zscore: Tools for Meta-analysis of gene expression data.
In GeneMeta: MetaAnalysis for High Throughput Experiments

Description Usage Arguments Details Value Author(s) References Examples

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

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")

`esets`	A `list` of `ExpressionSet`s, 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

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.

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.

M. Ruschhaupt

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

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,]

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colMeans, colSums, colnames, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, lengths, mapply, match, mget, order, paste, pmax, pmax.int,
    pmin, pmin.int, rank, rbind, rowMeans, rowSums, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: genefilter
             zSco_Ex_1  zSco_Ex_2       zSco     MUvals     MUsds     Qvals df
A28102_at   -0.9720703  0.2897012 -0.4782802 -0.1419667 0.2968274 0.8000955  1
AB000114_at -0.7127479 -0.1947118 -0.6407273 -0.1896826 0.2960427 0.1353908  1
             Qpvalues     Chisq Effect_Ex_1 Effect_Ex_2 EffectVar_Ex_1
A28102_at   0.3710648 0.6324508  -0.4099979  0.12103858      0.1778967
AB000114_at 0.7129069 0.5216999  -0.2991743 -0.08131079      0.1761882
            EffectVar_Ex_2
A28102_at        0.1745609
AB000114_at      0.1743862