condPlot {eisa} | R Documentation |
Creates a barplot of sample (=condition) scores, for a given transcription module. See details below.
condPlot (modules, number, eset, col = "white", all = TRUE, sep = NULL, sepcol = "grey", val = TRUE, srt = 90, adj.above = c(0, 0.5), adj.below = c(1, 0.5), plot.only = seq_len(ncol(eset)), ...)
modules |
An ISAModules object. |
number |
An integer scalar, the module to plot. |
eset |
An ExpressionSet or ISAExpressionSet
object. This is needed for calculating the scores of the samples
that are not in the module, see the all argument. If an
ExpressionSet object is supplied, then it is normalised by
calling ISANormalize on it. |
col |
Color of the bars, it it passed to
barplot , so it can be any format
barplot accepts. E.g. it can be a character
vector with different colors for the different bars. |
all |
Logical scalar, whether to plot all samples (if
TRUE , the default), or just the ones that are included in the
module. |
sep |
NULL or a numeric vector. If not NULL , then
the bars are separated at the given positions with vertical
lines. This is useful if you want to subdivide the samples into
groups. |
sepcol |
The color of the separating lines (see the sep
argument), if they are plotted. |
val |
Logical scalar, whether to add labels with the actual score values. |
srt |
Numeric scalar, the rotation angle of the text labels, this
is passed to the text function. |
adj.above |
Adjustment of the text labels that are above the
bars. This is passed to text , see its manual
for details. |
adj.below |
Adjustments of the text labels that are below the
bars. This is passed to text , see its manual
for details. |
plot.only |
Numeric vector, if supplied it is used to plot a subset of samples only. By default all samples are plotted. |
... |
Additional argument, to be passed to
barplot . |
condPlot
creates a barplot for the sample scores of an ISA
transcription module. Each sample is represented as a bar.
These plots are useful if you want to show that a given transcription module separates the samples into two (or more) groups. You can assign different colors to the samples, based on some external information, e.g. case and control samples can be colored separately.
In most cases the scores are between minus one and one, but this is not necessarily true.
It is possible to assign scores to samples that are not part of the
module, but this requires performing one (more precisely half) ISA
iteration step. Currently that function always performs this extra
step, even if the out-of-module samples are not plotted. Because the
sample scores in a module are only approximately constant during ISA
iteration, it might be possible that the plotted scores are slightly
different than the ones stored in the modules
variable.
None.
Gabor Csardi Gabor.Csardi@unil.ch
Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of large-scale gene expression data Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.
ISA
and ISAModules
.
data(ALLModulesSmall) library(ALL) data(ALL) col <- ifelse(grepl("^B", ALL$BT), "darkolivegreen", "orange") condPlot(ALLModulesSmall, 1, ALL, col=col)