ebam.wilc {siggenes} | R Documentation |

Performs an Empirical Bayes Analysis of Microarrays by using Wilcoxon Rank Sums as expression scores for the genes.

ebam.wilc(data,cl,delta=.9,p0=NA,ties.rand=TRUE,zero.rand=TRUE,gene.names=NULL, R.fold=TRUE,R.unlog=TRUE,file.out=NA,na.rm=FALSE,rand=NA)

`data` |
the data set that should be analyzed. Every row of this data set must correspond to a gene, and each column to a biological sample. |

`cl` |
a vector containing the class labels of the samples. In the two class unpaired case,
the label of a sample is either 0 (e.g, control group) or 1 (e.g., case group).
In the two class paired case, the labels are the integers between 1 and n/2
(e.g., after treatment group) and between -1 and -n/2 (e.g., before treatment
group), where n is the length of `cl` and k is paired with -k. |

`delta` |
a gene will be called significant, if its posterior probability of
being differentially expressed is larger than or equal to `delta` . |

`p0` |
prior probability that a gene is differentially expressed. If not specified, it will automatically be computed. |

`ties.rand` |
if `TRUE` (default), non-integer expression scores will be randomly
assigned to the next lower or upper integer. Otherwise, they are assigned to
the integer that is closer to the mean. |

`zero.rand` |
if `TRUE` (default), the sign of each Zero in the computation of
the Wilcoxon signed rank sums will be randomly assigned. If `FALSE` , the
sign of the Zeros will be set to '–'. |

`gene.names` |
a vector containing the names of the genes. |

`R.fold` |
if `TRUE` (default), the fold change for each differentially
expressed gene will be computed. |

`R.unlog` |
if `TRUE` , the anti-log of `data` will be used in the computation of the
R.fold. This is recommended if `data` consists of log2 transformed gene expression
data. |

`file.out` |
if specified, general information like the number of significant genes and the estimated FDR and gene-specific information like the expression scores, the q-values, the R fold etc. of the differentially expressed genes are stored in this file. |

`na.rm` |
if `FALSE` (default), the fold change of genes with at least one
missing value will be set to `NA` . If `TRUE` , missing values will be
replaced by the genewise mean. |

`rand` |
if specified, the random number generator will be set in a reproducible state. |

a plot of the expression scores vs. their posterior probability of being differentially expressed, and (optionally) a file containing general information like the FDR and the number of differentially expressed genes and gene-specific information on the differentially expressed genes like their names, their q-values and their fold change.

`nsig` |
number of significant genes. |

`fdr` |
estimated FDR. |

`ebam.output` |
table containing gene-specific information on the differentially expressed genes. |

`row.sig.genes` |
vector containing of the row numbers that belong to the differentially expressed genes. |

`...` |

Holger Schwender, holger.schw@gmx.de

Efron, B., Storey, J.D., Tibshirani, R. (2001). Microarrays, empirical Bayes methods, and
the false discovery rate, *Technical Report*, Department of Statistics, Stanford
University.

Storey, J.D., and Tibshirani, R. (2003). Statistical significance for genome-wide
experiments, *Technical Report*, Department of Statistics, Stanford University.

Schwender, H. (2003). Assessing the false discovery rate in a statistical analysis of
gene expression data, Chapter 8, *Diploma thesis*, Department of Statistics,
University of Dortmund, http://de.geocities.com/holgerschw/thesis.pdf.

## Not run: library(multtest) # Load the data of Golub et al. (1999). data(golub) contains # a 3051x38 gene expression matrix called golub, a vector of # length called golub.cl that consists of the 38 class labels, # and a matrix called golub.gnames whose third column contains # the gene names. data(golub) # An EBAM-Wilc analysis of the Golub data is performed by ebam.wilc.out<-ebam.wilc(golub,golub.cl,gene.names=golub.gnames[,3],rand=123) # For further analyses, the row numbers of the differentially expressed # genes are obtained by ebam.wilc.out$row.sig.genes ## End(Not run)

[Package *siggenes* version 1.4.0 Index]