RankingBaldiLong(x, y, type = c("unpaired", "paired", "onesample"), m = 100, conf = NULL, pvalues = TRUE, gene.names = NULL, ...)matrix of gene expression values with rows
corresponding to genes and columns corresponding to observations or alternatively an object of class ExpressionSet.
If type = paired, the first half of the columns corresponds to
the first measurements and the second half to the second ones.
For instance, if there are 10 observations, each measured twice,
stored in an expression matrix expr,
then expr[,1] is paired with expr[,11], expr[,2]
with expr[,12], and so on.x is a matrix, then y may be
a numeric vector or a factor with at most two levels.
If x is an ExpressionSet, then y
is a character specifying the phenotype variable in
the output from pData.
If type = "paired", take care that the coding is
analogously to the requirement concerning x.
y
is correct (s. above).
y has only one level.
Test whether the true mean is different
from zero.
TRUE.m determines the width of the window
used to obtain an estimate for the average variability of
gene expression for those genes that show a similar expression level.
The argument conf is non-negative and denotes the weight given to the Bayesian prior estimate of within-treatment
variance. Baldi and Long report reasonable performance with this parameter set equal to approximately 3 times the number of
observations, when the number of experimental observations is small (approximately 4 or less).
If the number of replicate experimental observations is large then the confidence value can be lowered
to be equal to the number of observations (or even less).
## Load toy gene expression data
data(toydata)
### class labels
yy <- toydata[1,]
### gene expression
xx <- toydata[-1,]
### run RankingBaldiLong
BaldiLong <- RankingBaldiLong(xx, yy, type="unpaired")
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