plot,MessinaClassResult,missing-method {messina} | R Documentation

## Plot the results of a Messina analysis on a classification / differential expression problem.

### Description

Produces a separate plot for each supplied feature index (either as an index into the expression data x as-supplied, or as an index into the features sorted by Messina margin, depending on the value of sort_features), showing sample expression levels, group membership, threshold value, and margin locations. Two different types of plots can be produced. See the vignette for examples.

### Usage

    ## S4 method for signature 'MessinaClassResult,missing'
    plot(x, y, ...)

### Arguments

`x` | the result of a Messina analysis, as returned by functions `messina` or `messinaDE`.
`...` | additional options to control the plot: `indices` a vector of indices of features to plot. If sort_features == FALSE, the indices are into the unsorted features, as originally supplied in x supplied to messina or messinaDE. If sort_features == TRUE, features are first sorted in order of decreasing margin, and then the indices in this parameter are plotted. For example, if indices == 2 and sort_features == FALSE, the second feature in x will be plotted. However, if sort_features == TRUE, the feature with the second best classifier margin will be plotted. `sort_features` a boolean indicating whether to sort features by decreasing margin size before selecting from indices. This affects the interpretation of the parameter 'indices'; for more details see the description of that parameter. `plot_type` a string giving the type of plot to produce, either "point" or "bar". "bar" is the default, and shows expression levels as horizontal bars. Although this representation is familiar, it can be misleading in the case of log-transformed data. In that case, the "point" plot type is preferable.
`y` | the y coordinates of points in the plot, optional if `x` is an appropriate structure.

### Author(s)

Mark Pinese m.pinese@garvan.org.au

### See Also

`MessinaClassResult-class`

`messina`

`messinaDE`

### Examples

    ## Load some example data
    library(antiProfilesData)
    data(apColonData)

    x = exprs(apColonData)
    y = pData(apColonData)$SubType

    ## Subset the data to only tumour and normal samples
    sel = y %in% c("normal", "tumor")
    x = x[,sel]
    y = y[sel]

    ## Run Messina to rank probesets on their classification ability, with
    ## classifiers needing to meet a minimum sensitivity of 0.95, and minimum
    ## specificity of 0.85.
    fit = messina(x, y == "tumor", min_sens = 0.95, min_spec = 0.85)

    ## Make bar plots of the five best fits
    plot(fit, indices = 1:5, sort_features = TRUE, plot_type = "bar")

    ## Make a point plot of the fit to the 10th feature
    plot(fit, indices = 10, sort_features = FALSE, plot_type = "point")

[Package messina version 1.30.0 Index]