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Last data update: 2014.03.03

R: Remove unwanted variation when testing for differential...

RUVfit

R Documentation

Remove unwanted variation when testing for differential methylation

Description

Provides an interface similar to lmFit from limma to the RUV2, RUV4, RUVinv and RUVrinv functions from the ruv package, which facilitates the removal of unwanted variation in a differential methylation analysis. A set of negative control variables, as described in the references, must be specified.

Usage

RUVfit(data, design, coef, ctl, method=c("inv", "rinv", "ruv4", "ruv2"), 
k = NULL, ...)

Arguments

`data`	numeric `matrix` with rows corresponding to the features of interest such as CpG sites and columns corresponding to samples or arrays.
`design`	the design matrix of the experiment, with rows corresponding to arrays/samples and columns to coefficients to be estimated.
`coef`	integer, column of the design matrix containing the comparison to test for differential methylation. Default is the last colum of the design matrix.
`ctl`	logical vector, `length == nrow(data)`. Features that are to be used as negative control variables are indicated as TRUE, all other features are FALSE.
`method`	character string, indicates which RUV method should be used. Default method is `RUVinv`.
`k`	integer, required if `method` is "ruv2" or "ruv4". Indicates the number of unwanted factors to use. Can be 0.
`...`	additional arguments that can be passed to `RUV2`, `RUV4`, `RUVinv` and `RUVrinv`. See linked function documentation for details.

Details

This function depends on the ruv and limma packages and is used to estimate and adjust for unwanted variation in a differential methylation analysis. Briefly, the unwanted factors W are estimated using negative control variables. Y is then regressed on the variables X, Z, and W. For methylation data, the analysis is performed on the M-values, defined as the log base 2 ratio of the methylated signal to the unmethylated signal.

Value

An object of class MArrayLM (see MArrayLM-class) containing:

`coefficients`	The estimated coefficients of the factor(s) of interest.
`sigma2`	Estimates of the features' variances.
`t`	t statistics for the factor(s) of interest.
`p`	P-values for the factor(s) of interest.
`multiplier`	The constant by which `sigma2` must be multiplied in order to get an estimate of the variance of `coefficients`
`df`	The number of residual degrees of freedom.
`W`	The estimated unwanted factors.
`alpha`	The estimated coefficients of W.
`byx`	The coefficients in a regression of Y on X (after both Y and X have been "adjusted" for Z). Useful for projection plots.
`bwx`	The coefficients in a regression of W on X (after X has been "adjusted" for Z). Useful for projection plots.
`X`	`X`. Included for reference.
`k`	`k`. Included for reference.
`ctl`	`ctl`. Included for reference.
`Z`	`Z`. Included for reference.
`fullW0`	Can be used to speed up future calls of `RUVfit`.

Author(s)

Jovana Maksimovic jovana.maksimovic@mcri.edu.au

References

Gagnon-Bartsch JA, Speed TP. (2012). Using control genes to correct for unwanted variation in microarray data. Biostatistics. 13(3), 539-52. Available at: http://biostatistics.oxfordjournals.org/content/13/3/539.full.

Gagnon-Bartsch, Jacob, and Speed. 2013. Removing Unwanted Variation from High Dimensional Data with Negative Controls. Available at: http://statistics.berkeley.edu/tech-reports/820.

Examples

if(require(minfi) & require(minfiData) & require(limma)) {

# Get methylation data for a 2 group comparison
meth <- getMeth(MsetEx)
unmeth <- getUnmeth(MsetEx)
Mval <- log2((meth + 100)/(unmeth + 100))

group<-factor(pData(MsetEx)$Sample_Group)
design<-model.matrix(~group)

# Perform initial analysis to empirically identify negative control features 
# when not known a priori
lFit = lmFit(Mval,design)
lFit2 = eBayes(lFit)
lTop = topTable(lFit2,coef=2,num=Inf)

# The negative control features should *not* be associated with factor of interest
# but *should* be affected by unwanted variation 
ctl = rownames(Mval) %in% rownames(lTop[lTop$adj.P.Val > 0.5,])

# Perform RUV adjustment and fit
fit = RUVfit(data=Mval, design=design, coef=2, ctl=ctl)
fit2 = RUVadj(fit)

# Look at table of top results
top = topRUV(fit2)
}

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)

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Type 'contributors()' for more information and
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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(missMethyl)
Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/missMethyl/RUVfit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: RUVfit
> ### Title: Remove unwanted variation when testing for differential
> ###   methylation
> ### Aliases: RUVfit
> 
> ### ** Examples
> 
> if(require(minfi) & require(minfiData) & require(limma)) {
+ 
+ # Get methylation data for a 2 group comparison
+ meth <- getMeth(MsetEx)
+ unmeth <- getUnmeth(MsetEx)
+ Mval <- log2((meth + 100)/(unmeth + 100))
+ 
+ group<-factor(pData(MsetEx)$Sample_Group)
+ design<-model.matrix(~group)
+ 
+ # Perform initial analysis to empirically identify negative control features 
+ # when not known a priori
+ lFit = lmFit(Mval,design)
+ lFit2 = eBayes(lFit)
+ lTop = topTable(lFit2,coef=2,num=Inf)
+ 
+ # The negative control features should *not* be associated with factor of interest
+ # but *should* be affected by unwanted variation 
+ ctl = rownames(Mval) %in% rownames(lTop[lTop$adj.P.Val > 0.5,])
+ 
+ # Perform RUV adjustment and fit
+ fit = RUVfit(data=Mval, design=design, coef=2, ctl=ctl)
+ fit2 = RUVadj(fit)
+ 
+ # Look at table of top results
+ top = topRUV(fit2)
+ }
Loading required package: minfi
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,
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    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

Loading required package: Biobase
Welcome to Bioconductor

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

Loading required package: lattice
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Attaching package: 'S4Vectors'

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

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biostrings
Loading required package: XVector
Loading required package: bumphunter
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Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: minfiData
Loading required package: IlluminaHumanMethylation450kmanifest
Loading required package: IlluminaHumanMethylation450kanno.ilmn12.hg19
Loading required package: limma

Attaching package: 'limma'

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

    plotMA

> 
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> 
> dev.off()
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