RUVfit {missMethyl} | R Documentation |
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.
RUVfit(data, design, coef, ctl, method=c("inv", "rinv", "ruv4", "ruv2"), k = NULL, ...)
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 M-values,
defined as the log base 2 ratio of the methylated signal to the unmethylated
signal.
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 |
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
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.
RUV2
, RUV4
, RUVinv
, RUVrinv
,
topRUV
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) }