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 10 11 12 13 14 15 16 17 18 19 20 | ```
## 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")
``` |

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