difExprs | R Documentation |

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.

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

`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. |

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

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

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.

`exprMat`

to obtain the expression matrix.

## Loading the expression matrix treat <- c(rep(0,10),rep(1,10)) norm <- read.table(system.file("extdata","expression_example.txt",package = "coexnet")) ## Running the function using both approaches sam <- difExprs(expData = norm,treatment = treat,fdr = 0.2,DifferentialMethod = "sam") acde <- difExprs(expData = norm,treatment = treat,fdr = 0.2,DifferentialMethod = "acde")

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