normalize {epigenomix} | R Documentation

## Normalization of ChIP-seq and other count data

### Description

This function implements some methods for between-sample normalization of count data. Although these methods were developed for RNA-seq data, they are also useful for ChIP-seq data normalization after reads were counted within regions or bins. Some methods may also be applied to count data after within-sample normalization (e.g. TPM or RPKM values).

### Usage

    ## S4 method for signature 'ChIPseqSet'
    normalize(object, method, isLogScale = FALSE, trim = 0.3, totalCounts)
    ## S4 method for signature 'ExpressionSet'
    normalize(object, method, isLogScale = FALSE, trim = 0.3, totalCounts)

### Arguments

`object` | An object of class `ChIPseqSet` or `ExpressionSet` that contains the raw data.
`method` | Normalization method, either "scale", "scaleMedianRegion", "quantile" or "tmm".
`isLogScale` | Indicates whether the raw data in `object` is already logarithmized. Default value is `FALSE`. Logarithmized data will be returned on the log scale, non logarithmized data will remain on its original scale.
`trim` | Only used if `method` is "tmm". Indicates the fraction of data points that should be trimmed before calculating the mean. Default value is 0.3.
`totalCounts` | Only used if `method` is "scale". A vector giving the total number of reads for each sample. The Vector's length must equal the number of samples in `object`. Default values are the sums over all features for each sample (i.e. colsums of `object`).

### Details

The following normalization methods are implemented:

1. scaleSamples are scaled by a factor such that all samples have the same number N of reads after normalization, where N is the median number of reads observed accross all samples. If the argument `totalCounts` is missing, the total numbers of reads are calculated from the given data. Otherwise, the values in `totalCounts` are used.

2. scaleMedianRegionThe scaling factor s_j for the j-th sample is defined as

   s_j = median_i \frac{k_{ij}}{∏_{v=1}^m k_{iv}}.

   k_{ij} is the value of region i in sample j. See Anders and Huber (2010) for details.

3. quantileQuantile normalization is applied to the ChIP-seq values such that each sample has the same cdf after normalization.

4. tmmThe trimmed mean M-value (tmm) normalization was proposed by Robinson and Oshlack (2010). Here, the logarithm of the scaling factor for sample i is calculated as the trimmed mean of

   \log(k_{i,j}/m_{j}).

   Variable m_{j} denotes the geometric mean of region j. Argument `trim` is set to 0.3 as default value, so that the smallest 15% and the largest 15% of the log ratios are trimmed before calculating the mean.

### Value

An object of the same class as the input `object` with the normalized data.

### Author(s)

Hans-Ulrich Klein (hklein@broadinstitute.org)

### References

Anders and Huber. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106.\ Robinson and Oshlack. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25

### Examples

      set.seed(1234)
      chip <- matrix(c(rpois(20, lambda=10), rpois(20, lambda=20)), nrow=20,
                     dimnames=list(paste("feature", 1:20, sep=""), c("sample1", "sample2")))
      rowRanges <- GRanges(IRanges(start=1:20, end=1:20),
                         seqnames=c(rep("1", 20)))
      names(rowRanges) = rownames(chip)
      cSet <- ChIPseqSet(chipVals=chip, rowRanges=rowRanges)

      tmmSet <- normalize(cSet, method="tmm", trim=0.3)
      mean(log(chipVals(tmmSet))[, 1], trim=0.3) -
          mean(log(chipVals(tmmSet))[, 2], trim=0.3) < 0.01

      quantSet <- normalize(cSet, method="quantile")
      all(quantile(chipVals(quantSet)[, 1]) == quantile(chipVals(quantSet)[, 2]))

[Package epigenomix version 1.32.0 Index]