Identify differentially expressed genes by integrating multiple studies(datasets) using minMCC approach
Identify differentially expressed genes with the same pattern across studies/datasets.
- 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.
- The number of permutations. If nperm is NULL,the results will be based on asymptotic distribution.
- The maximum percent missing data allowed in any gene (default 30 percent).
- 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.
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)
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)