diffAnalysis {PAA} | R Documentation

## Differential analysis.

### Description

Performs a univariate differential analysis.

### Usage

    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)

### Arguments

`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`).

### Details

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.

### Value

A matrix containing the analysis results is returned.

### Author(s)

Michael Turewicz, michael.turewicz@rub.de

### Examples

    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)

[Package PAA version 1.16.0 Index]