Description Usage Arguments Value Author(s) References See Also Examples

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.

1 |

`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.

1 2 3 4 5 6 7 8 9 | ```
## 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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