xGRviaGeneAnnoR 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 = NA,
size.range = c(10, 2000),
min.overlap = 5,
which.distance = NULL,
test = c("fisher", "hypergeo", "binomial"),
background.annotatable.only = NULL,
p.tail = c("one-tail", "two-tails"),
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",
guid = NULL
)

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 genes are used as background

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. By default, it is 'NA' to disable this option. Pre-built ontology and annotation data are detailed in xDefineOntology.

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

background.annotatable.only

logical to indicate whether the background is further restricted to the annotatable. By default, it is NULL: if ontology.algorithm is not 'none', it is always TRUE; otherwise, it depends on the background (if not provided, it will be TRUE; otherwise FALSE). Surely, it can be explicitly stated

p.tail

the tail used to calculate p-values. It can be either "two-tails" for the significance based on two-tails (ie both over- and under-overrepresentation) or "one-tail" (by default) for the significance based on one tail (ie only over-representation)

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

guid

a valid (5-character) Global Unique IDentifier for an OSF project. See xRDataLoader for details

Value

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

Note

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

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"

# 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)