Spike-in normalization {scran} | R Documentation

## Normalization with spike-in counts

### Description

Compute size factors based on the coverage of spike-in transcripts.

### Usage

    ## S4 method for signature 'SingleCellExperiment'
    computeSpikeFactors(x, type=NULL, assay.type="counts", sf.out=FALSE, general.use=TRUE)

### Arguments

`x` | A SingleCellExperiment object with rows corresponding spike-in transcripts.
`type` | A character vector specifying which spike-in sets to use.
`assay.type` | A string indicating which assay contains the counts.
`sf.out` | A logical scalar indicating whether only size factors should be returned.
`general.use` | A logical scalar indicating whether the size factors should be stored for general use by all genes.

### Details

The size factor for each cell is defined as the sum of all spike-in counts in each cell. This is equivalent to normalizing to equalize spike-in coverage between cells. Size factors are scaled so that the mean of all size factors is unity, for standardization purposes if one were to compare different sets of size factors.

Spike-in counts are assumed to be stored in the rows specified by `isSpike(x)`. This specification should have been performed by supplying the names of the spike-in sets – see `?isSpike` for more details. By default, if multiple spike-in sets are available, all of them will be used to compute the size factors. The function can be restricted to a subset of the spike-ins by specifying the names of the desired spike-in sets in `type`. An error will be raised if no spike-in rows are detected.

By default, the function will store several copies of the same size factors in the output object. One copy will also be stored in `sizeFactors(x, type=s)`, where `s` is the name of each spike-in set in `type`. (If `type=NULL`, a copy is stored for every spike-in set, as all of them would be used to compute the size factors.) Separate storage allows spike-in-specific normalization in `normalize`. If `general.use=TRUE`, a copy will also be stored in `sizeFactors(x)` for normalization of all genes.

### Value

If `sf.out=TRUE`, a numeric vector of size factors is returned directly.

Otherwise, an object of class `x` is returned, containing size factors for all cells. A copy of the vector is stored for each spike-in set that was used to compute the size factors. If `general.use=TRUE`, a copy is also stored for use by non-spike-in genes.

### Author(s)

Aaron Lun

### References

Lun ATL, McCarthy DJ and Marioni JC (2016). A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5:2122

### See Also

`isSpike`

### Examples

    ################
    # Mocking up some data.
    set.seed(100)
    ncells <- 200

    nspikes <- 100
    spike.means <- 2^runif(nspikes, 3, 8)
    spike.disp <- 100/spike.means + 0.5
    spike.data <- matrix(rnbinom(nspikes*ncells, mu=spike.means, size=1/spike.disp), ncol=ncells)

    ngenes <- 2000
    cell.means <- 2^runif(ngenes, 2, 10)
    cell.disp <- 100/cell.means + 0.5
    cell.data <- matrix(rnbinom(ngenes*ncells, mu=cell.means, size=1/cell.disp), ncol=ncells)

    combined <- rbind(cell.data, spike.data)
    colnames(combined) <- seq_len(ncells)
    rownames(combined) <- seq_len(nrow(combined))
    y <- SingleCellExperiment(list(counts=combined))
    isSpike(y, "Spike") <- ngenes + seq_len(nspikes)

    ################
    # Computing and storing spike-in size factors.
    y2 <- computeSpikeFactors(y)
    head(sizeFactors(y2))
    head(sizeFactors(y2, type="Spike"))

    # general.use=FALSE does not modify general size factors
    sizeFactors(y2) <- 1
    sizeFactors(y2, type="Spike") <- 1
    y2 <- computeSpikeFactors(y2, general.use=FALSE)
    head(sizeFactors(y2))
    head(sizeFactors(y2, type="Spike"))

[Package scran version 1.10.2 Index]