diffAnalysis: Differential analysis.
In PAA: PAA (Protein Array Analyzer)

Description Usage Arguments Details Value Author(s) Examples

Performs a univariate differential analysis.

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diffAnalysis(input=NULL, label1=NULL, label2=NULL, class1=NULL, class2=NULL,
output.path=NULL, mMs.matrix1=NULL, mMs.matrix2=NULL, above=1500,
between=400, features=NULL, feature.names=NULL)

`input`	`EList$E`- or `EListRaw$E`-matrix extended by row names comprising BRC-IDs of the corresponding features (mandatory; note: it is expected that this matrix is in original scale and not in log2 scale).
`label1`	vector of column names for group 1 (mandatory).
`label2`	vector of column names for group 2 (mandatory).
`class1`	label of group 1 (mandatory).
`class2`	label of group 2 (mandatory).
`output.path`	string indicating a path for saving the results (optionally).
`mMs.matrix1`	precomputed mMs reference matrix (see `mMsMatrix()`) for group 1 (mandatory).
`mMs.matrix2`	precomputed mMs reference matrix (see `mMsMatrix()`) for group 2 (mandatory).
`above`	mMs above parameter (integer). Default is `"1500"`.
`between`	mMs between parameter (integer). Default is `"400"`.
`features`	vector of row indices (optional).
`feature.names`	vector of corresponding feature names (additionally to `features`).

This function takes an EList$E- or EListRaw$E-matrix (e.g., temp <- elist$E) extended by row names comprising BRC-IDs of the corresponding features. The BRC-IDs can be created via:
brc <- paste(elist$genes[,1], elist$genes[,3], elist.$genes[,2]).
The BRC-row names can be defined as follows: rownames(temp) <- brc. Furthermore, the corresponding column name vectors, group labels and mMs-parameters are needed to perform the univariate differential analysis. This analysis covers inter alia p-value computation, p-value adjustment (method: Benjamini & Hochberg, 1995), and fold change computation. Since the results table is usually large, a path for saving the results can be defined via output.path. Optionally, a vector of row indices (features) and additionally (not mandatory for subset analysis) a vector of corresponding feature names (feature.names) can be forwarded to perform the analysis for a feature subset.

A matrix containing the analysis results 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="")
mMs.matrix1 <- mMs.matrix2 <- mMsMatrix(x=20, y=20)
temp <- elist$E
rownames(temp) <- paste(elist$genes[,1], elist$genes[,3], elist$genes[,2])
diffAnalysis(input=temp, label1=c1, label2=c2, class1="AD", class2="NDC",
 mMs.matrix1=mMs.matrix1, mMs.matrix2=mMs.matrix2, above=1500,
 between=400)