difExprs {coexnet} | R Documentation

## Differential expression analysis using two different methods.

### Description

Using the expression matrix calculate the differential expressed genes to two class analysis and fixing an expected FDR value. The methods are SAM and ACDE.

### Usage

    difExprs(expData, treatment, fdr, DifferentialMethod, plotting = FALSE)

### Arguments

`expData` | A matrix with the expression matrix, it may be stored in a SummarizedExperiment object.
`treatment` | A vector with the ientifiers of the classes, 0 to control and 1 to case.
`fdr` | The expected FDR value.
`DifferentialMethod` | The method to calculate the differential expressed genes, can be "sam" or "acde"
`plotting` | The option to show the result in a plot. By default FALSE.

### Value

A data.frame with the expression matrix to the expressed diferential genes only.

### Author(s)

Juan David Henao <judhenaosa@unal.edu.co>

### References

Tusher, V. G., Tibshirani, R., & Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences, 98(9), 5116-5121.

Acosta J and Lopez-Kleine L (2015). acde: Artificial Components Detection of Differentially Expressed Genes. R package version 1.4.0.

### See Also

`exprMat` to obtain the expression matrix.

### Examples

    ## Creating the expression matrix

    # The matrix have 200 genes and 20 samples

    n <- 200
    m <- 20

    # The vector with treatment samples and control samples

    treat <- c(rep(0,10),rep(1,10))

    # Calculating the expression values normalized

    mat <- as.matrix(rexp(n, rate = 1))
    norm <- t(apply(mat, 1, function(nm) rnorm(m, mean=nm, sd=1)))

    ## Running the function using the two approaches

    sam <- difExprs(expData = norm,treatment = treat,fdr = 0.2,DifferentialMethod = "sam")
    acde <- difExprs(expData = norm,treatment = treat,fdr = 0.2,DifferentialMethod = "acde")

[Package coexnet version 1.6.0 Index]