xGRviaGenomicAnno | R Documentation

## Function to conduct region-based enrichment analysis using genomic annotations via binomial test

### Description

`xGRviaGenomicAnno` is supposed to conduct region-based enrichment analysis for the input genomic region data (genome build h19), using genomic annotations (eg active chromatin, transcription factor binding sites/motifs, conserved sites). Enrichment analysis is based on binomial test for estimating the significance of overlaps either at the base resolution, at the region resolution or at the hybrid resolution. Test background can be provided; by default, the annotatable will be used.

### Usage

    xGRviaGenomicAnno(data.file, annotation.file = NULL,
    background.file = NULL, format.file = c("data.frame", "bed",
    "chr:start-end", "GRanges"), build.conversion = c(NA, "hg38.to.hg19",
    "hg18.to.hg19"), resolution = c("bases", "regions", "hybrid"),
    background.annotatable.only = T, p.tail = c("one-tail", "two-tails"),
    p.adjust.method = c("BH", "BY", "bonferroni", "holm", "hochberg",
    "hommel"), GR.annotation = NA, 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.
`annotation.file` | an input annotation file containing genomic annotations for genomic regions. If the input file is formatted as a 'data.frame', the first four columns correspond to the chromosome (1st column), the starting chromosome position (2nd column), the ending chromosome position (3rd column), and the genomic annotations (eg transcription factors and histones; 4th column). If the format is indicated as 'bed', the same as 'data.frame' format but the position is 0-based offset from chromomose position. If the format is indicated as "chr:start-end", the first two columns correspond to the chromosome:start-end (1st column) and the genomic annotations (eg transcription factors and histones; 2nd column). 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" and "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)
`resolution` | the resolution of overlaps being tested. It can be one of "bases" at the base resolution (by default), "regions" at the region resolution, and "hybrid" at the base-region hybrid resolution (that is, data at the region resolution but annotation/background at the base resolution). If regions being analysed are SNPs themselves, then the results are the same even when choosing this parameter as either 'bases' or 'hybrid' or 'regions'
`background.annotatable.only` | logical to indicate whether the background is further restricted to annotatable bases (covered by 'annotation.file'). In other words, if the background is provided, the background bases are those after being overlapped with annotatable bases. Notably, if only one annotation (eg only a transcription factor) is provided in 'annotation.file', it should be false
`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
`GR.annotation` | the genomic regions of annotation data. By default, it is 'NA' to disable this option. Pre-built genomic annotation data are detailed in the section 'Note'. Alternatively, the user can also directly provide a customised GR object (or a list of GR objects)
`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

a data frame with following columns (below explanations are based on results at the 'hybrid' resolution):

- `name`: the annotation name

- `nAnno`: the number of bases covered by that annotation. If the background is provided, they are also restricted by this

- `nOverlap`: the number of regions overlapped between input regions and annotation regions. If the background is provided, they are also restricted by this

- `fc`: fold change

- `zscore`: z-score

- `pvalue`: p-value

- `adjp`: adjusted p-value. 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

- `expProb`: the probability of expecting bases overlapped between background regions and annotation regions

- `obsProb`: the probability of observing regions overlapped between input regions and annotation regions

### Note

Pre-built genomic annotation data are detailed in `xDefineGenomicAnno`.

### See Also

`xDefineGenomicAnno`

### Examples

    # Load the XGR package and specify the location of built-in data
    library(XGR)
    RData.location <- "http://galahad.well.ox.ac.uk/bigdata"

    ## Not run:
    # 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 enrichment analysis using FANTOM expressed enhancers
    ### one-tail p-value calculation (by default)
    eTerm <- xGRviaGenomicAnno(data.file, format.file="bed",
    GR.annotation="FANTOM5_Enhancer_Cell", RData.location=RData.location)
    ### alternatively: two-tails p-value calculation (useful to identify depletions)
    eTerm_2 <- xGRviaGenomicAnno(data.file, format.file="bed",
    GR.annotation="FANTOM5_Enhancer_Cell", p.tail="two-tails",
    RData.location=RData.location)

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

    ## d) barplot of enriched terms
    bp <- xEnrichBarplot(eTerm, top_num='auto', displayBy="fc")
    bp

    ## e) forest plot of enriched terms
    gp <- xEnrichForest(eTerm)
    gp

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

    ##########################################
    ### Advanced use: customised GR.annotation
    ##########################################
    FANTOM5_CAT_Cell <- xRDataLoader('FANTOM5_CAT_Cell',
    RData.location=RData.location)
    ls_gr_lncRNA <- lapply(FANTOM5_CAT_Cell, function(x)
    x[grep('lncRNA',x$Category)])
    ls_gr_mRNA <- lapply(FANTOM5_CAT_Cell, function(x)
    x[grep('coding_mRNA',x$Category)])
    GR.annotations <- c("ls_gr_lncRNA","ls_gr_mRNA","FANTOM5_CAT_Cell")
    ls_df <- lapply(1:length(GR.annotations), function(i){
    GR.annotation <- get(GR.annotations[i])
    df <- xGRviaGenomicAnno(data.file=data.file, format.file="bed",
    GR.annotation=GR.annotation, RData.location=RData.location)
    df$group <- GR.annotations[i]
    return(df)
    })
    df <- do.call(rbind, ls_df)
    gp <- xEnrichHeatmap(df, fdr.cutoff=0.05, displayBy="zscore")

    ##########################################
    ### Advanced use: customised EpigenomeAtlas_15Segments
    ##########################################
    info <- xRDataLoader('EpigenomeAtlas_15Segments_info',
    RData.location=RData.location)
    GR.annotations <- paste0('EpigenomeAtlas_15Segments_',names(info))
    names(GR.annotations) <- info
    ls_df <- lapply(1:length(GR.annotations), function(i){
    GR.annotation <- GR.annotations[i]
    message(sprintf("Analysing '%s' (%s) ...", names(GR.annotation),
    as.character(Sys.time())), appendLF=T)
    df <- xGRviaGenomicAnno(data.file=data.file, format.file="bed",
    GR.annotation=GR.annotation, RData.location=RData.location, verbose=F)
    df$group <- names(GR.annotation)
    return(df)
    })
    df <- do.call(rbind, ls_df)
    gp <- xEnrichHeatmap(df, fdr.cutoff=0.05, displayBy="fdr",
    reorder="both")

    ## End(Not run)