sam {siggenes} | R Documentation |
Performs a Significance Analysis of Microarrays (SAM) for a set of positive thresholds.
sam(data,x,y,paired=FALSE,mat.samp=NULL,B=100,balanced=FALSE, na.rm=FALSE,s0=NA,alpha.s0=seq(0,1,.05),include.s0=TRUE,factor.s0= 1.4826,p0=NA,lambda.p0=1,vec.lambda.p0=(0:95)/100,delta.fdr= (1:10)/5,med.fdr=TRUE,graphic.fdr=TRUE,thres.fdr=seq(0.5,2,.5), pty.fdr=TRUE,help.fdr=TRUE,ngenes=NA,iteration=3,initial.delta= c(0.1,seq(.2,2,.2),4),rand=NA)
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 the data set 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: n1. |
y |
vector of the columns of the data set 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.
Default is B=100 . |
balanced |
if TRUE , balanced permutations will be used. Default is FALSE . |
na.rm |
if FALSE (default), the expression scores d 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. |
s0 |
the fudge factor. If NA (default), the fudge factor s0 will be computed
automatically. |
alpha.s0 |
the possible values of the fudge factor s0 in terms of quantiles of the standard deviations of the genes. |
include.s0 |
if TRUE (default), s0=0 is a possible choice for the
fudge factor. |
factor.s0 |
constant with which the MAD is multiplied in the computation of the fudge factor. |
p0 |
the probability that a gene is not differentially expressed. If not specified (default), it will be computed. |
lambda.p0 |
number between 0 and 1 that is used to estimate p0.
If set to 1 (default), the automatic p0 selection using
the natural cubic spline fit is used. |
vec.lambda.p0 |
vector of values for λ used in the automatical computation of p0. |
delta.fdr |
a vector of values for the threshold Delta for which the SAM analysis is performed. |
med.fdr |
if TRUE (default), the median number, otherwise the expected
number, of falsely called genes will be computed. |
graphic.fdr |
if TRUE (default), both the SAM plot and the plots of Delta vs.
FDR and Delta vs. number of significant genes will be generated. |
thres.fdr |
for each value contained in thres.fdr , two lines parallel
to the 45-degree line are generated in the SAM plot. |
pty.fdr |
if TRUE (default), a square SAM Plot will be generated. |
help.fdr |
if TRUE (default), help-lines will be drawn in both Delta
plots. |
ngenes |
a number or proportion of genes for which the FDR is estmated. |
iteration |
the number of iterations used in the estimation of the FDR for a given number or proportion of genes. |
initial.delta |
a set of initial guesses for Delta in the computation of the FDR for a given number or proportion of genes. |
rand |
if specified, the random number generator will be put in a reproducible state. |
a table of statistics (estimate of p0, number of significant genes, number of falsely called genes and FDR) for the specified set of Deltas, a SAM Plot, a Delta vs. FDR plot, and a plot of Delta vs. the number of significant genes.
For further analyses with sam.plot
, the results of sam
must be assigned
to an object.
Holger Schwender holger.schw@gmx.de
Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response, PNAS, 98, 5116-5121.
Storey, J.D. (2002). A direct approach to the false discovery rate, Journal of the Royal Statistical Society, Series B, 64, 479-498.
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 5, Diploma thesis, Department of Statistics, University of Dortmund, http://de.geocities.com/holgerschw/thesis.pdf.