normOffsets {csaw} | R Documentation

## Normalize trended biases across libraries

### Description

Calculate normalization offsets by performing a loess fit to count data from multiple libraries.

### Usage

    normOffsets(object, ..., assay.id = "counts", se.out = TRUE)

### Arguments

`object` | A SummarizedExperiment object containing a count matrix and library sizes in the `totals` field of the `colData`. Alternatively, a DGEList object containing a count matrix in `object$counts` and library sizes in `object$samples$lib.size`. Alternatively, an ordinary matrix containing counts.
`...` | Other arguments to be passed to `loessFit`.
`assay.id` | An integer scalar or string specifying the assay values to use for normalization.
`se.out` | A logical scalar indicating whether or not a SummarizedExperiment object should be returned. Alternatively, a SummarizedExperiment or DGEList object in which normalization factors are to be stored.

### Details

This function performs non-linear normalization similar to the fast loess algorithm in `normalizeCyclicLoess`. The aims is to account for mean dependencies in the efficiency biases between libraries. For each sample, a lowess curve is fitted to the log-counts against the log-average count. The fitted value for each genomic window is used as an offset in a generalized linear model for that feature and sample. The use of the average count provides more stability than the average log-count when low counts are present for differentially bound regions.

The trend fits are always computed from `object`. However, if `se.out` is a (different) SummarizedExperiment or DGEList object, the trend fits will be used to compute offsets for each entry in `se.out` using spline interpolation. This is useful when `se.out` contains counts for windows in an endogenous genome, but the trend fits are computed using spike-in chromatin regions.

An error is raised if the library sizes in `se.out$totals` are not identical to `object$totals`. This is because the average abundances are only comparable when the library sizes are the same. Consistent library sizes can be achieved by using the same `readParam` object in `windowCounts` and related functions.

### Value

If `se.out=FALSE`, a numeric matrix of dimensions equal to `object`, containing the offset for each observation. These offsets have already been scaled to be comparable in magnitude to the log-library sizes.

If `se.out=TRUE`, the same matrix is stored in the `offset` assay of `object` (if it is a SummarizedExperiment) or `object$offset` (if a DGEList) and the modified `object` is returned.

If `se.out` is a separate SummarizedExperiment or DGEList object, the offset matrix instead has dimensions equal to `se.out`. This matrix is stored inside `se.out` and the modified object is returned.

### Author(s)

Aaron Lun

### References

Ballman KV, Grill DE, Oberg AL, Therneau TM (2004). Faster cyclic loess: normalizing RNA arrays via linear models. Bioinformatics 20, 2778-86.

### See Also

`loessFit`, for the fitting algorithm.

`normalizeCyclicLoess`, for the original inspiration for this method.

### Examples

    counts <- matrix(rnbinom(400, mu=10, size=20), ncol=4)
    data <- SummarizedExperiment(list(counts=counts))
    data$totals <- colSums(counts)

    # TMM normalization.
    normFactors(data)

    # Using loess-based normalization, instead.
    offsets <- normOffsets(data)
    head(offsets)
    offsets <- normOffsets(data, span=0.4)
    offsets <- normOffsets(data, iterations=1)

[Package csaw version 1.26.0 Index]