plotRLDF {limma} | R Documentation

## Plot of regularized linear discriminant functions for microarray data

### Description

Plot regularized linear discriminant functions for classifying samples based on expression data.

### Usage

    plotRLDF(y, design = NULL, z = NULL, nprobes = 100, plot = TRUE,
             labels.y = NULL, labels.z = NULL, pch.y = NULL, pch.z = NULL,
             col.y = "black", col.z = "black",
             show.dimensions = c(1,2), ndim = max(show.dimensions),
             var.prior = NULL, df.prior = NULL, trend = FALSE, robust = FALSE, ...)

### Arguments

`y` | the training dataset. Can be any data object which can be coerced to a matrix, such as `ExpressionSet` or `EList`.
`design` | design matrix defining the training groups to be distinguished. The first column is assumed to represent the intercept. Defaults to `model.matrix(~factor(labels.y))`.
`z` | the dataset to be classified. Can be any data object which can be coerced to a matrix, such as `ExpressionSet` or `EList`. Rows must correspond to rows of `y`.
`nprobes` | number of probes to be used for the calculations. The probes will be selected by moderated F statistic.
`plot` | logical, should a plot be created?
`labels.y` | character vector of sample names or labels in `y`. Defaults to `colnames(y)` or failing that to `1:n`.
`labels.z` | character vector of sample names or labels in `z`. Defaults to `colnames(z)` or failing that to `letters[1:n]`.
`pch.y` | plotting symbol or symbols for `y`. See `points` for possible values. Takes precedence over `labels.y` if both are specified.
`pch.z` | plotting symbol or symbols for `y`. See `points` for possible values. Takes precedence over `labels.z` if both are specified.
`col.y` | colors for the plotting `labels.y`.
`col.z` | colors for the plotting `labels.z`.
`show.dimensions` | integer vector of length two indicating which two discriminant functions to plot. Functions are in decreasing order of discriminatory power.
`ndim` | number of discriminant functions to compute
`var.prior` | prior variances, for regularizing the within-group covariance matrix. By default is estimated by `squeezeVar`.
`df.prior` | prior degrees of freedom for regularizing the within-group covariance matrix. By default is estimated by `squeezeVar`.
`trend` | logical, should a trend be estimated for `var.prior`? See `eBayes` for details. Only used if `var.prior` or `df.prior` are `NULL`.
`robust` | logical, should `var.prior` and `df.prior` be estimated robustly? See `eBayes` for details. Only used if `var.prior` or `df.prior` are `NULL`.
`...` | any other arguments are passed to `plot`.

### Details

The function builds discriminant functions from the training data (`y`) and applies them to the test data (`z`). The method is a variation on classifical linear discriminant functions (LDFs), in that the within-group covariance matrix is regularized to ensure that it is invertible, with eigenvalues bounded away from zero. The within-group covariance matrix is squeezed towards a diagonal matrix with empirical Bayes posterior variances as diagonal elements.

The calculations are based on a filtered list of probes. The `nprobes` probes with largest moderated F statistics are used to discriminate.

The `ndim` argument allows all required LDFs to be computed even though only two are plotted.

### Value

If `plot=TRUE` a plot is created on the current graphics device. A list containing the following components is (invisibly) returned:

`training` | numeric matrix with `ncol(y)` rows and `ndim` columns containing discriminant functions evaluated for the training data.
`predicting` | numeric matrix with `ncol(z)` rows and `ndim` columns containing discriminant functions evalulated on the classification data.
`top` | integer vector of length `nprobes` giving indices of probes used.
`metagenes` | numeric matrix with `nprobes` rows and `ndim` columns containing probe weights defining each discriminant function.
`singular.values` | singular.values showing the predictive power of each discriminant function.
`rank` | maximum number of discriminant functions with singular.values greater than zero.
`var.prior` | numeric vector of prior variances.
`df.prior` | numeric vector of prior degrees of freedom.

### Note

The default values for `df.prior` and `var.prior` were changed in limma 3.27.10. Previously these were preset values. Now the default is to estimate them using `squeezeVar`.

### Author(s)

Gordon Smyth, Di Wu and Yifang Hu

### See Also

`lda` in package `MASS`

### Examples

    # Simulate gene expression data for 1000 probes and 6 microarrays.
    # Samples are in two groups
    # First 50 probes are differentially expressed in second group
    sd <- 0.3*sqrt(4/rchisq(1000,df=4))
    y <- matrix(rnorm(1000*6,sd=sd),1000,6)
    rownames(y) <- paste("Gene",1:1000)
    y[1:50,4:6] <- y[1:50,4:6] + 2

    z <- matrix(rnorm(1000*6,sd=sd),1000,6)
    rownames(z) <- paste("Gene",1:1000)
    z[1:50,4:6] <- z[1:50,4:6] + 1.8
    z[1:50,1:3] <- z[1:50,1:3] - 0.2

    design <- cbind(Grp1=1,Grp2vs1=c(0,0,0,1,1,1))
    options(digit=3)

    # Samples 1-6 are training set, samples a-f are test set:
    plotRLDF(y, design, z=z, col.y="black", col.z="red")
    legend("top", pch=16, col=c("black","red"), legend=c("Training","Predicted"))

[Package limma version 3.42.2 Index]