xA2EnricherRegions: Function to conduct enrichment analysis given a list of...
In A2: Atlas and Analysis of Systems-Biology-Led Pathways

Description Usage Arguments Value Note See Also Examples

xA2EnricherRegions is supposed to conduct enrichment analysis given a list of genomic region sets and a list of pathway sets. It returns an object of the class 'ls_eTerm'.

xA2EnricherRegions(list_vec, background = NULL, build.conversion =
c(NA,
"hg38.to.hg19", "hg18.to.hg19"), crosslink = c("genehancer",
"PCHiC_combined", "GTEx_V6p_combined", "nearby"),
crosslink.customised = NULL, crosslink.top = NULL,
nearby.distance.max = 50000, nearby.decay.kernel = c("rapid", "slow",
"linear", "constant"), nearby.decay.exponent = 2, ontologies =
c("KEGG",
"KEGGmetabolism", "KEGGgenetic", "KEGGenvironmental", "KEGGcellular",
"KEGGorganismal", "KEGGdisease", "REACTOME", "REACTOME_ImmuneSystem",
"REACTOME_SignalTransduction", "AA"), size.range = c(10, 2000),
min.overlap = 3, test = c("hypergeo", "fisher", "binomial"),
background.annotatable.only = NULL, p.tail = c("one-tail",
"two-tails"),
p.adjust.method = c("BH", "BY", "bonferroni", "holm", "hochberg",
"hommel"),
verbose = TRUE, silent = FALSE, plot = TRUE, fdr.cutoff = 0.05,
displayBy = c("zscore", "fdr", "pvalue", "fc", "or"),
RData.location = "http://galahad.well.ox.ac.uk/bigdata")

`list_vec`	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
`background`	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
`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)
`crosslink`	the built-in crosslink info with a score quantifying the link of a GR to a gene. It can be one of 'genehancer' (enhancer genes; PMID:28605766), 'nearby' (nearby genes; if so, please also specify the relevant parameters 'nearby.distance.max', 'nearby.decay.kernel' and 'nearby.decay.exponent' below), 'PCHiC_combined' (conformation genes; PMID:27863249), 'GTEx_V6p_combined' (eQTL genes; PMID:29022597)
`crosslink.customised`	the crosslink info with a score quantifying the link of a GR to a gene. A user-input matrix or data frame with 4 columns: 1st column for genomic regions (formatted as "chr:start-end", genome build 19), 2nd column for Genes, 3rd for crosslink score (crosslinking a genomic region to a gene, such as -log10 significance level), and 4th for contexts (optional; if not provided, it will be added as 'C'). Alternatively, it can be a file containing these 4 columns. Required, otherwise it will return NULL
`crosslink.top`	the number of the top genes defined by 'data' will be used for test. By default, it is NULL
`nearby.distance.max`	the maximum distance between genes and GR. Only those genes no far way from this distance will be considered as seed genes. This parameter will influence the distance-component weights calculated for nearby GR per gene
`nearby.decay.kernel`	a character specifying a decay kernel function. It can be one of 'slow' for slow decay, 'linear' for linear decay, and 'rapid' for rapid decay. If no distance weight is used, please select 'constant'
`nearby.decay.exponent`	a numeric specifying a decay exponent. By default, it sets to 2
`ontologies`	the ontologies supported currently. It can be 'AA' for AA-curated pathways, KEGG pathways (including 'KEGG' for all, 'KEGGmetabolism' for 'Metabolism' pathways, 'KEGGgenetic' for 'Genetic Information Processing' pathways, 'KEGGenvironmental' for 'Environmental Information Processing' pathways, 'KEGGcellular' for 'Cellular Processes' pathways, 'KEGGorganismal' for 'Organismal Systems' pathways, and 'KEGGdisease' for 'Human Diseases' pathways), 'REACTOME' for REACTOME pathways or 'REACTOME_x' for its sub-ontologies (where x can be 'CellCellCommunication', 'CellCycle', 'CellularResponsesToExternalStimuli', 'ChromatinOrganization', 'CircadianClock', 'DevelopmentalBiology', 'DigestionAndAbsorption', 'Disease', 'DNARepair', 'DNAReplication', 'ExtracellularMatrixOrganization', 'GeneExpression(Transcription)', 'Hemostasis', 'ImmuneSystem', 'Metabolism', 'MetabolismOfProteins', 'MetabolismOfRNA', 'Mitophagy', 'MuscleContraction', 'NeuronalSystem', 'OrganelleBiogenesisAndMaintenance', 'ProgrammedCellDeath', 'Reproduction', 'SignalTransduction', 'TransportOfSmallMolecules', 'VesicleMediatedTransport')
`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
`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
`verbose`	logical to indicate whether the messages will be displayed in the screen. By default, it sets to false for no display
`silent`	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
`plot`	logical to indicate whether heatmap plot is drawn
`fdr.cutoff`	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
`displayBy`	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
`RData.location`	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

none

xA2EnricherRegions

# Load the library
library(A2)
RData.location <- "http://galahad.well.ox.ac.uk/bigdata_dev/"


# 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)


## Not run: 
# b) perform enrichment analysis
ls_eTerm <- xA2EnricherRegions(data, crosslink="genehancer",
ontologies=c("REACTOME_ImmuneSystem","REACTOME_SignalTransduction"),
RData.location=RData.location)
ls_eTerm

## forest plot of enrichment results
gp <- xEnrichForest(ls_eTerm, top_num=10, signature=FALSE)

## End(Not run)