pvaluePlot: Draw a p-value plot.
In PAA: PAA (Protein Array Analyzer)

Description Usage Arguments Details Value Author(s) Examples

Draws a p-value plot to visualize the p-values for all features stored in a EList or EListRaw object.

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pvaluePlot(elist=NULL, group1=NULL, group2=NULL, log=NULL, method="tTest",
output.path=NULL, tag="", mMs.matrix1=NULL, mMs.matrix2=NULL, above=1500,
between=400, adjust=FALSE)

`elist`	`EList` or `EListRaw` object (mandatory).
`group1`	vector of column names for group 1 (mandatory).
`group2`	vector of column names for group 2 (mandatory).
`log`	indicates whether the data is in log scale (mandatory; note: if TRUE log2 scale is expected).
`method`	method for p-value computation: `"tTest"` or `"mMs"`. Default is `"tTest"`.
`output.path`	string indicating a path for saving the plot (optional).
`tag`	string that can be used for tagging the saved plot (optional).
`mMs.matrix1`	precomputed M score reference matrix (see `mMsMatrix()`) for group 1 (mandatory when method = "mMs").
`mMs.matrix2`	precomputed M score reference matrix (see `mMsMatrix()`) for group 2 (mandatory when method = "mMs").
`above`	M score above parameter (integer). Default is `"1500"`.
`between`	M score between parameter (integer). Default is `"400"`.
`adjust`	logical indicating whether p-values should be adjusted. Default is `FALSE`.

This function takes an EList or EListRaw object and the corresponding column name vectors to draw a plot of p-values for all features stored in elist (sorted in increasing order and in log2 scale). The p-value computation method ("tTest" or "mMs") can be set via the argument method. Furthermore, when adjust=TRUE adjusted p-values (method: Benjamini & Hochberg, 1995, computed via p.adjust()) will be used. When an output path is defined (via output.path) the plot will be saved as a tiff file.

No value is returned.

Michael Turewicz, michael.turewicz@rub.de

cwd <- system.file(package="PAA")
load(paste(cwd, "/extdata/Alzheimer.RData", sep=""))
elist <- elist[elist$genes$Block < 10,]
c1 <- paste(rep("AD",20), 1:20, sep="")
c2 <- paste(rep("NDC",20), 1:20, sep="")
pvaluePlot(elist=elist, group1=c1, group2=c2, log=FALSE, method="tTest",
 tag="_tTest", adjust=FALSE)