| bga {made4} | R Documentation |
Discrimination of samples using between group analysis as described by Culhane et al., 2002.
bga(dataset, classvec, type = "coa", ...)
plot.bga(x, axes1=1, axes2=2, arraycol=NULL, genecol="gray25", nlab=10,
genelabels= NULL, ...)
dataset |
Training dataset. A matrix, data.frame,
exprSet or marrayRaw.
If the input is gene expression data in a matrix or data.frame. The
rows and columns are expected to contain the variables (genes) and cases (array samples)
respectively.
|
classvec |
A factor or vector which describes the classes in the training dataset |
type |
Character, "coa", "pca" or "nsc" indicating which data transformation is required. The default value is type="coa" |
x |
An object of class bga. The output from bga or
bga.suppl. It contains the projection coordinates from bga,
the $ls, $co or $li coordinates to be plotted |
arraycol, genecol |
Character, colour of points on plot. If arraycol is NULL,
arraycol will obtain a set of contrasting colours using getcol, for each classes
of cases (microarray samples) on the array (case) plot. genecol is the colour of the
points for each variable (genes) on gene plot |
nlab |
Numeric. An integer indicating the number of variables (genes) at the end of axes to be labelled, on the gene plot. |
axes1 |
Integer, the column number for the x-axis. The default is 1. |
axes2 |
Integer, the column number for the y-axis, The default is 2. |
genelabels |
A vector of variables labels, if genelabels=NULL the row.names
of input matrix dataset will be used |
... |
further arguments passed to or from other methods |
bga performs a between group analysis on the input dataset. This function
calls between. The input format of the dataset
is verified using array2ade4.
Between group analysis is a supervised method for sample discrimination and class prediction.
BGA is carried out by ordinating groups (sets of grouped microarray samples), that is,
groups of samples are projected into a reduced dimensional space. This is most easily
done using PCA or COA, of the group means. The choice of PCA, COA is defined by the parameter type.
The user must define microarray sample groupings in advance. These groupings are defined using
the input classvec, which is a factor or vector.
Cross-validation and testing of bga results:
bga results should be validated using one leave out jack-knife cross-validation using
bga.jackknife and by projecting a blind test datasets onto the bga axes
using suppl.
bga and suppl are combined in bga.suppl
which requires input of both a training and test dataset.
It is important to ensure that the selection of cases for a training and test set are not biased, and
generally many cross-validations should be performed. The function randomiser
can be used to randomise the selection of training and test samples.
Plotting and visualising bga results:
2D plots:
Use plot.bga to plot results from bga. plot.bga calls the functions
s.var and s.groups to draw an xy plot of cases ($ls).
s.var and s.groups are modifications of the ADE4 graphing functions
s.label and s.class.
plotgenes, is used to draw an xy plot of the variables (genes).
3D plots:
3D graphs can be generated using do3D and html3D.
html3D produces a web page in which a 3D plot can be interactively rotated, zoomed,
and in which classes or groups of cases can be easily highlighted.
1D plots, show one axis only:
1D graphs can be plotted using between.graph and
graph1D. between.graph is used for plotting the cases,
and required both the co-ordinates of the cases ($ls) and their centroids ($li). It accepts an object bga.
graph1D can be used to plot either cases (microarrays) or variables (genes) and only requires
a vector of coordinates.
Analysis of the distribution of variance among axes:
It is important to know which cases (microarray samples) are discriminated by the axes.
The number of axes or principal components from a bga will equal the number of classes - 1,
that is length(levels(classvec))-1.
The distribution of variance among axes is described in the eigenvalues ($eig) of the bga analysis.
These can be visualised using a scree plot, using scatterutil.eigen as it done in plot.bga.
It is also useful to visualise the principal components from a using a bga or principal components analysis
dudi.pca, or correspondence analysis dudi.coa using a
heatmap. In MADE4 the function heatplot will plot a heatmap with nicer default colours.
Extracting list of top variables (genes):
Use topgenes to get list of variables or cases at the ends of axes. It will return a list
of the top n variables (by default n=5) at the positive, negative or both ends of an axes.
sumstats can be used to return the angle (slope) and distance from the origin of a list of
coordinates.
For more details see Culhane et al., 2002 and http://bioinf.ucd.ie/research/BGA.
A list with a class bga containing:
ord |
Results of initial ordination. A list of class "dudi" (see dudi ) |
bet |
Results of between group analysis. A list of class "dudi" (see dudi),
"between" (see between) |
fac |
The input classvec, the factor or vector which described the classes in the input dataset |
Aedin Culhane
Culhane AC, et al., 2002 Between-group analysis of microarray data. Bioinformatics. 18(12):1600-8.
See Also bga,
suppl, suppl.bga, between,
bga.jackknife
data(khan)
if (require(ade4, quiet = TRUE)) {
khan.bga<-bga(khan$train, classvec=khan$train.classes)
}
khan.bga
plot(khan.bga, genelabels=khan$annotation$Symbol)
# Provide a view of the principal components (axes) of the bga
heatplot(khan.bga$bet$ls, dend=FALSE,lowcol="blue")