# MetaDE.minMCC

From MetaDE v1.0.5
by Xingbin Wang

##### 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$meta.stat the statistics for the chosen meta analysis method meta.analysis$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

##### 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)
```

*Documentation reproduced from package MetaDE, version 1.0.5, License: GPL-2*

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