Description Usage Arguments Value References See Also Examples

View source: R/meta_analysis03282012.r

`MetaDE.minMCC`

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

1 | ```
MetaDE.minMCC(x,nperm=100,miss.tol=0.3)
``` |

`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). |

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)

`MetaDE.rawdata`

`MetaDE.pvalue`

`MetaDE.ES`

`draw.DEnumber`

1 2 3 4 5 6 7 | ```
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)
``` |

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