List of matrices (or data frames). Each matrix has one row per gene and one column per replicate and gives the logratios of one study. All studies must have the same genes.
moderated
Method to calculate the test statistic inside each study from which the effect size is computed. moderated has to be chosen between "limma", "SMVar" and "t".
BHth
Benjamini Hochberg threshold. By default, the False Discovery Rate is controlled at 5%.
Value
List
Study1
Vector of indices of differentially expressed genes in study 1. Similar names are given for the other individual studies.
AllIndStudies
Vector of indices of differentially expressed genes found by at least one of the individual studies.
Meta
Vector of indices of differentially expressed genes in the meta-analysis.
TestStatistic
Vector with test statistics for differential expression in the meta-analysis.
Note
While the invisible object resulting from this function contains
the values described previously, other quantities of interest are printed:
DE,IDD,Loss,IDR,IRR.
All these quantities are defined in function IDDIDR and in (Marot et al., 2009)
Author(s)
Guillemette Marot
References
Marot, G., Foulley, J.-L., Mayer, C.-D., Jaffrezic, F. (2009) Moderated effect size and p-value combinations for microarray meta-analyses. Bioinformatics. 25 (20): 2692-2699.
Examples
data(Singhdata)
#create artificially paired data:
artificialdata=lapply(Singhdata$esets,FUN=function(x) (x[,1:10]-x[,11:20]))
#Meta-analysis
res=EScombination.paired(artificialdata)
#Number of differentially expressed genes in the meta-analysis
length(res$Meta)
#To plot an histogram of raw p-values
rawpval=2*(1-pnorm(abs(res$TestStatistic)))
hist(rawpval,nclass=100)