xGRviaGeneAnno | R Documentation

## Function to conduct region-based enrichment analysis using nearby gene annotations

### Description

`xGRviaGeneAnno` is supposed to conduct region-based enrichment analysis for the input genomic region data (genome build h19), using nearby gene annotations. To do so, nearby genes are first defined within the maximum gap between genomic regions and gene location. Enrichment analysis is based on either Fisher's exact test or Hypergeometric test for estimating the significance of overlapped nearby genes. Test background can be provided; by default, the annotatable genes will be used.

### Usage

    xGRviaGeneAnno(data.file, background.file = NULL,
    format.file = c("data.frame", "bed", "chr:start-end", "GRanges"),
    build.conversion = c(NA, "hg38.to.hg19", "hg18.to.hg19"), gap.max = 0,
    GR.Gene = c("UCSC_knownGene", "UCSC_knownCanonical"), ontology =
    c("GOBP",
    "GOMF", "GOCC", "PS", "PS2", "SF", "Pfam", "DO", "HPPA", "HPMI",
    "HPCM",
    "HPMA", "MP", "EF", "MsigdbH", "MsigdbC1", "MsigdbC2CGP",
    "MsigdbC2CPall",
    "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA",
    "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM",
    "MsigdbC5BP",
    "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7", "DGIdb", "GTExV4",
    "GTExV6", "CreedsDisease", "CreedsDiseaseUP", "CreedsDiseaseDN",
    "CreedsDrug",
    "CreedsDrugUP", "CreedsDrugDN", "CreedsGene", "CreedsGeneUP",
    "CreedsGeneDN"), size.range = c(10, 2000), min.overlap = 3,
    which.distance = NULL, test = c("hypergeo", "fisher", "binomial"),
    p.adjust.method = c("BH", "BY", "bonferroni", "holm", "hochberg",
    "hommel"),
    ontology.algorithm = c("none", "pc", "elim", "lea"), elim.pvalue =
    0.01,
    lea.depth = 2, path.mode = c("all_paths", "shortest_paths",
    "all_shortest_paths"), true.path.rule = F, verbose = T,
    RData.location = "http://galahad.well.ox.ac.uk/bigdata")

### Arguments

`data.file` | an input data file, containing a list of genomic regions to test. If the input file is formatted as a 'data.frame' (specified by the parameter 'format.file' below), the first three columns correspond to the chromosome (1st column), the starting chromosome position (2nd column), and the ending chromosome position (3rd column). If the format is indicated as 'bed' (browser extensible data), the same as 'data.frame' format but the position is 0-based offset from chromomose position. If the genomic regions provided are not ranged but only the single position, the ending chromosome position (3rd column) is allowed not to be provided. If the format is indicated as "chr:start-end", instead of using the first 3 columns, only the first column will be used and processed. If the file also contains other columns, these additional columns will be ignored. Alternatively, the input file can be the content itself assuming that input file has been read. Note: the file should use the tab delimiter as the field separator between columns
`background.file` | an input background file containing a list of genomic regions as the test background. The file format is the same as 'data.file'. By default, it is NULL meaning all annotatable bases (ig non-redundant bases covered by 'annotation.file') are used as background. However, if only one annotation (eg only a transcription factor) is provided in 'annotation.file', the background must be provided.
`format.file` | the format for input files. It can be one of "data.frame", "chr:start-end", "bed" or "GRanges"
`build.conversion` | the conversion from one genome build to another. The conversions supported are "hg38.to.hg19" and "hg18.to.hg19". By default it is NA (no need to do so)
`gap.max` | the maximum distance to nearby genes. Only those genes no far way from this distance will be considered as nearby genes. By default, it is 0 meaning that nearby genes are those overlapping with genomic regions
`GR.Gene` | the genomic regions of genes. By default, it is 'UCSC_knownGene', that is, UCSC known genes (together with genomic locations) based on human genome assembly hg19. It can be 'UCSC_knownCanonical', that is, UCSC known canonical genes (together with genomic locations) based on human genome assembly hg19. Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to Gene Symbols. Then, tell "GR.Gene" with your RData file name (with or without extension), plus specify your file RData path in "RData.location"
`ontology` | the ontology supported currently. It can be "GOBP" for Gene Ontology Biological Process, "GOMF" for Gene Ontology Molecular Function, "GOCC" for Gene Ontology Cellular Component, "PS" for phylostratific age information, "PS2" for the collapsed PS version (inferred ancestors being collapsed into one with the known taxonomy information), "SF" for SCOP domain superfamilies, "Pfam" for Pfam domain families, "DO" for Disease Ontology, "HPPA" for Human Phenotype Phenotypic Abnormality, "HPMI" for Human Phenotype Mode of Inheritance, "HPCM" for Human Phenotype Clinical Modifier, "HPMA" for Human Phenotype Mortality Aging, "MP" for Mammalian Phenotype, "EF" for Experimental Factor Ontology (used to annotate GWAS Catalog genes), Drug-Gene Interaction database ("DGIdb") for drugable categories, tissue-specific eQTL-containing genes from GTEx ("GTExV4" and "GTExV6"), crowd extracted expression of differential signatures from CREEDS ("CreedsDisease", "CreedsDiseaseUP", "CreedsDiseaseDN", "CreedsDrug", "CreedsDrugUP", "CreedsDrugDN", "CreedsGene", "CreedsGeneUP" and "CreedsGeneDN"), and the molecular signatures database (Msigdb, including "MsigdbH", "MsigdbC1", "MsigdbC2CGP", "MsigdbC2CPall", "MsigdbC2CP", "MsigdbC2KEGG", "MsigdbC2REACTOME", "MsigdbC2BIOCARTA", "MsigdbC3TFT", "MsigdbC3MIR", "MsigdbC4CGN", "MsigdbC4CM", "MsigdbC5BP", "MsigdbC5MF", "MsigdbC5CC", "MsigdbC6", "MsigdbC7")
`size.range` | the minimum and maximum size of members of each term in consideration. By default, it sets to a minimum of 10 but no more than 2000
`min.overlap` | the minimum number of overlaps. Only those terms with members that overlap with input data at least min.overlap (3 by default) will be processed
`which.distance` | which terms with the distance away from the ontology root (if any) is used to restrict terms in consideration. By default, it sets to 'NULL' to consider all distances
`test` | the test statistic used. It can be "fisher" for using fisher's exact test, "hypergeo" for using hypergeometric test, or "binomial" for using binomial test. Fisher's exact test is to test the independence between gene group (genes belonging to a group or not) and gene annotation (genes annotated by a term or not), and thus compare sampling to the left part of background (after sampling without replacement). Hypergeometric test is to sample at random (without replacement) from the background containing annotated and non-annotated genes, and thus compare sampling to background. Unlike hypergeometric test, binomial test is to sample at random (with replacement) from the background with the constant probability. In terms of the ease of finding the significance, they are in order: hypergeometric test > fisher's exact test > binomial test. In other words, in terms of the calculated p-value, hypergeometric test < fisher's exact test < binomial test
`p.adjust.method` | the method used to adjust p-values. It can be one of "BH", "BY", "bonferroni", "holm", "hochberg" and "hommel". The first two methods "BH" (widely used) and "BY" control the false discovery rate (FDR: the expected proportion of false discoveries amongst the rejected hypotheses); the last four methods "bonferroni", "holm", "hochberg" and "hommel" are designed to give strong control of the family-wise error rate (FWER). Notes: FDR is a less stringent condition than FWER
`ontology.algorithm` | the algorithm used to account for the hierarchy of the ontology. It can be one of "none", "pc", "elim" and "lea". For details, please see 'Note' below
`elim.pvalue` | the parameter only used when "ontology.algorithm" is "elim". It is used to control how to declare a signficantly enriched term (and subsequently all genes in this term are eliminated from all its ancestors)
`lea.depth` | the parameter only used when "ontology.algorithm" is "lea". It is used to control how many maximum depth is used to consider the children of a term (and subsequently all genes in these children term are eliminated from the use for the recalculation of the signifance at this term)
`path.mode` | the mode of paths induced by vertices/nodes with input annotation data. It can be "all_paths" for all possible paths to the root, "shortest_paths" for only one path to the root (for each node in query), "all_shortest_paths" for all shortest paths to the root (i.e. for each node, find all shortest paths with the equal lengths)
`true.path.rule` | logical to indicate whether the true-path rule should be applied to propagate annotations. By default, it sets to false
`verbose` | logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display
`RData.location` | the characters to tell the location of built-in RData files. See `xRDataLoader` for details

### Value

an object of class "eTerm", a list with following components:

- `term_info`: a matrix of nTerm X 4 containing snp/gene set information, where nTerm is the number of terms, and the 4 columns are "id" (i.e. "Term ID"), "name" (i.e. "Term Name"), "namespace" and "distance"

- `annotation`: a list of terms containing annotations, each term storing its annotations. Always, terms are identified by "id"

- `g`: an igraph object to represent DAG

- `data`: a vector containing input data in consideration. It is not always the same as the input data as only those mappable are retained

- `background`: a vector containing the background data. It is not always the same as the input data as only those mappable are retained

- `overlap`: a list of overlapped snp/gene sets, each storing snps overlapped between a snp/gene set and the given input data (i.e. the snps of interest). Always, gene sets are identified by "id"

- `fc`: a vector containing fold changes

- `zscore`: a vector containing z-scores

- `pvalue`: a vector containing p-values

- `adjp`: a vector containing adjusted p-values. It is the p value but after being adjusted for multiple comparisons

- `cross`: a matrix of nTerm X nTerm, with an on-diagnal cell for the overlapped-members observed in an individaul term, and off-diagnal cell for the overlapped-members shared betwene two terms

- `call`: the call that produced this result

### Note

The interpretation of the algorithms used to account for the hierarchy of the ontology is:

- "none": does not consider the ontology hierarchy at all.

- "lea": computers the significance of a term in terms of the significance of its children at the maximum depth (e.g. 2). Precisely, once snps are already annotated to any children terms with a more signficance than itself, then all these snps are eliminated from the use for the recalculation of the signifance at that term. The final p-values takes the maximum of the original p-value and the recalculated p-value.

- "elim": computers the significance of a term in terms of the significance of its all children. Precisely, once snps are already annotated to a signficantly enriched term under the cutoff of e.g. pvalue<1e-2, all these snps are eliminated from the ancestors of that term).

- "pc": requires the significance of a term not only using the whole snps as background but also using snps annotated to all its direct parents/ancestors as background. The final p-value takes the maximum of both p-values in these two calculations.

- "Notes": the order of the number of significant terms is: "none" > "lea" > "elim" > "pc".

### See Also

`xEnrichViewer`, `xEnricherGenes`

### Examples

    ## Not run:
    # Load the XGR package and specify the location of built-in data
    library(XGR)
    RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev"

    # Enrichment analysis for GWAS SNPs from ImmunoBase
    ## a) provide input data
    data.file <- "http://galahad.well.ox.ac.uk/bigdata/ImmunoBase_GWAS.bed"

    ## b) perform DO enrichment analysis for nearby genes (with GWAS SNPs)
    eTerm <- xGRviaGeneAnno(data.file=data.file, format.file="bed",
    gap.max=0, ontology="DO", RData.location=RData.location)

    ## c) view enrichment results for the top significant terms
    xEnrichViewer(eTerm)

    ## d) save enrichment results to the file called 'Regions2genes_enrichments.txt'
    output <- xEnrichViewer(eTerm, top_num=length(eTerm$adjp),
    sortBy="adjp", details=TRUE)
    utils::write.table(output, file="Regions2genes_enrichments.txt",
    sep="\t", row.names=FALSE)

    ## e) barplot of significant enrichment results
    bp <- xEnrichBarplot(eTerm, top_num=10, displayBy="fc")
    print(bp)

    ## End(Not run)