"plotMD"(object, column = 1, xlab = "Average log-expression", ylab = "Expression log-ratio (this sample vs others)", main = colnames(object)[column], status=NULL, ...)
"plotMD"(object, column = 1, array = NULL, xlab = "Average log-expression", ylab = "Expression log-ratio (this sample vs others)", main = colnames(object)[column], status=object$genes$Status, zero.weights = FALSE, ...)
"plotMD"(object, column = 1, array = NULL, xlab = "A", ylab = "M", main = colnames(object)[column], status=object$genes$Status, zero.weights = FALSE, ...)
"plotMD"(object, column = 1, array = NULL, xlab = "A", ylab = "M", main = colnames(object)[column], status=object$genes$Status, zero.weights = FALSE, ...)
"plotMD"(object, column = ncol(object), coef = NULL, xlab = "Average log-expression", ylab = "log-fold-change", main = colnames(object)[column], status=object$genes$Status, zero.weights = FALSE, ...)RGList, MAList, EList, ExpressionSet or MArrayLM object.
Alternatively a numeric matrix.object to be plotted.column for microarray data objects. If specified, then column is ignored.column for fitted model objects. If specified, then column is ignored.object.
If NULL, then all points are plotted in the default color, symbol and size.plotWithHighlights.mdplot) with the red and green log2-intensities of the array providing the two columns.For single channel data objects, a between-array MD-plot is produced. An articifial array is produced by averaging all the arrays other than the array specified. A mean-difference plot is then producing from the specified array and the artificial array. Note that this procedure reduces to an ordinary mean-difference plot when there are just two arrays total.
If object is an MArrayLM object, then the plot is an fitted model MD-plot in which the estimated coefficient is on the y-axis and the average A-value is on the x-axis.
The status vector can correspond to any grouping of the probes that is of interest.
If object is a fitted model object, then status vector is often used to indicate statistically significance, so that differentially expressed points are highlighted.
If object is a microarray data object, then status might distinguish control probes from regular probes so that different types of controls are highlighted.
The status can be included as the component object$genes$Status instead of being passed as an argument to plotMD.
See plotWithHighlights for how to set colors and graphics parameters for the highlighted and non-highlighted points.
plotMD is plotWithHighlights.
See also mdplot for a very basic mean-difference plot function.An overview of plot functions available in LIMMA is given in 09.Diagnostics.
A <- runif(1000,4,16)
y <- A + matrix(rnorm(1000*3,sd=0.2),1000,3)
status <- rep(c(0,-1,1),c(950,40,10))
y[,1] <- y[,1] + status
plotMD(y, column=1, status=status, values=c(-1,1), hl.col=c("blue","red"))
MA <- new("MAList")
MA$A <- runif(300,4,16)
MA$M <- rt(300,df=3)
# Spike-in values
MA$M[1:3] <- 0
MA$M[4:6] <- 3
MA$M[7:9] <- -3
status <- rep("Gene",300)
status[1:3] <- "M=0"
status[4:6] <- "M=3"
status[7:9] <- "M=-3"
values <- c("M=0","M=3","M=-3")
hl.col <- c("blue","red","green")
plotMD(MA,main="MA-Plot with 12 spiked-in points",
status=status, values=values, hl.col=hl.col)
# Same as above but setting graphical parameters as attributes
attr(status,"values") <- values
attr(status,"col") <- hl.col
plotMD(MA, main="Mean-Difference Plot with 12 spiked-in points", status=status)
# Same as above but passing status as part of object
MA$genes$Status <- status
plotMD(MA, main="Mean-Difference Plot with 12 spiked-in points")
# Change settings for background points
MA$genes$Status <- status
plotMD(MA, bg.pch=1, bg.cex=0.5)
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