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,x,y,paired=FALSE,mat.samp=NULL,B=100,balanced=FALSE,na.rm=FALSE,delta=0.9, alpha=(0:9)/10,include.0=TRUE,p0=NA,stable=TRUE,number.int=139,rand=NA,plot.legend=TRUE)
data |
the data set that should be analyzed. Every row of this data set must correspond to a gene. |
x |
vector of the columns of data that correspond to the treatment group.
In the paired case, (x[i],y[i]) build a pair. If, e.g., the first n1
columns contain the gene expression values of the treatment group,x=1:\eqn{n_1}{n1} . |
y |
vector of the columns of data that correspond to the control group. In the
paired case, (x[i], y[i]) are an observation pair. |
paired |
paired (TRUE ) or unpaired (FALSE ) data. Default is FALSE . |
mat.samp |
a permutation matrix. If specified, this matrix will be used, even if
rand and B are specified. |
B |
number of permutations used in the calculation of the null density. |
balanced |
if TRUE , only balanced permutations will be used. Default is
FALSE . |
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. |
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
of the fudge factor. |
p0 |
the prior probability that a gene is differentially expressed. If not specified, it will automatically be computed. |
stable |
if TRUE (default), p0 will be computed by the algorithm of
Storey and Tibshirani (2003). If FALSE , the (unstable) estimate that
ensures that the posterior probability of being differentially expressed is
always non-negative is computed. |
number.int |
number of equally spaced intervals between the minimum and and the maximum of the expression scores z that are used in the logistic regression for estimating the ratio of the null density to the mixture density. |
rand |
if specified, the random number generator will be put in a reproducible state. |
plot.legend |
if TRUE (default), a legend will be added to the plot of the
expression scores vs. their logit-transformed posterior probability. |
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.