estimateSizeFactors,DESeqDataSet-method {DESeq2} | R Documentation

## Estimate the size factors for a `DESeqDataSet`

### Description

This function estimates the size factors using the "median ratio method" described by Equation 5 in Anders and Huber (2010). The estimated size factors can be accessed using the accessor function `sizeFactors`. Alternative library size estimators can also be supplied using the assignment function `sizeFactors<-`.

### Usage

    ## S4 method for signature 'DESeqDataSet'
    estimateSizeFactors(
      object,
      type = c("ratio", "poscounts", "iterate"),
      locfunc = stats::median,
      geoMeans,
      controlGenes,
      normMatrix,
      quiet = FALSE
    )

### Arguments

`object` | a DESeqDataSet
`type` | Method for estimation: either "ratio", "poscounts", or "iterate". "ratio" uses the standard median ratio method introduced in DESeq. The size factor is the median ratio of the sample over a "pseudosample": for each gene, the geometric mean of all samples. "poscounts" and "iterate" offer alternative estimators, which can be used even when all genes contain a sample with a zero (a problem for the default method, as the geometric mean becomes zero, and the ratio undefined). The "poscounts" estimator deals with a gene with some zeros, by calculating a modified geometric mean by taking the n-th root of the product of the non-zero counts. This evolved out of use cases with Paul McMurdie's phyloseq package for metagenomic samples. The "iterate" estimator iterates between estimating the dispersion with a design of ~1, and finding a size factor vector by numerically optimizing the likelihood of the ~1 model.
`locfunc` | a function to compute a location for a sample. By default, the median is used. However, especially for low counts, the `shorth` function from the genefilter package may give better results.
`geoMeans` | by default this is not provided and the geometric means of the counts are calculated within the function. A vector of geometric means from another count matrix can be provided for a "frozen" size factor calculation. The size factors will be scaled to have a geometric mean of 1 when supplying `geoMeans`.
`controlGenes` | optional, numeric or logical index vector specifying those genes to use for size factor estimation (e.g. housekeeping or spike-in genes)
`normMatrix` | optional, a matrix of normalization factors which do not yet control for library size. Note that this argument should not be used (and will be ignored) if the `dds` object was created using `tximport`. In this case, the information in `assays(dds)[["avgTxLength"]]` is automatically used to create appropriate normalization factors. Providing `normMatrix` will estimate size factors on the count matrix divided by `normMatrix` and store the product of the size factors and `normMatrix` as `normalizationFactors`. It is recommended to divide out the row-wise geometric mean of `normMatrix` so the rows roughly are centered on 1.
`quiet` | whether to print messages

### Details

Typically, the function is called with the idiom:

`dds <- estimateSizeFactors(dds)`

See `DESeq` for a description of the use of size factors in the GLM. One should call this function after `DESeqDataSet` unless size factors are manually specified with `sizeFactors`. Alternatively, gene-specific normalization factors for each sample can be provided using `normalizationFactors` which will always preempt `sizeFactors` in calculations.

Internally, the function calls `estimateSizeFactorsForMatrix`, which provides more details on the calculation.

### Value

The DESeqDataSet passed as parameters, with the size factors filled in.

### Author(s)

Simon Anders

### References

Reference for the median ratio method:

Simon Anders, Wolfgang Huber: Differential expression analysis for sequence count data. Genome Biology 2010, 11:106. http://dx.doi.org/10.1186/gb-2010-11-10-r106

### See Also

`estimateSizeFactorsForMatrix`

### Examples

    dds <- makeExampleDESeqDataSet(n=1000, m=4)
    dds <- estimateSizeFactors(dds)
    sizeFactors(dds)

    dds <- estimateSizeFactors(dds, controlGenes=1:200)

    m <- matrix(runif(1000 * 4, .5, 1.5), ncol=4)
    dds <- estimateSizeFactors(dds, normMatrix=m)
    normalizationFactors(dds)[1:3,]

    geoMeans <- exp(rowMeans(log(counts(dds))))
    dds <- estimateSizeFactors(dds,geoMeans=geoMeans)
    sizeFactors(dds)

[Package DESeq2 version 1.32.0 Index]