annotatePeakInBatch {ChIPpeakAnno} | R Documentation

## Obtain the distance to the nearest TSS, miRNA, and/or exon for a list of peaks

### Description

Obtain the distance to the nearest TSS, miRNA, exon et al for a list of peak locations leveraging IRanges and biomaRt package

### Usage

    annotatePeakInBatch(
      myPeakList,
      mart,
      featureType = c("TSS", "miRNA", "Exon"),
      AnnotationData,
      output = c("nearestLocation", "overlapping", "both", "shortestDistance", "inside",
        "upstream&inside", "inside&downstream", "upstream", "downstream",
        "upstreamORdownstream", "nearestBiDirectionalPromoters"),
      multiple = c(TRUE, FALSE),
      maxgap = -1L,
      PeakLocForDistance = c("start", "middle", "end", "endMinusStart"),
      FeatureLocForDistance = c("TSS", "middle", "start", "end", "geneEnd"),
      select = c("all", "first", "last", "arbitrary"),
      ignore.strand = TRUE,
      bindingRegion = NULL,
      ...
    )

### Arguments

`myPeakList` | A GRanges object
`mart` | A mart object, used if AnnotationData is not supplied, see useMart of bioMaRt package for details
`featureType` | A charcter vector used with mart argument if AnnotationData is not supplied; it's value is "TSS"", "miRNA"" or "Exon"
`AnnotationData` | A GRanges or annoGR oject. It can be obtained from function getAnnotation or customized annotation of class GRanges containing additional variable: strand (1 or + for plus strand and -1 or - for minus strand). Pre-compliled annotations, such as TSS.human.NCBI36, TSS.mouse.NCBIM37, TSS.rat.RGSC3.4 and TSS.zebrafish.Zv8, are provided by this package (attach them with data() function). Another method to provide annotation data is to obtain through biomaRt real time by using the parameters of mart and featureType
`output` | nearestLocation (default) will output the nearest features calculated as PeakLoc - FeatureLocForDistance overlapping will output overlapping features with maximum gap specified as maxgap between peak range and feature range shortestDistance will output nearest features both will output all the nearest features, in addition, will output any features that overlap the peak that is not the nearest features upstream&inside will output all upstream and overlapping features with maximum gap inside&downstream will output all downstream and overlapping features with maximum gap upstream will output all upstream features with maximum gap. downstream will output all downstream features with maximum gap. upstreamORdownstream will output all upstream features with maximum gap or downstream with maximum gap nearestBiDirectionalPromoters will use annoPeaks to annotate peaks. Nearest promoters from both direction of the peaks (strand is considered). It will report bidirectional promoters if there are promoters in both directions in the given region (defined by bindingRegion). Otherwise, it will report the closest promoter in one direction.
`multiple` | Not applicable when output is nearest. TRUE: output multiple overlapping features for each peak. FALSE: output at most one overlapping feature for each peak. This parameter is kept for backward compatibility, please use select.
`maxgap` | The maximum gap that is allowed between 2 ranges for the ranges to be considered as overlapping. The gap between 2 ranges is the number of positions that separate them. The gap between 2 adjacent ranges is 0. By convention when one range has its start or end strictly inside the other (i.e. non-disjoint ranges), the gap is considered to be -1.
`PeakLocForDistance` | Specify the location of peak for calculating distance,i.e., middle means using middle of the peak to calculate distance to feature, start means using start of the peak to calculate the distance to feature, endMinusStart means using the end of the peak to calculate the distance to features on plus strand and the start of the peak to calculate the distance to features on minus strand. To be compatible with previous version, by default using start
`FeatureLocForDistance` | Specify the location of feature for calculating distance,i.e., middle means using middle of the feature to calculate distance of peak to feature, start means using start of the feature to calculate the distance to feature, TSS means using start of feature when feature is on plus strand and using end of feature when feature is on minus strand, geneEnd means using end of feature when feature is on plus strand and using start of feature when feature is on minus strand. To be compatible with previous version, by default using TSS
`select` | "all" may return multiple overlapping peaks, "first" will return the first overlapping peak, "last" will return the last overlapping peak and "arbitrary" will return one of the overlapping peaks.
`ignore.strand` | When set to TRUE, the strand information is ignored in the annotation. Unless you have stranded peaks and you are interested in annotating peaks to the features in the same strand only, you should just use the default setting ignore.strand = TRUE.
`bindingRegion` | Annotation range used for annoPeaks, which is a vector with two integer values, default to c (-5000, 5000). The first one must be no bigger than 0. And the sec ond one must be no less than 1. Once bindingRegion is defined, annotation will based on annoPeaks. Here is how to use it together with the parameter output and FeatureLocForDistance. To obtain peaks with nearest bi-directional promoters within 5kb upstream and 3kb downstream of TSS, set output = "nearestBiDirectionalPromoters" and bindingRegion = c(-5000, 3000) To obtain peaks within 5kb upstream and up to 3kb downstream of TSS within the gene body, set output="overlapping", FeatureLocForDistance="TSS" and bindingRegion = c(-5000, 3000) To obtain peaks up to 5kb upstream within the gene body and 3kb downstream of gene/Exon End, set output="overlapping", FeatureLocForDistance="geneEnd" and bindingRegion = c(-5000, 3000) To obtain peaks from 5kb upstream to 3kb downstream of genes/Exons, set output="overlapping", bindingType = "fullRange" and bindingRegion = c(-5000, 3000) For details, see annoPeaks.
`...` | Parameters could be passed to annoPeaks

### Value

An object of GRanges with slot start holding the start position of the peak, slot end holding the end position of the peak, slot space holding the chromosome location where the peak is located, slot rownames holding the id of the peak. In addition, the following variables are included.

`list("feature")` | id of the feature such as ensembl gene ID
`list("insideFeature")` | upstream: peak resides upstream of the feature; downstream: peak resides downstream of the feature; inside: peak resides inside the feature; overlapStart: peak overlaps with the start of the feature; overlapEnd: peak overlaps with the end of the feature; includeFeature: peak include the feature entirely
`list("distancetoFeature")` | distance to the nearest feature such as transcription start site. By default, the distance is calculated as the distance between the start of the binding site and the TSS that is the gene start for genes located on the forward strand and the gene end for genes located on the reverse strand. The user can specify the location of peak and location of feature for calculating this
`list("start_position")` | start position of the feature such as gene
`list("end_position")` | end position of the feature such as the gene
`list("strand")` | 1 or + for positive strand and -1 or - for negative strand where the feature is located
`list("shortestDistance")` | The shortest distance from either end of peak to either end the feature.
`list("fromOverlappingOrNearest")` | nearest: indicates this feature's start (feature's end for features at minus strand) is closest to the peak start; Overlapping: indicates this feature overlaps with this peak although it is not the nearest feature start

### Author(s)

Lihua Julie Zhu, Jianhong Ou

### References

1. Zhu L.J. et al. (2010) ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 2010, 11:237doi:10.1186/1471-2105-11-237

2. Zhu L (2013). "Integrative analysis of ChIP-chip and ChIP-seq dataset." In Lee T and Luk ACS (eds.), Tilling Arrays, volume 1067, chapter 4, pp. -19. Humana Press. http://dx.doi.org/10.1007/978-1-62703-607-8_8

### See Also

getAnnotation, findOverlappingPeaks, makeVennDiagram, addGeneIDs, peaksNearBDP, summarizePatternInPeaks, annoGR, annoPeaks

### Examples

        ## example 1: annotate myPeakList by TxDb or EnsDb.
        data(myPeakList)
        library(ensembldb)
        library(EnsDb.Hsapiens.v75)
        annoData <- annoGR(EnsDb.Hsapiens.v75)
        annotatePeak = annotatePeakInBatch(myPeakList[1:6], AnnotationData=annoData)
        annotatePeak

        ## example 2: annotate myPeakList (GRanges)
        ## with TSS.human.NCBI36 (Granges)
        data(TSS.human.NCBI36)
        annotatedPeak = annotatePeakInBatch(myPeakList[1:6],
                                            AnnotationData=TSS.human.NCBI36)
        annotatedPeak

        ## example 3: you have a list of transcription factor biding sites from
        ## literature and are interested in determining the extent of the overlap
        ## to the list of peaks from your experiment. Prior calling the function
        ## annotatePeakInBatch, need to represent both dataset as GRanges
        ## where start is the start of the binding site, end is the end of the
        ## binding site, names is the name of the binding site, space and strand
        ## are the chromosome name and strand where the binding site is located.

        myexp <- GRanges(seqnames=c(6,6,6,6,5,4,4),
                         IRanges(start=c(1543200,1557200,1563000,1569800,
                                         167889600,100,1000),
                                 end=c(1555199,1560599,1565199,1573799,
                                       167893599,200,1200),
                                 names=c("p1","p2","p3","p4","p5","p6", "p7")),
                         strand="+")
        literature <- GRanges(seqnames=c(6,6,6,6,5,4,4),
                              IRanges(start=c(1549800,1554400,1565000,1569400,
                                              167888600,120,800),
                                      end=c(1550599,1560799,1565399,1571199,
                                            167888999,140,1400),
                                      names=c("f1","f2","f3","f4","f5","f6","f7")),
                              strand=rep(c("+", "-"), c(5, 2)))
        annotatedPeak1 <- annotatePeakInBatch(myexp,
                                              AnnotationData=literature)
        pie(table(annotatedPeak1$insideFeature))
        annotatedPeak1
        ### use toGRanges or rtracklayer::import to convert BED or GFF format
        ###  to GRanges before calling annotatePeakInBatch
        test.bed <- data.frame(space=c("4", "6"),
                               start=c("100", "1000"),
                               end=c("200", "1100"),
                               name=c("peak1", "peak2"))
        test.GR = toGRanges(test.bed)
        annotatePeakInBatch(test.GR, AnnotationData = literature)

     library(testthat)
      peak <- GRanges(seqnames = "chr1",
                      IRanges(start = 24736757, end=24737528,
                              names = "testPeak"))
      data(TSS.human.GRCh37)
      TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001461"]
      # GRanges object with 1 range and 1 metadata column:
      # seqnames            ranges strand |            description
      #<Rle>         <IRanges>  <Rle> |            <character>
      # ENSG00000001461        1 24742285-24799466      + | NIPA-like domain con..
      peak
      #GRanges object with 1 range and 0 metadata columns:
      #   seqnames            ranges strand
      #<Rle>         <IRanges>  <Rle>
      #  testPeak     chr1 24736757-24737528      *
      TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001460"]
      #GRanges object with 1 range and 1 metadata column:
      #   seqnames            ranges strand |            description
      #<Rle>         <IRanges>  <Rle> |            <character>
      #   ENSG00000001460        1 24683490-24743424      - | UPF0490 protein C1or..
      ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
                                PeakLocForDistance = "start")
      stopifnot(ap$feature=="ENSG00000001461")
      ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
                                PeakLocForDistance = "end")
      stopifnot(ap$feature=="ENSG00000001461")
      ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
                                PeakLocForDistance = "middle")
      stopifnot(ap$feature=="ENSG00000001461")
      ap <- annotatePeakInBatch(peak, Annotation=TSS.human.GRCh37,
                                PeakLocForDistance = "endMinusStart")
      stopifnot(ap$feature=="ENSG00000001461")
      ## Let's calculate the distances between the peak and the TSS of the genes
      ## in the annotation file used for annotating the peaks.
      ## Please note that we need to compute the distance using the annotation
      ## file TSS.human.GRCh37.
      ## If you would like to use  TxDb.Hsapiens.UCSC.hg19.knownGene,
      ## then you will need to annotate the peaks
      ## using TxDb.Hsapiens.UCSC.hg19.knownGene as well.
      ### using start
      start(peak) -start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                          "ENSG00000001461"]) #picked
      #[1] -5528
      start(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                       "ENSG00000001460"])
      #[1] -6667
      #### using middle
      (start(peak) + end(peak))/2 -
          start(TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001461"])
      #[1] -5142.5
      (start(peak) + end(peak))/2 -
          end(TSS.human.GRCh37[names(TSS.human.GRCh37)== "ENSG00000001460"])
      # [1] 49480566
      end(peak) -start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                       "ENSG00000001461"]) #picked
      # [1] -4757
      end(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                     "ENSG00000001460"])
      # [1] -5896
      #### using endMinusStart
      end(peak) - start(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                        "ENSG00000001461"]) ## picked
      # [1] -4575
      start(peak) -end(TSS.human.GRCh37[names(TSS.human.GRCh37)==
                                        "ENSG00000001460"])
      #[1] -6667
      ###### using txdb object to annotate the peaks
      library(org.Hs.eg.db)
      select(org.Hs.eg.db, key="STPG1", keytype="SYMBOL",
             columns=c("ENSEMBL", "ENTREZID", "SYMBOL"))
      #  SYMBOL         ENSEMBL ENTREZID
      #  STPG1 ENSG00000001460    90529
      select(org.Hs.eg.db, key= "ENSG00000001461", keytype="ENSEMBL",
             columns=c("ENSEMBL", "ENTREZID", "SYMBOL"))
      #ENSEMBL ENTREZID SYMBOL
      # ENSG00000001461    57185 NIPAL3
      require(TxDb.Hsapiens.UCSC.hg19.knownGene)
      txdb.ann <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene)
      STPG1 <- select(org.Hs.eg.db, key="STPG1", keytype="SYMBOL",
                      columns=c( "SYMBOL", "ENSEMBL", "ENTREZID"))[1,3]
      NIPAL3 <- select(org.Hs.eg.db, key="NIPAL3", keytype="SYMBOL",
                       columns=c( "SYMBOL", "ENSEMBL", "ENTREZID"))[1,3]
      ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
                                PeakLocForDistance = "start")
      expect_equal(ap$feature, STPG1)
      ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
                                PeakLocForDistance = "end")
      expect_equal(ap$feature, STPG1)
      ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
                                PeakLocForDistance = "middle")
      expect_equal(ap$feature, STPG1)
      ap <- annotatePeakInBatch(peak, Annotation=txdb.ann,
                                PeakLocForDistance = "endMinusStart")
      expect_equal(ap$feature, NIPAL3)
      txdb.ann[NIPAL3]
      txdb.ann[txdb.ann$gene_id == NIPAL3]
      #  GRanges object with 1 range and 1 metadata column:
      #    seqnames            ranges strand |     gene_id
      #  <Rle>         <IRanges>  <Rle> | <character>
      #   57185     chr1 24742245-24799473      + |       57185
      #-------
      txdb.ann[txdb.ann$gene_id == STPG1]
      #   GRanges object with 1 range and 1 metadata column:
      #     seqnames            ranges strand |     gene_id
      #  <Rle>         <IRanges>  <Rle> | <character>
      #     90529     chr1 24683489-24741587      - |       90529

[Package ChIPpeakAnno version 3.26.4 Index]