xGRviaGeneAnnoAdv is supposed to conduct enrichment analysis
given a list of gene sets and a list of ontologies. It is an advanced
version of xGRviaGeneAnno, returning an object of the class
'ls_eTerm'.
xGRviaGeneAnnoAdv(list_vec, background = NULL, build.conversion = c(NA,
"hg38.to.hg19", "hg18.to.hg19"), gap.max = 0,
GR.Gene = c("UCSC_knownGene", "UCSC_knownCanonical"),
ontologies = 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, silent = FALSE, plot = TRUE,
fdr.cutoff = 0.05, displayBy = c("zscore", "fdr", "pvalue", "fc",
"or"), RData.location = "http://galahad.well.ox.ac.uk/bigdata")an input vector containing genomic regions. Alternatively it can be a list of vectors, representing multiple groups of genomic regions. Formatted as "chr:start-end" are genomic regions
a background vector containing genomic regions (formatted as "chr:start-end") as the test background. If NULL, by default all annotatable are used as background
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)
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
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"
the ontologies supported currently. By default, it is
'NA' to disable this option. Pre-built ontology and annotation data are
detailed in xDefineOntology.
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
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 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
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
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
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)
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
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
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)
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)
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)
logical to indicate whether the true-path rule should be applied to propagate annotations. By default, it sets to false
logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display
logical to indicate whether the messages will be silent completely. By default, it sets to false. If true, verbose will be forced to be false
logical to indicate whether heatmap plot is drawn
fdr cutoff used to declare the significant terms. By default, it is set to 0.05. This option only works when setting plot (see above) is TRUE
which statistics will be used for drawing heatmap. It can be "fc" for enrichment fold change, "fdr" for adjusted p value (or FDR), "pvalue" for p value, "zscore" for enrichment z-score (by default), "or" for odds ratio. This option only works when setting plot (see above) is TRUE
the characters to tell the location of built-in
RData files. See xRDataLoader for details
an object of class "ls_eTerm", a list with following components:
df: a data frame of n x 12, where the 12 columns are
"group" (the input group names), "ontology" (input ontologies), "id"
(term ID), "name" (term name), "nAnno" (number in members annotated by
a term), "nOverlap" (number in overlaps), "fc" (enrichment fold
changes), "zscore" (enrichment z-score), "pvalue" (nominal p value),
"adjp" (adjusted p value (FDR)), "or" (odds ratio), "CIl" (lower bound
confidence interval for the odds ratio), "CIu" (upper bound confidence
interval for the odds ratio), "distance" (term distance or other
information), "members" (members (represented as Gene Symbols) in
overlaps)
mat: NULL if the plot is not drawn; otherwise, a matrix of
term names X groups with numeric values for the signficant enrichment,
NA for the insignificant ones
gp: NULL if the plot is not drawn; otherwise, a 'ggplot'
object
# NOT RUN {
# Load the library
library(XGR)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata/"
# Enrichment analysis for GWAS SNPs from ImmunoBase
## a) provide input data (bed-formatted)
data.file <- "http://galahad.well.ox.ac.uk/bigdata/ImmunoBase_GWAS.bed"
input <- read.delim(file=data.file, header=T, stringsAsFactors=F)
data <- paste0(input$chrom, ':', (input$chromStart+1), '-',
input$chromEnd)
# b) perform enrichment analysis
## overlap with gene body
ls_eTerm <- xGRviaGeneAnnoAdv(data, gap.max=0,
ontologies=c("REACTOME_ImmuneSystem","REACTOME_SignalTransduction"),
RData.location=RData.location)
ls_eTerm
## forest plot of enrichment results
gp <- xEnrichForest(ls_eTerm, top_num=10, CI.one=F)
gp
# }
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