pvalcombination {metaMA} | R Documentation |
P-value combination for unpaired data
Description
Calculates differential expression p-values from unpaired data either from classical or moderated t-tests (Limma, SMVar) for each study and combines these p-values by the inverse normal method.
Usage
pvalcombination(esets, classes, moderated = c("limma", "SMVar", "t")[1], BHth = 0.05)
Arguments
esets |
List of matrices (or data frames), one matrix per study. Each matrix has one row per gene and one column per replicate and gives the expression data for both conditions with the order specified in the |
classes |
List of class memberships, one per study. Each vector or factor of the list can only contain two levels which correspond to the two conditions studied. |
moderated |
Method to calculate the test statistic inside each study from which the p-value 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)
#Meta-analysis
res=pvalcombination(esets=Singhdata$esets,classes=Singhdata$classes)
#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)