This function performs normalization and/or log2 transformation on gene expression data.

1 | ```
preprocess(exprsObj,log2=TRUE, norm="ALL", destname=NULL)
``` |

`exprsObj` |
An eSet object where its assay data will be normalized |

`log2` |
Performs logarithmic transformation of base 2 prior to any normalzation. The default value is TRUE |

`norm` |
The user may define a specific normalization method rather than "ALL" which is the default case. The available abbreviations are described in the details section |

`destname` |
Here we define the destination path and the name of the jpeg file with the density plots. The default path is the working directory |

The available normalization methods are:

Mean-centering normalization "mc"

z-score normalization "z"

Quantile normalization "q"

Cyclic loess normalization "cl"

Mean-centering normalization and log2 transformation "mcL2"

z-score normalization and log2 transformation "zL2"

Quantile normalization and log2 transformation "qL2"

Cyclic loess normalization and log2 transformation "clL2"

`rawdata` |
The initial gene expression values |

`mc.normdata ` |
The values after 'mean-centering' normalization |

`z.normdata ` |
The values after 'z-score' normalization |

`q.normdata ` |
The values after 'quantile' normalization |

`cl.normdata ` |
The values after 'cyclic loess' normalization |

`mcL2.normdata ` |
The values after 'mean-centering' normalization and log2 |

`zL2.normdata ` |
The values after 'z-score' normalization and log2 |

`qL2.normdata ` |
The values after 'quantile' normalization and log2 |

`clL2.normdata ` |
The values after 'cyclic loess' normalization and log2 |

Argiris Sakellariou

1 2 3 4 5 | ```
library(mAPKLData)
data(mAPKLData)
varLabels(mAPKLData)
breast <- sampling(Data=mAPKLData, valPercent=40, classLabels="type", seed=135)
normTrainData <- preprocess(exprsObj=breast$trainData)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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