Plot selection probabilities of predictors — plotSelectionProb • monaLisa

This function plots the selection probabilities of predictors (for example the selected motifs), optionally multiplied with either +1 or -1 to give a sense of both the strength and the directionality of the associated effects. The directionality is estimated from the sign of the correlation coefficient between each predictor and the response vector.

Usage

plotSelectionProb(
  se,
  directional = TRUE,
  selProbMin = metadata(se)$stabsel.params.cutoff,
  selProbMinPlot = 0.4,
  showSelProbMin = TRUE,
  selColor = "cadetblue",
  notSelColor = "grey",
  selProbCutoffColor = "firebrick",
  method = c("pearson", "kendall", "spearman"),
  ylimext = 0.2
)

Arguments

se: The SummarizedExperiment object with the results from stability selection (typically returned by randLassoStabSel).
directional: A logical scalar. If TRUE, selection probabilities are plotted with the sign of the marginal correlation between a predictor and the response.
selProbMin: A numerical scalar in [0,1]. Predictors with a selection probability greater than selProbMin are considered selected and colored by the input from selColor. By default, selProbMin is extracted from the parameters stored in se.
selProbMinPlot: A numerical scalar in [0,1] less than selProbMin. Predictors with a selection probability greater than selProbMinPlot but less than selProbMin are shown as bars with color defined in notSelColor. selProbMinPlot is useful in order to include additional predictors in the barplot, that were not selected according to selProbMin but may be close to that cutoff or are simply nice to visualize alongside the selected predictors. Setting selProbMinPlot = 0 will include all predictors.
showSelProbMin: A logical scalar. If TRUE, the value of selProbMin is shown by a horizontal line with the color defined by selProbCutoffColor.
selColor: Color for the selected predictors which have a selection probability greater than selProbMin.
notSelColor: Color for the rest of the (unselected) predictors which will be show in the barplot.
selProbCutoffColor: Color for the line depicting the selection probability cutoff.
method: A character scalar with the correlation method to use in the calculation of predictor-response marginal correlations. One of "pearson", "kendall" or "spearman" (see cor).
ylimext: A numeric scalar defining how much the y axis limits should be expanded beyond the plotted probabilities to allow for space for the bar labels. This value can be increased if the predictor names above the bars are too long and not showing in the plot.

Value

a ggplot2 object.

Details

This function creates a bar plot with ggplot. Each bar corresponds to a predictor (motif) and the colors correspond to whether or not it was selected. The y-axis shows the selection probabilities (directional=FALSE) or selection probabilities with the sign of the marginal correlation to the response (directional=TRUE).

Examples

## create data set
set.seed(321)
Y <- rnorm(n = 500, mean = 2, sd = 1)
X <- matrix(data = NA, nrow = length(Y), ncol = 50)
for (i in seq_len(ncol(X))) {
  X[ ,i] <- runif(n = 500, min = 0, max = 3)
}
s_cols <- sample(x = seq_len(ncol(X)), size = 10,
  replace = FALSE)
for (s in s_cols) {
  X[ ,s] <- X[, s] + (Y + rnorm(500, 0, 4)) * ifelse(s %% 2, -1, 1)
}

## reproducible randLassoStabSel() with 1 core
set.seed(123)
ss <- randLassoStabSel(x = X, y = Y)
plotSelectionProb(ss)