xEnricherYours | R Documentation

## Function to conduct enrichment analysis given YOUR own input data

### Description

`xEnricherYours` is supposed to conduct enrichment analysis given the input data and the ontology in query. It returns an object of class "eTerm". Enrichment analysis is based on either Fisher's exact test or Hypergeometric test.

### Usage

    xEnricherYours(data.file, annotation.file, background.file = NULL,
    size.range = c(10, 2000), min.overlap = 5, 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"), verbose = T,
    silent = FALSE)

### Arguments

`data.file` | an input data file, containing a list of entities (e.g. genes or SNPs) to test. The entities can be anything, for example, in this file http://dcgor.r-forge.r-project.org/data/InterPro/InterPro.txt, the entities are InterPro domains (InterPro). As seen in this example, entries in the first column must be domains. If the file also contains other columns, these additional columns will be ignored. Alternatively, the data.file can be a matrix or data frame, assuming that input file has been read. Note: the file should use the tab delimiter as the field separator between columns
`annotation.file` | an input annotation file containing annotations between entities and ontology terms. For example, a file containing annotations between InterPro domains and GO Molecular Function (GOMF) terms can be found in http://dcgor.r-forge.r-project.org/data/InterPro/Domain2GOMF.txt. As seen in this example, the input file must contain two columns: 1st column for domains, 2nd column for ontology terms. If there are additional columns, these columns will be ignored. Alternatively, the annotation.file can be a matrix or data frame, 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 entities as the test background. The file format is the same as 'data.file'. By default, it is NULL meaning all annotatable entities (i.g. those entities in 'annotation.file') are used as background
`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 (covered by 'annotation.file'). By default, it is NULL: if the background not provided, it will be TRUE; otherwise FALSE. Surely, it can be explicitly stated. Notably, if only one annotation is provided in 'annotation.file', it should be false (also the background.file should be provided)
`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

### 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

- `or`: a vector containing odds ratio

- `CIl`: a vector containing lower bound confidence interval for the odds ratio

- `CIu`: a vector containing upper bound confidence interval for the odds ratio

- `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

None

### See Also

`xEnricher`

### Examples

    ## Not run:
    # Load the library
    library(XGR)
    library(igraph)

    # Enrichment analysis using your own data
    # a) provide your own data (eg InterPro domains and their annotations by GO terms)
    ## All InterPro domains
    input.file <-
    "http://dcgor.r-forge.r-project.org/data/InterPro/InterPro.txt"
    data <- utils::read.delim(input.file, header=F, row.names=NULL,
    stringsAsFactors=F)[,1]
    ## provide the input domains of interest (eg 100 randomly chosen domains)
    data.file <- sample(data, 100)
    ## InterPro domains annotated by GO Molecular Function (GOMF) terms
    annotation.file <-
    "http://dcgor.r-forge.r-project.org/data/InterPro/Domain2GOMF.txt"

    # b) perform enrichment analysis
    eTerm <- xEnricherYours(data.file=data.file,
    annotation.file=annotation.file)

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

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

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

    # Using ImmunoBase SNPs and associations/annotations with disease traits
    ## get ImmunoBase
    RData.location <- "http://galahad.well.ox.ac.uk/bigdata/"
    ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase',
    RData.location=RData.location)
    ## get disease associated variants/SNPs
    variants_list <- lapply(ImmunoBase, function(x)
    cbind(SNP=names(x$variants),
    Disease=rep(x$disease,length(x$variants))))
    ## extract annotations as a data frame: Variant Disease_Name
    annotation.file <- do.call(rbind, variants_list)
    head(annotation.file)
    ## provide the input SNPs of interest
    ## for example, cis-eQTLs induced by interferon gamma
    cis <- xRDataLoader(RData.customised='JKscience_TS2A',
    RData.location=RData.location)
    data.file <- matrix(cis[which(cis$IFN_t>0),c('variant')], ncol=1)
    # perform enrichment analysis
    eTerm <- xEnricherYours(data.file=data.file,
    annotation.file=annotation.file)
    # view enrichment results for the top significant terms
    xEnrichViewer(eTerm)
    # barplot of significant enrichment results
    bp <- xEnrichBarplot(eTerm, top_num="auto", displayBy="adjp")
    print(bp)

    ## End(Not run)