Normalization with spike-in counts

Description

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

Usage

## S4 method for signature 'SCESet'
computeSpikeFactors(x)

Arguments

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. Spike-in counts are assumed to be stored in rows with fData(x)$is_control_feature – see ?isSpike<- for more details. Note that the geometric mean of all size factors is set to unity, for standardization purposes if one were to compare different sets of size factors.

Value

An object of class x is returned, containing a numeric vector of size factors for all cells in sizeFactors(x).

Author(s)

Aaron Lun

See Also

SCESet

Examples

set.seed(100)
popsize <- 200
ngenes <- 1000
all.facs <- 2^rnorm(popsize, sd=0.5)
counts <- matrix(rnbinom(ngenes*popsize, mu=all.facs*10, size=1), ncol=popsize, byrow=TRUE)
spikes <- matrix(rnbinom(100*popsize, mu=all.facs*10, size=0.5), ncol=popsize, byrow=TRUE)

combined <- rbind(counts, spikes)
colnames(combined) <- seq_len(popsize)
rownames(combined) <- seq_len(nrow(combined))
y <- newSCESet(countData=combined)
isSpike(y) <- rep(c(FALSE, TRUE), c(ngenes, 100))
out.facs <- computeSpikeFactors(y)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(scran)
Loading required package: BiocParallel
Loading required package: scater
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: ggplot2

Attaching package: 'scater'

The following object is masked from 'package:stats':

    filter

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/scran/computeSpikeFactors.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Spike-in normalization
> ### Title: Normalization with spike-in counts
> ### Aliases: computeSpikeFactors computeSpikeFactors,SCESet-method
> ### Keywords: normalization
> 
> ### ** Examples
> 
> set.seed(100)
> popsize <- 200
> ngenes <- 1000
> all.facs <- 2^rnorm(popsize, sd=0.5)
> counts <- matrix(rnbinom(ngenes*popsize, mu=all.facs*10, size=1), ncol=popsize, byrow=TRUE)
> spikes <- matrix(rnbinom(100*popsize, mu=all.facs*10, size=0.5), ncol=popsize, byrow=TRUE)
> 
> combined <- rbind(counts, spikes)
> colnames(combined) <- seq_len(popsize)
> rownames(combined) <- seq_len(nrow(combined))
> y <- newSCESet(countData=combined)
> isSpike(y) <- rep(c(FALSE, TRUE), c(ngenes, 100))
> out.facs <- computeSpikeFactors(y)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>