RUVadj: RUV adjust

In ChuanJ/test: Analysing Illumina HumanMethylation BeadChip Data

Description

Post-process and summarize the results of call to RUVfit.

Usage

RUVadj(Y, fit, var.type=c("ebayes", "standard", "pooled"),
       p.type=c("standard", "rsvar", "evar"), cpginfo=NULL, ...)

Arguments

Y	The original data matrix used in the call to RUVfit.
fit	A RUV model fit (a list) as returned by RUVfit.
var.type	Which type of estimate for sigma2 should be used from the call to variance_adjust? The options are "ebayes", "standard", or "pooled." See variance_adjust for details.
p.type	Which type of p-values should be used from the call to variance_adjust? The options are "standard", "rsvar", or "evar".
cpginfo	A matrix or dataframe containing information about the CpGs. This information is included in the summary that is returned.
...	Other parameters that can be passed to ruv function ruv_summary.

Details

This function post-processes the results of a call to RUVfit and then summarizes the output. The post-processing step primarily consists of a call to ruv_summary and variance_adjust, which computes various adjustments to variances, t-statistics, and and p-values. See variance_adjust for details. The var.type and p.type options determine which of these adjustments are used.

After post-processing, the results are summarized into a list containing 4 objects: 1) the data matrix Y; 2) a dataframe R containing information about the rows (samples); 3) a dataframe C containing information about the columns (features, e.g. genes), and 4) a list misc of other information returned by RUVfit.

Value

An list containing:

Y	The original data matrix..
R	A dataframe of sample-wise information, including X, Z, and any other data passed in with rowinfo.
C	A dataframe of cpg-wise information, including p-values, estimated regression coefficients, estimated variances, column means, an index of the negative controls, and any other data passed in with cpginfo.
misc	A list of additional information returned by RUVfit.

Author(s)

Jovana Maksimovic [e-mail]

References

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.

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.

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.

See Also

MArrayLM, RUV2, RUV4, RUVinv, RUVrinv, p.adjust, get_empirical_variances, sigmashrink

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(Y=Mval, X=group, ctl=ctl)
fit2 <- RUVadj(Y=Mval, fit=fit)

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

ChuanJ/test documentation built on Oct. 30, 2019, 5:43 a.m.