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t2d_2012.Rmd
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t2d_2012.Rmd
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---
title: "Enrichment results of type 2 diabetes"
author: "Xiang Zhu"
date: 2017-04-12
output: workflowr::wflow_html
---
```{r, echo=FALSE}
data.name <- "t2d2012"
# DO NOT MODIFY THE CODES BELOW
source("~/Dropbox/rss/Data/gwas_gsea/notebook_util.R")
# specify file paths
gsea.path <- "~/Dropbox/rss/Data/gwas_gsea/summarize_enrichment/round2_pathway/"
innout.path <- "~/Dropbox/rss/Data/gwas_gsea/summarize_enrichment/round2_pathway_innout/"
topexpr.path <- "~/Dropbox/rss/Data/gwas_gtex_topexpr/aggregate_results/"
toptssc.path <- "~/Dropbox/rss/Data/gwas_gtex_toptssc/aggregate_results/"
gtexclust.path <- "~/Dropbox/rss/Data/gwas_gtexclust/round2_results/"
topexpr.innout.path <- "~/Dropbox/rss/Data/gwas_gtex_topexpr/aggregate_results_innout/"
toptssc.innout.path <- "~/Dropbox/rss/Data/gwas_gtex_toptssc/aggregate_results_innout/"
gtexclust.innout.path <- "~/Dropbox/rss/Data/gwas_gtexclust/aggregate_results_innout/"
# load gtex tissue names
source("~/Dropbox/rss/Data/gtex_database/color/load_gtex_color.R")
tissue.df <- color.dat[, c("tissue.name","tissue.abbr")]
names(tissue.df) <- c("tissue","id")
```
## Input data
Results below were generated from the GWAS summary statistics published in the paper
["Large-Scale Association Analysis Provides Insights into the Genetic Architecture and Pathophysiology of Type 2 Diabetes" (*Nature Genetics*, 2012)](https://www.ncbi.nlm.nih.gov/pubmed/22885922).
The summary data file is available at http://diagram-consortium.org/downloads.html.
## Analysis results
Enrichment analyses are summarized by the following three quantities.
- **BF:** Bayes factor comparing the enrichment model against the baseline model;
- **Outside $\pi$:** proportion of trait-associated SNPs that are "outside" the gene set;
- **Inside $\pi$:** proportion of trait-associated SNPs that are "inside" the gene set.
The first quantity reflects the significance of enrichment,
whereas the last two capture the magnitude of enrichment.
For each gene set, we report these three quantities in the
last three columns of tables below, on log 10 scale.
### Biological pathways
```{r, echo=FALSE}
# load results and create an output data frame
gsea.path <- paste0(gsea.path,data.name,"_pathway_201609.mat")
prop.path <- paste0(innout.path,data.name,"_pathway_201609_innout.mat")
gsea.df <- gsea.mat2df(gsea.path, prop.path)
# display the data table
gsea.dt <- DT::datatable(gsea.df, rownames=FALSE, class="white-space: nowrap", escape=FALSE) %>% DT::formatRound(c("Log10 BF"), 3)
gsea.dt
```
### Tissue highly expressed genes
```{r, echo=FALSE}
# load results and create an output data frame
gtex.path <- paste0(topexpr.path, data.name, "_gtextop100expr_201609.mat")
prop.path <- paste0(topexpr.innout.path,data.name,"_gtextop100expr_201609_innout.mat")
gtex.df <- gtex.mat2df(gtex.path, prop.path)
# clean the tissue name
gtex.df <- merge(gtex.df, tissue.df, by="id")
gtex.df$id <- as.character(gtex.df$tissue)
gtex.df$tissue <- NULL
gtex.df <- plyr::arrange(gtex.df, -log10.bf)
# display the data table
gtex.dt <- DT::datatable(gtex.df, rownames=FALSE, class="white-space: nowrap", escape=FALSE, colnames=c("Tissue name"="id","Log10 BF"="log10.bf")) %>% DT::formatRound(c("Log10 BF"), 3)
gtex.dt
```
### Tissue selectively expressed genes
```{r, echo=FALSE}
# load results and create an output data frame
gtex.path <- paste0(toptssc.path, data.name, "_gtextop100tssc_201609.mat")
prop.path <- paste0(toptssc.innout.path,data.name,"_gtextop100tssc_201609_innout.mat")
gtex.df <- gtex.mat2df(gtex.path, prop.path)
# display the data table
gtex.dt <- DT::datatable(gtex.df, rownames=FALSE, class="white-space: nowrap", escape=FALSE, colnames=c("Tissue name"="id","Log10 BF"="log10.bf")) %>% DT::formatRound(c("Log10 BF"), 3)
gtex.dt
```
### Cluster distinctively expressed genes
```{r, echo=FALSE}
# load results and create an output data frame
gtex.path <- paste0(gtexclust.path, data.name, "_gtexclust_201609.mat")
prop.path <- paste0(gtexclust.innout.path,data.name,"_gtexclust_201609_innout.mat")
gtex.df <- gtex.mat2df(gtex.path, prop.path)
# display the data table
gtex.dt <- DT::datatable(gtex.df, rownames=FALSE, class="white-space: nowrap", escape=FALSE, colnames=c("Cluster ID"="id","Log10 BF"="log10.bf")) %>% DT::formatRound(c("Log10 BF"), 3)
gtex.dt
```
The relationship between tissues and clusters is shown in
Figure 1 of [Dey et al. (2017)](http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1006599);
see below.
