plotMeanDiff: Construct an MA (mean-difference) plot

In fmicompbio/mutscan: Preprocessing and Analysis of Deep Mutational Scanning Data

Construct an MA (mean-difference) plot

Description

Construct an MA (mean-difference) plot

Usage

plotMeanDiff(
  res,
  meanCol = NULL,
  logFCCol = NULL,
  pvalCol = NULL,
  padjCol = NULL,
  padjThreshold = 0.05,
  pointSize = "small",
  interactivePlot = FALSE,
  nTopToLabel = 0
)

Arguments

res	data.frame (typically output from calculateRelativeFC()) with columns corresponding to the average abundance (logCPM or AveExpr), log-fold change (logFC) and significance (FDR or adj.P.Val).
meanCol, logFCCol, pvalCol, padjCol	Character scalars indicating the columns from res that will be used to represent the mean value (x-axis), logFC (y-axis), nominal p-value (used to find the top features to label) and adjusted p-value (used for coloring). If NULL (default), pre-specified values will be used depending on the available columns ("logCPM" or "AveExpr", "logFC", "PValue" or "P.Value", and "FDR" or "adj.P.Val", respectively).
padjThreshold	Numeric scalar indicating the adjusted p-value threshold to use for coloring the points. All features with adjusted p-value below the treshold will be shown in red.
pointSize	Either "small" or "large", indicating which of the two available plot styles that will be used.
interactivePlot	Logical scalar, indicating whether an interactive plot should be returned, in which one can hover over the individual points and obtain further information.
nTopToLabel	Numeric scalar, indicating the number of points that should be labeled in the plot. The points will be ranked by the pvalCol column, and the top nTopToLabel values will be labeled by the corresponding row names. Only used if interactivePlot is FALSE.

Value

If interactivePlot is TRUE, a plotly object. If interactivePlot is FALSE, a ggplot2 object.

Author(s)

Charlotte Soneson

Examples

se <- readRDS(system.file("extdata", "GSE102901_cis_se.rds",
                          package = "mutscan"))[1:200, ]
design <- model.matrix(~ Replicate + Condition,
                       data = SummarizedExperiment::colData(se))
res <- calculateRelativeFC(se, design, coef = "Conditioncis_output")
plotMeanDiff(res, pointSize = "large", nTopToLabel = 3)

fmicompbio/mutscan documentation built on March 30, 2024, 9:13 a.m.