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.
1 2 
data 
numeric 
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, 
method 
character string, indicates which RUV method should be used. Default method is

k 
integer, required if 
... 
additional arguments that can be passed to 
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 Mvalues,
defined as the log base 2 ratio of the methylated signal to the unmethylated
signal.
An object of class MArrayLM
(see MArrayLMclass
) 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 
Pvalues for the factor(s) of interest. 
multiplier 
The constant by which 
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 

k 

ctl 

Z 

fullW0 
Can be used to speed up future calls of 
Jovana Maksimovic jovana.maksimovic@mcri.edu.au
GagnonBartsch JA, Speed TP. (2012). Using control genes to correct for unwanted variation in microarray data. Biostatistics. 13(3), 53952. Available at: http://biostatistics.oxfordjournals.org/content/13/3/539.full.
GagnonBartsch, Jacob, and Speed. 2013. Removing Unwanted Variation from High Dimensional Data with Negative Controls. Available at: http://statistics.berkeley.edu/techreports/820.
RUV2
, RUV4
, RUVinv
, RUVrinv
,
topRUV
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  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)
}

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