epigenomix (version 1.12.0)

normalizeChIP: Normalization of ChIP-seq count data. (deprecated)

Description

This method is deprecated. Use normalize instead. This method implements some normalization approaches for ChIP-seq data after counting reads within regions or bins. Similar methods are often applied to RNA-seq data after counting reads within genes.

Usage

normalizeChIP(object, method)

Arguments

object

A ChIPseqSet object as generated by summarizeReads

method

Normalization method, either "scaleTotal", "scaleRegion", "scaleMedianRegion" or "quantile"

Value

• An ChIPseqSet-class object with normalized ChIP-seq values.

Details

The following normalization methods are implemented:

1. scaleTotal

{Samples are scaled by a factor such that all samples have the same number of reads (the median number of reads observed accross all samples before normalization). All reads are used for calculating the scaling factor.} scaleRegion{Samples are scaled by a factor such that all samples have the same number of reads (the median number of reads observed accross all samples before normalization). In contrast to scaleTotal, only reads falling into the regions (genes, promoters) that were used to create the given ChIPseqSet object are used for calculating the scaling factor. Hence, the sum of all columns of the returned ChIPseqSet are equal after applying this method.} scaleMedianRegion{The scaling factor $s_j$ for the $j$-th sample is defined as: $$s_j = median_i \frac{k_{ij}}{\prod_{v=1}^m k_{iv}}$$ $k_{ij}$ is the value of region $i$ in sample $j$. See Anders and Huber (2010) for details.} quantile{Quantile normalization is applied to the ChIP-seq values such that each sample has the same cdf after normalization.}

References

Anders and Huber; Differential expression analysis for sequence count data; Genome Biology 2010, 11:R106

See Also

summarizeReads

Examples

chip <- matrix(c(5,6,5,6,10,12,10,12), nrow=4,
                 dimnames=list(c("f1", "f2", "f3", "f4"), c("s1", "s2")))
  rowRanges <- GRanges(IRanges(start=c(10, 20, 30, 40), end=c(11, 21, 31, 41)),
                     seqnames=c("1", "1", "1", "1"))
  names(rowRanges) = rownames(chip)
  chipDf <- DataFrame(totalCount=c(100, 100),
                      row.names=colnames(chip))
  cSet <- ChIPseqSet(chipVals=chip, rowRanges=rowRanges, colData=chipDf)

  chipVals(cSet)
  chipVals(normalize(cSet, method="scaleMedianRegion"))
  chipVals(normalize(cSet, method="quantile"))