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,x,y,paired=FALSE,delta=.9,p0=NA,stable.p0=TRUE,use.offset=TRUE,use.weights=TRUE, ties.rand=TRUE,zero.rand=TRUE,ns.df=5,col.accession=NA,col.gene.name=NA,R.fold=TRUE, R.dataset=data,file.out=NA,rand=NA,na.rm=FALSE)
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 of
data build the treatment group, x=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 . |
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. |
stable.p0 |
if TRUE (default), p0 will be computed by the algorithm
of Storey and Tibshirani (2003). If FALSE , the (unstable) estimate will
be computed that ensures that the posterior probability of being differentially
expressed is always nonnegative. |
use.offset |
if TRUE (default), an offset will be used in the Poisson regression
for the estimation of the density of the expression scores of all genes. |
use.weights |
if TRUE (default), weights are used in the natural cubic spline
fit for the estimation of p0. |
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 ''. |
ns.df |
the number of degrees of freedom used in the Poisson regression for the estimation of the mixture density of the expression scores of all genes. |
col.accession |
the column of data containing the accession numbers of
the genes. If specified, the accession numbers of the significant genes
will be added to the output. |
col.gene.name |
the column of data that contains the names of the genes.
If specified, the names of the significant genes will be added to the output. |
R.fold |
if TRUE (default), the fold change for each differentially
expressed gene will be computed. |
R.dataset |
the data set used in the computation of the fold change. This data
set can be a transformed version of 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. |
rand |
if specified, the random number generator will be set in a reproducible state. |
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. |
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.