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find.a0(data, cl, method = z.find, B = 100, delta = 0.9, quan.a0 = (0:5)/5, include.zero = TRUE, control = find.a0Control(), gene.names = dimnames(data)[[1]], rand = NA, ...)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 naming
the column of pData(data) that contains the class labels of the samples.
In the one-class case, cl should be a vector of 1's.
In the two class unpaired case, cl should be a vector containing 0's
(specifying the samples of, e.g., the control group) and 1's (specifying,
e.g., the case group).
In the two class paired case, cl can be either a numeric vector or
a numeric matrix. If it is a vector, then cl has to consist of the
integers between -1 and $-n/2$ (e.g., before treatment group) and between
1 and $n/2$ (e.g., after treatment group), where $n$ is the length of
cl and $k$ is paired with $-k$, $k=1,\dots,n/2$. If cl
is a matrix, one column should contain -1's and 1's specifying, e.g., the before
and the after treatment samples, respectively, and the other column should
contain integer between 1 and $n/2$ specifying the $n/2$ pairs of
observations.
In the multiclass case and if method = cat.stat, cl should be a
vector containing integers between 1 and $g$, where $g$ is the number
of groups.
For examples of how cl can be specified, see the manual of siggenes.z.find
provides these objects for t- and F-statistics. It is, however, also possible
to employ an user-written function. For how to write such a function, see the
vignette of siggenes.delta are called differentially expressed.find.a0.
For these arguments, see find.a0Control.nrow(data) containing the
names of the genes. By default, the row names of data are used.NA, the random number generator
will be set into a reproducible state.fun. For
further arguments of fun = z.find, see z.find.delta.
Actually, only the genes having a posterior probability larger than delta
are called differentially expressed that do not exhibit a test score less extreme
than the score of a gene whose posterior probability is less than delta.
So, let's say, we have done an EBAM analysis with a t-test and we have ordered
the genes by their t-statistic. Let's further assume that Gene 1 to Gene 5 (i.e.
the five genes with the lowest t-statistics), Gene 7 and 8, Gene 3012 to 3020,
and Gene 3040 to 3051 are the only genes that show a posterior probability larger
than delta. Then, Gene 1 to 5, and 3040 to 3051 are called differentially
expressed, but Gene 7 and 8, and 3012 to 3020 are not called differentially
expressed, since Gene 6 and Gene 3021 to 3039 show a posterior probability less
than delta.
ebam, FindA0-class, find.a0Control