d.stat(data, cl, var.equal = FALSE, B = 100, med = FALSE, s0 = NA, s.alpha = seq(0, 1, 0.05), include.zero = TRUE, n.subset = 10, mat.samp = NULL, B.more = 0.1, B.max = 30000, gene.names = NULL, R.fold = 1, use.dm = TRUE, R.unlog = TRUE, na.replace = TRUE, na.method = "mean", rand = NA)ExpressionSet object. Each row of
data (or exprs(data), respectively) must correspond to a variable (e.g., a gene),
and each column to a sample (i.e.\ an observation).ncol(data) containing the class
labels of the samples. In the two class paired case, cl can also
be a matrix with ncol(data) rows and 2 columns. If data is
an ExpressionSet object, cl can also be a character string.
For details on how cl should be specified, see ?sam.FALSE (default), Welch's t-statistic will be computed.
If TRUE, the pooled variance will be used in the computation of
the t-statistic.FALSE (default), the mean number of falsely called genes
will be computed. Otherwise, the median number is calculated.NA (default),
s0 will be computed automatically.s0. If
s.alpha is a vector, the fudge factor is computed as proposed by
Tusher et al. (2001). Otherwise, the quantile of the standard deviations
specified by s.alpha is used as fudge factor.TRUE, s0 = 0 will also be a possible choice
for the fudge factor. Hence, the usual t-statistic or F statistic, respectively,
can also be a possible choice for the expression score $d$. If FALSE,
s0=0 will not be a possible choice for the fudge factor. The latter
follows Tusher et al. (2001) definition of the fudge factor in which only strictly
positive values are considered.med = TRUE, n.subset will be set to 1.ncol(data) columns except for the two class
paired case in which mat.samp has ncol(data)/2 columns.
Each row specifies one permutation of the group labels used in the computation
of the expected expression scores $d.bar$. If not specified
(mat.samp=NULL), a matrix having B rows and ncol(data) is
generated automatically and used in the computation of $d.bar$. In
the two class unpaired case and the multiclass case, each row of mat.samp
must contain the same group labels as cl. In the one class and the two
class paired case, each row must contain -1's and 1's. In the one class case,
the expression values are multiplied by these -1's and 1's. In the two class paired
case, each column corresponds to one observation pair whose difference is multiplied
by either -1 or 1. For more details and examples, see the manual of siggenes.B.more)*B, full permutation will be done.
Otherwise, B permutations are used. This avoids that B permutations
will be used -- and not all permutations -- if the number of all possible permutations
is just a little larger than B.nrow(data) containing the
names of the genes.B.max, B randomly selected permutations will be used
in the computation of the null distribution. Otherwise, B random draws
of the group labels are used. In the latter way of permuting it is possible that
some of the permutations are used more than once.R.fold, or larger than or equal to 1/R.fold,respectively,
then this gene will be excluded from the SAM analysis. The expression score
$d$ of excluded genes is set to NA. By default, R.fold
is set to 1 such that all genes are included in the SAM analysis. Setting
R.fold to 0 or a negative value will avoid the computation of the fold
change. The fold change is only computed in the two-class unpaired cases.TRUE, the fold change is computed by 2 to the power of the difference between
the mean log2 intensities of the two groups, i.e.\ 2 to the power of the numerator of the test statistic.
If FALSE, the fold change is determined
by computing 2 to the power of data (if R.unlog = TRUE) and then calculating the ratio of the
mean intensity in the group coded by 1 to the mean intensity in the group coded
by 0. The latter is the definition of the fold change used in Tusher et al.\ (2001).TRUE, the anti-log of data will be used in the computation of the
fold change. Otherwise, data is used. This transformation should be done
when data is log2-tranformed (in a SAM analysis it is highly recommended
to use log2-transformed expression data). Ignored if use.dm = TRUE.TRUE, missing values will be removed by the genewise/rowwise
statistic specified by na.method. If a gene has less than 2 non-missing
values, this gene will be excluded from further analysis. If na.replace=FALSE,
all genes with one or more missing values will be excluded from further analysis.
The expression score $d$ of excluded genes is set to NA.na.replace=TRUE. Must be either "mean" (default)
or median.NA, the random number generator
will be set into a reproducible state.Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. PNAS, 98, 5116-5121.
SAM-class,sam, z.ebam