SAM Analysis Using Wilcoxon Rank Statistics
Generates the required statistics for a Significance Analysis of Microarrays
analysis using standardized Wilcoxon rank statistics.
Should not be called directly, but via sam(..., method = wilc.stat).
wilc.stat(data, cl, gene.names = NULL, R.fold = 1, use.dm = FALSE,
R.unlog = TRUE, na.replace = TRUE, na.method = "mean",
approx50 = TRUE, ties.method=c("min","random","max"),
use.row = FALSE, rand = NA)
a matrix or a data frame. Each row of
data must correspond to a variable (e.g., a gene),
and each column to a sample (i.e.\ an observation).
a numeric vector of length
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. For details
cl should be specified, see
a character vector of length
nrow(data) containing the
names of the genes.
a numeric value. If the fold change of a gene is smaller than or
R.fold, or larger than or equal to 1/
then this gene will be excluded from the SAM analysis. The expression score
d of excluded genes is set to
NA. By default,
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 case.
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.
FALSE, the fold change is determined
by computing 2 to the power of
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 default, as this definition of the fold change is 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
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
a character string naming the statistic with which missing values
will be replaced if
na.replace=TRUE. Must be either
TRUE, the null distribution will be approximated by
the standard normal distribution. Otherwise, the exact null distribution is
computed. This argument will automatically be set to
FALSE if there
are less than 50 samples in each of the groups.
"random", the ranks of ties are randomly assigned. If
the ranks of ties are set to the minimum or maximum rank, respectively. For details,
see the help of
use.row = TRUE,
ties.method = "max"
will be used. For the handling of Zeros, see Details.
rowWilcoxon is used to compute the Wilcoxon
numeric value. If specified, i.e. not
NA, the random number generator
will be set into a reproducible state.
Standardized versions of the Wilcoxon rank statistics are computed. This means that
W* = (W - mean(W)) / sd(W) is used as expression
score d, where W is the usual Wilcoxon rank sum statistic or Wilcoxon
signed rank statistic, respectively.
In the computation of these statistics, the ranks of ties are by default set to the
minimum rank. In the computation of the Wilcoxon signed rank statistic, zeros are randomly
set either to a very small positive or negative value.
If there are less than 50 observations in each of the groups, the exact null distribution
will be used. If there are more than 50 observations in at least one group, the null
distribution will by default be approximated by the standard normal distribution. It is,
however, still possible to compute the exact null distribution by setting
A list containing statistics required by
Holger Schwender, firstname.lastname@example.org
Schwender, H., Krause, A. and Ickstadt, K. (2003). Comparison of
the Empirical Bayes and the Significance Analysis of Microarrays.
Technical Report, SFB 475, University of Dortmund, Germany.
Tusher, V.G., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays
applied to the ionizing radiation response. PNAS, 98, 5116-5121.