EScombination.paired {metaMA} | R Documentation |
Effect size combination for paired data
Description
Calculates effect sizes from paired data either from classical or moderated t-tests (Limma, SMVar) for each study and combines these effect sizes.
Usage
EScombination.paired(logratios, moderated = c("limma", "SMVar", "t")[1], BHth = 0.05)
Arguments
logratios |
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. |
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)