MetaDE.minMCC: Identify differentially expressed genes by integrating...

In MetaDE: MetaDE: Microarray meta-analysis for differentially expressed gene detection

Description

MetaDE.minMCC Identify differentially expressed genes with the same pattern across studies/datasets.

Usage

MetaDE.minMCC(x,nperm=100,miss.tol=0.3)

Arguments

x	a list of data sets and their labels. The first list is a list of datasets, the second list is a list of their labels. see examples for details.
nperm	The number of permutations. If nperm is NULL,the results will be based on asymptotic distribution.
miss.tol	The maximum percent missing data allowed in any gene (default 30 percent).

Value

A list containing:

meta.analysis$meta.stat	the statistics for the chosen meta analysis method
meta.analysis$pval	the p-value for the above statistic. It is calculated from permutation.
meta.analysis$FDR	the FDR of the p-value.
meta.analysis$AW.weight	The optimal weight assigned to each dataset/study for each gene if the 'AW' or 'AW.OC' method was chosen.
raw.data	the raw data of your input. That's x. This part will be used for plotting.

References

Jia Li and George C. Tseng. (2011) An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies. Annals of Applied Statistics. 5:994-1019.

Shuya Lu, Jia Li, Chi Song, Kui Shen and George C Tseng. (2010) Biomarker Detection in the Integration of Multiple Multi-class Genomic Studies. Bioinformatics. 26:333-340. (PMID: 19965884; PMCID: PMC2815659)

See Also

MetaDE.rawdata MetaDE.pvalue MetaDE.ES draw.DEnumber

Examples

label1<-rep(0:2,each=5)
label2<-rep(0:2,each=4)
exp1<-cbind(matrix(rnorm(5*20),20,5),matrix(rnorm(5*20,2),20,5),matrix(rnorm(5*20,2.5),20,5))
exp2<-cbind(matrix(rnorm(4*20),20,4),matrix(rnorm(4*20,1.5),20,4),matrix(rnorm(4*20,2.5),20,4))
x<-list(list(exp1,label1),list(exp2,label2))

MetaDE.minMCC(x,nperm=100)

MetaDE documentation built on May 29, 2017, 9 a.m.