MA-Plot of Expression Data

Description

Creates an MA-plot with color coding for control spots.

Usage

## Default S3 method:
plotMA(object, array = 1, xlab = "Average log-expression",
       ylab = "Expression log-ratio (this sample vs others)",
       main = colnames(object)[array], status=NULL, ...)
## S3 method for class 'EList'
plotMA(object, array = 1, xlab = "Average log-expression",
       ylab = "Expression log-ratio (this sample vs others)",
       main = colnames(object)[array], status=object$genes$Status,
       zero.weights = FALSE, ...)
## S3 method for class 'RGList'
plotMA(object, array = 1, xlab = "A", ylab = "M",
       main = colnames(object)[array], status=object$genes$Status,
       zero.weights = FALSE, ...)
## S3 method for class 'MAList'
plotMA(object, array = 1, xlab = "A", ylab = "M",
       main = colnames(object)[array], status=object$genes$Status,
       zero.weights = FALSE, ...)
## S3 method for class 'MArrayLM'
plotMA(object, coef = ncol(object), xlab = "Average log-expression",
       ylab = "log-fold-change", main = colnames(object)[coef],
       status=object$genes$Status, zero.weights = FALSE, ...)

Arguments

Details

An MA-plot is a plot of log-intensity ratios (M-values) versus log-intensity averages (A-values). See Ritchie et al (2015) for a brief historical review.

For two color data objects, a within-array MA-plot is produced with the M and A values computed from the two channels for the specified array. This is the same as a mean-difference plot (mdplot) with the red and green log2-intensities of the array providing the two columns.

For single channel data objects, a between-array MA-plot is produced. An artificial 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 MA-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 plotMA.

See plotWithHighlights for how to set colors and graphics parameters for the highlighted and non-highlighted points.

Value

A plot is created on the current graphics device.

Note

The plotMD function provides the same functionality as plotMA with slightly different arguments.

Author(s)

Gordon Smyth

References

Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, and Smyth, GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research Volume 43, e47. http://nar.oxfordjournals.org/content/43/7/e47

See Also

The driver function for plotMA is plotWithHighlights.

An overview of plot functions available in LIMMA is given in 09.Diagnostics.

Examples

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
plotMA(y, array=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")
col <- c("blue","red","green")

plotMA(MA,main="MA-Plot with 12 spiked-in points",
       status=status, values=values, hl.col=col)

#  Same as above but setting graphical parameters as attributes
attr(status,"values") <- values
attr(status,"col") <- col
plotMA(MA, main="MA-Plot with 12 spiked-in points", status=status)

#  Same as above but passing status as part of object
MA$genes$Status <- status
plotMA(MA, main="MA-Plot with 12 spiked-in points")

#  Change settings for background points
MA$genes$Status <- status
plotMA(MA, bg.pch=1, bg.cex=0.5)