Calculate rescaled variances, empirical variances, etc. For use with RUV model
fits produced using RUVfit
.
1 
fit 
An object of class 
ebayes 
A logical variable. Should empirical bayes estimates be calculated? 
evar 
A logical variable. Should empirical variance estimates be calculated? 
rsvar 
A logical variable. Should rescaled variance estimates be calculated? 
... 
additional arguments that can be passed to 
Adjust variance. By default only the empirical bayes method of Smyth (2004) is performed.
An object of class MArrayLM
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 
The following items may or may not be present depending on the options
selected when RUVadj
was run:
p.rsvar 
Pvalues, after applying the method of rescaled variances. 
p.evar 
Pvalues, after applying the method of empirical variances. 
p.ebayes 
Pvalues, after applying the empirical bayes method of Smyth (2004). 
p.rsvar.ebayes 
Pvalues, after applying the empirical bayes method of Smyth (2004) and the method of rescaled variances. 
p.BH 
Pvalues adjusted for false discovery rate (FDR) using the method of Benjamini and Hochberg (1995). 
p.rsvar.BH 
FDRadjusted pvalues, after applying the method of rescaled variances. 
p.evar.BH 
FDRadjusted pvalues, after applying the method of empirical variances. 
p.ebayes.BH 
FDRadjusted pvalues, after applying the empirical bayes method of Smyth (2004). 
p.rsvar.ebayes.BH 
FDRadjusted pvalues, after applying the empirical bayes method of Smyth (2004) and the method of rescaled variances. 
Jovana Maksimovic jovana.maksimovic@mcri.edu.au
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series, B, 57, 289300.
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.
Smyth, G. K. (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, Volume 3, Article 3. http://www.statsci.org/smyth/pubs/ebayes.pdf.
MArrayLM
, RUV2
, RUV4
,
RUVinv
, RUVrinv
, p.adjust
,
get_empirical_variances
, sigmashrink
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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