topRUV {missMethyl} | R Documentation |
Extract a table of the top-ranked CpGs from a linear model fit after
performing a differential methylation analysis using RUVfit
.
topRUV(fit, number=10, p.value.cut = 1, cut.on = c("p.ebayes.BH","p.BH","p.rsvar.BH","p.evar.BH","p.rsvar.ebayes.BH"), sort.by = c("p.ebayes.BH","p.BH","p.rsvar.BH","p.evar.BH","p.rsvar.ebayes.BH"))
fit |
An object containing a linear model fit produced by |
number |
integer, maximum number of genes to list. Default is 10. |
p.value.cut |
numeric, cutoff value for adjusted p-values. Only features with lower p-values are listed. Must ve between 0 and 1. Default is 1. |
cut.on |
numeric, the type of adjusted p-value that the cutoff should be applied to.
Default is |
sort.by |
character string, the type of adjusted p-value that should be used for sorting.
Default is |
This function summarises the results of a differential methylation analysis
performed using RUVfit
, followed by RUVadj
. The top ranked CpGs
are selected by first ranking the adjusted p-values (Default: p.ebayes.BH
),
then ranking the raw p-values (Default: p.ebayes
).
Produces a dataframe with rows corresponding to the top number
CpGs and
the following columns:
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 |
The following columns may or may not be present depending on the options
selected when RUVadj
was run:
p.rsvar |
P-values, after applying the method of rescaled variances. |
p.evar |
P-values, after applying the method of empirical variances. |
p.ebayes |
P-values, after applying the empirical bayes method of Smyth (2004). |
p.rsvar.ebayes |
P-values, after applying the empirical bayes method of Smyth (2004) and the method of rescaled variances. |
p.BH |
P-values adjusted for false discovery rate (FDR) using the method of Benjamini and Hochberg (1995). |
p.rsvar.BH |
FDR-adjusted p-values, after applying the method of rescaled variances. |
p.evar.BH |
FDR-adjusted p-values, after applying the method of empirical variances. |
p.ebayes.BH |
FDR-adjusted p-values, after applying the empirical bayes method of Smyth (2004). |
p.rsvar.ebayes.BH |
FDR-adjusted p-values, 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, 289-300.
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.
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) }