1040101 | Peter Carbonetto | 2025-04-30 | Small edit to rosmap_rnaseq_protein.Rmd. |
| html | c8753a1 | Peter Carbonetto | 2025-04-30 | First build of the rosmap_rnaseq_protein analysis. |
| Rmd | 496ed0f | Peter Carbonetto | 2025-04-30 | wflow_publish("rosmap_rnaseq_protein.Rmd", verbose = TRUE, view = FALSE) |
| Rmd | 8a4d3fb | Peter Carbonetto | 2025-04-30 | workflowr::wflow_publish("index.Rmd") |
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 a few packages used in the code below:
library(data.table)
library(ggplot2)
library(cowplot)Load the RNA-seq fine-mapping results:
rnaseq <- fread("../outputs/ROSMAP_DLPFC_mega_eQTL.cs_only.tsv.gz",
sep = "\t",header = TRUE,stringsAsFactors = FALSE)
class(rnaseq) <- "data.frame"
rnaseq <- rnaseq[c("gene","variant_id","chr","pos","ref","alt","maf",
"pip","cs_coverage_0.95","context")]
rnaseq <-
transform(rnaseq,
gene = factor(gene),
chr = factor(chr),
ref = factor(ref),
alt = factor(alt),
cs_coverage_0.95 = factor(cs_coverage_0.95),
context = factor(context))Plot a histogram of the CS sizes for the RNA-seq fine-mapping:
bins <- c(0,1,2,5,10,20,Inf)
gene_cs <- with(rnaseq,paste(gene,cs_coverage_0.95,sep = "_"))
gene_cs <- factor(gene_cs)
cs_size <- as.numeric(table(gene_cs))
cs_size <- cut(cs_size,bins)
levels(cs_size) <- bins[-1]
p <- ggplot(data.frame(cs_size = cs_size),aes(x = cs_size)) +
geom_histogram(stat = "count",color = "white",fill = "dodgerblue",
width = 0.65) +
labs(x = "CS size",y = "number of CSs") +
theme_cowplot(font_size = 10)
print(p)
| Version | Author | Date |
|---|---|---|
| c8753a1 | Peter Carbonetto | 2025-04-30 |
Load the protein fine-mapping results:
protein <- fread("../outputs/ROSMAP_DLPFC_pQTL.cs_only.tsv.gz",
sep = "\t",header = TRUE,stringsAsFactors = FALSE)
class(protein) <- "data.frame"
protein <- protein[c("gene","variant_id","chr","pos","ref","alt","maf",
"pip","cs_coverage_0.95","context")]
protein <-
transform(protein,
gene = factor(gene),
chr = factor(chr),
ref = factor(ref),
alt = factor(alt),
cs_coverage_0.95 = factor(cs_coverage_0.95),
context = factor(context))
protein <- subset(protein,context == "DLPFC_Bennett_pQTL")Plot a histogram of the CS sizes for the proteomics fine-mapping:
gene_cs <- with(protein,paste(gene,cs_coverage_0.95,sep = "_"))
gene_cs <- factor(gene_cs)
cs_size <- as.numeric(table(gene_cs))
cs_size <- cut(cs_size,bins)
levels(cs_size) <- bins[-1]
p <- ggplot(data.frame(cs_size = cs_size),aes(x = cs_size)) +
geom_histogram(stat = "count",color = "white",fill = "darkorchid",
width = 0.65) +
labs(x = "CS size",y = "number of CSs") +
theme_cowplot(font_size = 10)
print(p)
sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS 15.4.1
#
# Matrix products: default
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# time zone: America/Chicago
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# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
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