# loci2path Annotating a given genomic locus or a set of genomic loci is an important yet challenging task. This is especially true for the non-coding part of the genome which is enormous yet poorly understood. Since gene set enrichment analyses have demonstrated to be effective approach to annotate a set of genes, this idea can be extended to explore the enrichment of functional elements or features in a set of genomic intervals to reveal potential functional connections. `loci2path` is a novel computational strategy that takes advantage of the newly emerged, genome-wide and tissue-specific expression quantitative trait loci (eQTL) information to help annotate a set of genomic intervals in terms of transcription regulation. By checking the presence or absence of millions of eQTLs in the set of genomic intervals of interest, loci2path build a bridge connecting genomic intervals to biological pathway or pre-defined biological-meaningful gene sets. Our method enjoys two key advantages over existing methods: first, we no longer rely on proximity to link a locus to a gene which has shown to be unreliable; second, eQTL allows us to provide the regulatory annotation under the context of specific tissue types which is important. # Install ``` library(devtools) install_github("stanleyxu/loci2path") ``` * The Bioconductor package is under review process; Once released you can also install loci2path using `BiocManager::install()` function from bioconductor. # Query loci2path with genomic regions ## 1. Query regions `loci2path` takes query regions in the format of `GenomicRanges`. Only the Genomic Locations (chromosomes, start and end position) will be used. Strand information and other metadata columns are ignored. In the demo data, 47 regions associated with Psoriasis disease were downloaded from **immunoBase.org** and used as demo query regions. ```{r query_region} require(GenomicRanges) bed.file <- system.file("extdata", "query/Psoriasis.BED", package = "loci2path") query.bed <- read.table(bed.file, header=FALSE) colnames(query.bed) <- c("chr","start","end") query.gr <- makeGRangesFromDataFrame(query.bed) ``` ## 2. Prepare eQTL sets. eQTL sets are entities recording 1-to-1 links between eQTL SNPs and genes. eQTL set entity also contains the following information: tissue name for the eQTL study, IDs and genomic ranges for the eQTL SNPs, IDs for the associated genes. eQTL set can be constructed manually by specifying the corresponding information in each slot. eQTL set list is a list of multiple eQTL sets, usually collected from different tissues. Below is an example to construct customized eQTL set and eQTL set list using demo data files. In the demo data folder, three eQTL sets downloaded from GTEx project are included. Due to the large size, each eQTL dataset is down sampled to 3000 records for demostration purpose. #### 2.1 construct eQTL set ```{r eset} library(loci2path) library(GenomicRanges) brain.file <- system.file("extdata", "eqtl/brain.gtex.txt", package="loci2path") tab <- read.table(brain.file, stringsAsFactors=FALSE, header=TRUE) snp.gr <- GRanges(seqnames=Rle(tab$snp.chr), ranges=IRanges(start=tab$snp.pos, width=1)) brain.eset <- eqtlSet(tissue="brain", eqtlId=tab$snp.id, eqtlRange=snp.gr, gene=as.character(tab$gene.entrez.id)) brain.eset skin.file <- system.file("extdata", "eqtl/skin.gtex.txt", package="loci2path") tab=read.table(skin.file, stringsAsFactors=FALSE, header=TRUE) snp.gr <- GRanges(seqnames=Rle(tab$snp.chr), ranges=IRanges(start=tab$snp.pos, width=1)) skin.eset <- eqtlSet(tissue="skin", eqtlId=tab$snp.id, eqtlRange=snp.gr, gene=as.character(tab$gene.entrez.id)) skin.eset blood.file <- system.file("extdata", "eqtl/blood.gtex.txt", package="loci2path") tab <- read.table(blood.file, stringsAsFactors=FALSE, header=TRUE) snp.gr <- GRanges(seqnames=Rle(tab$snp.chr), ranges=IRanges(start=tab$snp.pos, width=1)) blood.eset <- eqtlSet(tissue="blood", eqtlId=tab$snp.id, eqtlRange=snp.gr, gene=as.character(tab$gene.entrez.id)) blood.eset ``` #### 2.2 construct eQTL set list ```{r esetlist} eset.list <- list(Brain=brain.eset, Skin=skin.eset, Blood=blood.eset) eset.list ``` ## 3. Prepare gene set collection A geneset collection contains a list of gene sets, with each gene set is represented as a vector of member genes. A vector of description is also provided as the metadata slot for each gene set. The total number of gene in the geneset collection is also required to perform the enrichment test. In this tutorial the BIOCARTA pathway collection was downloaded from MSigDB. ```{r} biocarta.link.file <- system.file("extdata", "geneSet/biocarta.txt", package="loci2path") biocarta.set.file <- system.file("extdata", "geneSet/biocarta.set.txt", package="loci2path") biocarta.link <- read.delim(biocarta.link.file, header=FALSE, stringsAsFactors=FALSE) set.geneid <- read.table(biocarta.set.file, stringsAsFactors=FALSE) set.geneid <- strsplit(set.geneid[,1], split=",") names(set.geneid) <- biocarta.link[,1] head(biocarta.link) head(set.geneid) ``` In order to build gene set, we also need to know the total number of genes in order to perform enrichment test. In this study, the total number of gene in MSigDB pathway collection is 31,847(Liberzon et. al, 2015). ```{r} #build geneSet biocarta <- geneSet( numGene=31847, description=biocarta.link[,2], geneSetList=set.geneid) biocarta ``` ## 4.Perform query #### 4.1 peroform query from one eQTL set ```{r} res.one <- query( query.gr=query.gr, loci=skin.eset, path=biocarta) ``` Enrichment result table ``` res.one$result.table ``` All the genes associated with eQTLs covered by the query region ``` res.one$cover.gene ``` #### 4.2 peroform query from multiple eQTL sets ```{r} #query from one eQTL set. res.esetlist <- query( query.gr=query.gr, loci=eset.list, path=biocarta) ``` Enrichment result table ``` resultTable(res.esetlist) ``` All the genes associated with eQTLs covered by the query region; names of the list are tissue names from eqtl set list ``` coveredGene(res.esetlist) ``` #### 4.3 parallel query from multiple eQTL sets ```{r} #query from one eQTL set. res.paral <- query( query.gr=query.gr, loci=eset.list, path=biocarta, parallel=TRUE) ``` Should return the same result as res.esetlist #### 4.4 obtain tissue enrichment for query regions In this case, in the generic function `query`, only the argunment `loci` need to be provided. This will trigger the query of tissue specificity ```{r} #query tissue specificity gr.tissue <- query(query.gr, loci=eset.list) gr.tissue ``` ## 5. Explore query result #### 5.1 obtain eQTL gene list ```{r} #all the genes associated with eQTLs covered by the query region res.one$cover.gene #all the genes associated with eQTLs covered by the query region; #names of the list are tissue names from eqtl set list coveredGene(res.esetlist) ``` #### 5.2 obtain average tissue degree for each pathway ```{r} tissue.degree=getTissueDegree(res.esetlist, eset.list) #check gene-tissue mapping for each gene head(tissue.degree$gene.tissue.map) #check degree for each gene head(tissue.degree$gene.tissue.degree) #average tissue degree for the input result table tissue.degree$mean.tissue.degree #add avg. tissue degree to existing result table res.tissue=data.frame(resultTable(res.esetlist), t.degree=tissue.degree$mean.tissue.degree) ``` #### 5.3 extract tissue-pathway heatmap ```{r} #extract tissue-pathway matrix mat <- getMat(res.esetlist, test.method = "gene") #plot heatmap heatmap.para <- getHeatmap(res.esetlist) ``` #### 5.4 extract word cloud from result ```{r} #plot word cloud wc <- getWordcloud(res.esetlist) ``` #### 5.5 plot p-value distribution of result ```{r} #plot p-value distribution of result pval <- getPval(res.esetlist, test.method="gene") ``` #### 5.6 obtain geneset description from object ```{r} #obtain geneset description from object description <- getPathDescription(res.esetlist, biocarta) head(description) ```