zScores {GeneMeta} | R Documentation |
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 expression sets, 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 zScores. |
theScores |
A vector of scores (e.g. t-statistics or z Scores) |
thePermScores |
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 exprSets. The function zScorePermuted
applies
zScore
to a single permutation of the class labels.
The function zScoreFDR
computes a FDR for each gene. It also
computes zScores, both for the combines experiment and for each single
experiment. The FDR is also computed for each single experiment and for the combined 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 Chisquare distribution. This is equal to the number of combined experiments minus one. |
Qpvalues |
The probability that a Chisquare random variable,
with df degrees of freedom) has a higher value than the value from
the Q statistic. |
Chisq |
The probability that a Chisquare 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,]