find.a0 {siggenes} | R Documentation |

Provides the required information for obtaining the optimal choice of the fudge factor in the Empirical Bayes Analysis of Microarrays that uses the modified t statistics.

find.a0(data,cl,B=100,balanced=FALSE,mat.samp=NULL,delta=0.9,alpha=(0:9)/10, include.0=TRUE,p0=NA,plot.legend=TRUE,na.rm=FALSE,rand=TRUE)

`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.
For one group data, the label for each sample should be 1. |

`B` |
number of permutations used in the calculation of the null density. |

`balanced` |
if `TRUE` , only balanced permutations will be used. Default is
`FALSE` . |

`mat.samp` |
a permutation matrix. If specified, this matrix will be used, even if
`rand` and `B` are specified. |

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

`alpha` |
a vector of possible values for the fudge factor a0 in terms
of quantiles of the standard deviations of the genes. |

`include.0` |
if `TRUE` (default), a0=0 will also be a possible
choice for the fudge factor. |

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

`plot.legend` |
if `TRUE` (default), a legend will be added to the plot of the
expression scores vs. their logit-transformed posterior probability. |

`na.rm` |
if `FALSE` (default), the expression score of genes with one or more
missing values will be set to `NA` . If `TRUE` , the missing values
will be replaced by the genewise mean of the non-missing values. |

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

a list of the numbers of genes called differentially expressed by the EBAM analysis
for several choices of *a0*, and the plot of the expression scores vs. their
corresponding logit-transformed posterior probability of being significant.

`sig.a0` |
vector containing the number of differentially expressed genes for the
specified set of values for a0. |

`a0` |
the optimal choice of the fudge factor using the criterion of Efron et al. (2001)
that the a0 should be used which leads to the most differentially
expressed genes. |

The results of `find.a0`

must be assigned to an object for the further analysis
with `ebam`

.

Holger Schwender, holger.schw@gmx.de

Efron, B., Tibshirani, R., Storey, J.D., and Tusher, V. (2001). Empirical Bayes
Analysis of a Microarray Experiment, *JASA*, 96, 1151-1160.

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 7, *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) # Now the optimal value for the fudge factor a0 is computed, # where possible values of the a0 are 0 and the 0, 0.05 and # 0.1 quantile of the standard deviations of the genes. # Setting rand=123 makes the results reproducible. find.out<-find.a0(golub,golub.cl,alpha=c(0,0.05,0.1),rand=123) ## End(Not run)

[Package *siggenes* version 1.4.0 Index]