GeneMeta (version 1.44.0)

zScores: 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

Value

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.

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.

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,]