bga {made4} | R Documentation

## Between group analysis

### Description

Discrimination of samples using between group analysis as described by Culhane et al., 2002.

### Usage

    bga(dataset, classvec, type = "coa", ...)
    ## S3 method for class 'bga'
    plot(x, axis1=1, axis2=2, arraycol=NULL, genecol="gray25", nlab=10,
             genelabels= NULL, ...)

### Arguments

`dataset` | Training dataset. A `matrix`, `data.frame`, `ExpressionSet` or `marrayRaw-class`. 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.
`axis1` | Integer, the column number for the x-axis. The default is 1.
`axis2` | 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.

### Details

`bga` performs a between group analysis on the input dataset. This function calls `bca`. The input format of the dataset is verified using `isDataFrame`.

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: 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.

2D plots: Use `plot.bga` to plot results from `bga`. plot.bga calls the functions `plotarrays` to draw an xy plot of cases (\$ls). `plotgenes`, is used to draw an xy plot of the variables (genes). `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.

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.

### Value

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 `bca`)
`fac` | The input classvec, the `factor` or `vector` which described the classes in the input dataset

### Author(s)

Aedin Culhane

### References

Culhane AC, et al., 2002 Between-group analysis of microarray data. Bioinformatics. 18(12):1600-8.

### See Also

See Also `bga`, `suppl`, `suppl.bga`, `bca`, `bga.jackknife`

### Examples

    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="none")

[Package made4 version 1.64.0 Index]