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.