0th

Percentile

##### Identify differentially expressed genes by integrating multiple studies(datasets) using minMCC approach

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$meta.statthe statistics for the chosen meta analysis method • meta.analysis$pvalthe p-value for the above statistic. It is calculated from permutation.
• meta.analysis$FDRthe FDR of the p-value. • meta.analysis$AW.weightThe optimal weight assigned to each dataset/study for each gene if the 'AW' or 'AW.OC' method was chosen.
• raw.datathe 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)

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

##### Aliases
label1<-rep(0:2,each=5)
MetaDE.minMCC(x,nperm=100)