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.
Details
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 differently.
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 the 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.
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.
See Also
ISA and ISAModules.
Examples
data(ALLModulesSmall)
library(ALL)
data(ALL)
col <- ifelse(grepl("^B", ALL$BT), "darkolivegreen", "orange")
condPlot(ALLModulesSmall, 1, ALL, col=col)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(eisa)
Loading required package: isa2
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors
Attaching package: 'S4Vectors'
The following objects are masked from 'package:base':
colMeans, colSums, expand.grid, rowMeans, rowSums
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/eisa/cond.plot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: condPlot
> ### Title: Plot sample scores of a transcription module
> ### Aliases: condPlot
> ### Keywords: cluster
>
> ### ** Examples
>
> data(ALLModulesSmall)
> library(ALL)
> data(ALL)
>
> col <- ifelse(grepl("^B", ALL$BT), "darkolivegreen", "orange")
> condPlot(ALLModulesSmall, 1, ALL, col=col)
>
>
>
>
>
> dev.off()
null device
1
>