plot,MessinaClassResult,missing-method {messina} | R Documentation |
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.
## S4 method for signature 'MessinaClassResult,missing' plot(x, y, ...)
x |
the result of a Messina analysis, as returned by
functions |
... |
additional options to control the plot:
|
y |
the y coordinates of points in the plot,
optional if |
Mark Pinese m.pinese@garvan.org.au
## 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")