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., before treatment group) and between -1 and -n/2 (e.g., after 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.
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)