find.a0 {siggenes}R Documentation

Computation of the Fudge Factor


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.




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,


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,

See Also

ebam ebam.wilc


## Not run: 
    # Load the data of Golub et al. (1999). data(golub) contains
    # a 3051x38 gene expression matrix called golub, a vector of
    # length called that consists of the 38 class labels,
    # and a matrix called golub.gnames whose third column contains
    # the gene names.
    # 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.
## End(Not run)

[Package siggenes version 1.4.0 Index]