R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Remove Unwanted Variation Using Control Genes

RUVg-methods

R Documentation

Remove Unwanted Variation Using Control Genes

Description

This function implements the RUVg method of Risso et al. (2014).

Usage

RUVg(x, cIdx, k, drop=0, center=TRUE, round=TRUE, epsilon=1, tolerance=1e-8, isLog=FALSE)

Arguments

`x`	Either a genes-by-samples numeric matrix or a SeqExpressionSet object containing the read counts.
`cIdx`	A character, logical, or numeric vector indicating the subset of genes to be used as negative controls in the estimation of the factors of unwanted variation.
`k`	The number of factors of unwanted variation to be estimated from the data.
`drop`	The number of singular values to drop in the estimation of the factors of unwanted variation. This number is usually zero, but might be set to one if the first singular value captures the effect of interest. It must be less than `k`.
`center`	If `TRUE`, the counts are centered, for each gene, to have mean zero across samples. This is important to ensure that the first singular value does not capture the average gene expression.
`round`	If `TRUE`, the normalized measures are rounded to form pseudo-counts.
`epsilon`	A small constant (usually no larger than one) to be added to the counts prior to the log transformation to avoid problems with log(0).
`tolerance`	Tolerance in the selection of the number of positive singular values, i.e., a singular value must be larger than `tolerance` to be considered positive.
`isLog`	Set to `TRUE` if the input matrix is already log-transformed. Ignored if `x` is a SeqExpressionSet.

Details

The RUVg procedure performs factor analysis of the read counts based on a suitably-chosen subset of negative control genes known a priori not be differentially expressed (DE) between the samples under consideration.

Several types of controls can be used, including housekeeping genes, spike-in sequences (e.g., ERCC), or “in-silico” empirical controls (e.g., least significantly DE genes based on a DE analysis performed prior to RUV normalization).

Note that one can relax the negative control gene assumption by requiring instead the identification of a set of positive or negative controls, with a priori known expression fold-changes between samples. RUVg can then simply be applied to control-centered log counts, as detailed in the vignette.

Methods

signature(x = "matrix", cIdx = "ANY", k = "numeric")

It returns a list with

A samples-by-factors matrix with the estimated factors of unwanted variation (W).
The genes-by-samples matrix of normalized expression measures (possibly rounded) obtained by removing the factors of unwanted variation from the original read counts (normalizedCounts).

signature(x = "SeqExpressionSet", cIdx = "character", k="numeric")

It returns a SeqExpressionSet with

The normalized counts in the normalizedCounts slot.
The estimated factors of unwanted variation as additional columns of the phenoData slot.

Author(s)

Davide Risso

References

D. Risso, J. Ngai, T. P. Speed, and S. Dudoit. Normalization of RNA-seq data using factor analysis of control genes or samples. Nature Biotechnology, 2014. (In press).

D. Risso, J. Ngai, T. P. Speed, and S. Dudoit. The role of spike-in standards in the normalization of RNA-Seq. In D. Nettleton and S. Datta, editors, Statistical Analysis of Next Generation Sequence Data. Springer, 2014. (In press).

Examples

library(zebrafishRNASeq)
data(zfGenes)

## run on a subset of genes for time reasons 
## (real analyses should be performed on all genes)
genes <- rownames(zfGenes)[grep("^ENS", rownames(zfGenes))]
spikes <- rownames(zfGenes)[grep("^ERCC", rownames(zfGenes))]
set.seed(123)
idx <- c(sample(genes, 1000), spikes)
seq <- newSeqExpressionSet(as.matrix(zfGenes[idx,]))

# RUVg normalization
seqRUVg <- RUVg(seq, spikes, k=1)

pData(seqRUVg)
head(normCounts(seqRUVg))

plotRLE(seq, outline=FALSE, ylim=c(-3, 3))
plotRLE(seqRUVg, outline=FALSE, ylim=c(-3, 3))

barplot(as.matrix(pData(seqRUVg)), beside=TRUE)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RUVSeq)
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: EDASeq
Loading required package: ShortRead
Loading required package: BiocParallel
Loading required package: Biostrings
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

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

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: XVector
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: GenomicAlignments
Loading required package: SummarizedExperiment
Loading required package: edgeR
Loading required package: limma

Attaching package: 'limma'

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

    plotMA

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RUVSeq/RUVg.Rd_%03d_medium.png", width=480, height=480)
> ### Name: RUVg-methods
> ### Title: Remove Unwanted Variation Using Control Genes
> ### Aliases: RUVg RUVg-methods RUVg,matrix,ANY,numeric-method
> ###   RUVg,SeqExpressionSet,character,numeric-method
> 
> ### ** Examples
> 
> library(zebrafishRNASeq)
> data(zfGenes)
> 
> ## run on a subset of genes for time reasons 
> ## (real analyses should be performed on all genes)
> genes <- rownames(zfGenes)[grep("^ENS", rownames(zfGenes))]
> spikes <- rownames(zfGenes)[grep("^ERCC", rownames(zfGenes))]
> set.seed(123)
> idx <- c(sample(genes, 1000), spikes)
> seq <- newSeqExpressionSet(as.matrix(zfGenes[idx,]))
> 
> # RUVg normalization
> seqRUVg <- RUVg(seq, spikes, k=1)
> 
> pData(seqRUVg)
              W_1
Ctl1  -0.07675368
Ctl3   0.52288762
Ctl5   0.38470564
Trt9  -0.25997886
Trt11 -0.69905397
Trt13  0.12819326
> head(normCounts(seqRUVg))
                   Ctl1 Ctl3 Ctl5 Trt9 Trt11 Trt13
ENSDARG00000043686    2    3    1    0     2     0
ENSDARG00000089089    0    0    0    0     0     0
ENSDARG00000060813  278  158  214  228   202   722
ENSDARG00000092245    0    7    2    0    10     2
ENSDARG00000094339    0    0    0    0     0     0
ENSDARG00000007918  125   81   58   69    76   141
> 
> plotRLE(seq, outline=FALSE, ylim=c(-3, 3))
> plotRLE(seqRUVg, outline=FALSE, ylim=c(-3, 3))
> 
> barplot(as.matrix(pData(seqRUVg)), beside=TRUE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>