zscore: Tools for Meta-analysis of gene expression data.

In Bioconductor/GeneMeta: MetaAnalysis for High Throughput Experiments

Tools for Meta-analysis of gene expression data.

Description

A small number of meta-analysis functions for computing zScores for FEM and REM and computing FDR.

Usage

zScores(esets, classes, useREM=TRUE, CombineExp=1:length(esets))
zScorePermuted(esets, classes, useREM=TRUE, CombineExp=1:length(esets))
zScoreFDR(esets, classes, useREM=TRUE, nperm=1000, CombineExp=1:length(esets))
multExpFDR(theScores, thePermScores, type="pos")

Arguments

esets	A list of ExpressionSets, one expression set per experiment. All experiments must have the same variables(genes).
classes	A list of class memberships, one per experiment. Each list can only contain 2 levels.
useREM	A logical value indicating whether or not to use a REM, TRUE, or a FEM, FALSE, for combining the z scores.
theScores	A vector of scores (e.g. t-statistics or z scores)
thePermScores	A vector of permuted scores (e.g. t-statistics or z scores)
type	"pos", "neg" or "two.sided"
nperm	number of permutations to calculate the FDR
CombineExp	vector of integer- which experiments should be combined-default:all experiments

Details

The function zScores implements the approach of Choi et al. for for a set of ExpressionSets. The function zScorePermuted applies zScore to a single permutation of the class labels. The function zScoreFDR computes a FDR for each gene, both for each single experiment and for the combined experiment. The FDR is calculated as described in Choi et al. Up to now ties in the zscores are not taken into account in the calculation. The function might produce incorrect results in that case. The function also computes zScores, both for the combines experiment and for each single experiment.

Value

A matrix with one row for each probe(set) and the following columns:

zSco_Ex_	For each single experiment the standardized mean difference, Effect_Ex_, divided by the estimated standard deviation, the square root of the EffectVar_Ex_ column.
MUvals	The combined standardized mean difference (using a FEM or REM)
MUsds	The standard deviation of the MUvals.
zSco	The z statistic - the MUvals divided by their standard deviations, MUsds.
Qvals	Cochran's Q statistic for each gene.
df	The degree of freedom for the Chi-square distribution. This is equal to the number of combined experiments minus one.
Qpvalues	The probability that a Chi-square random variable, with df degrees of freedom) has a higher value than the value from the Q statistic.
Chisq	The probability that a Chi-square random variate (with 1 degree of freedom) has a higher value than the value of zSco^2.
Effect_Ex_	The standardized mean difference for each single experiment.
EffectVar_Ex_	The variance of the standardized mean difference for each single experiment.

Note that the three column names that end in an underscore are replicated, once for each experiment that is being analyzed.

Author(s)

M. Ruschhaupt

References

Choi et al, Combining multiple microarray studies and modeling interstudy variation. Bioinformatics, 2003, i84-i90.

Examples

data(Nevins)

##Splitting 
thestatus  <- Nevins$ER.status
group1     <- which(thestatus=="pos")
group2     <- which(thestatus=="neg")
rrr        <- c(sample(group1, floor(length(group1)/2)),
                sample(group2,ceiling(length(group2)/2)))
Split1     <- Nevins[,rrr]
Split2     <- Nevins[,-rrr]

#obtain classes
Split1.ER <- as.numeric(Split1$ER.status) - 1
Split2.ER <-as.numeric(Split2$ER.status) - 1

esets     <- list(Split1,Split2)
classes   <- list(Split1.ER,Split2.ER)
theScores <- zScores(esets,classes,useREM=FALSE)
theScores[1:2,]

Bioconductor/GeneMeta documentation built on May 4, 2024, 4:40 p.m.