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

`IsoModel`

Performs a model comparison for each gene to detect genes with different trends in time course experiments and applies maSigPro to the Isoforms belonging to selected genes.

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`data` |
matrix containing isoform expression. Isoforms must be in rows and experimental conditions in columns |

`gen` |
vector with the name of the gene each isoform belongs to |

`design` |
design matrix for the regression fit such as that generated by the |

`Q` |
significance level |

`min.obs` |
cases with less than this number of true numerical values will be excluded from the analysis. Minimum value to estimate the model is (degree+1)xGroups+1. Default is 6. |

`minorFoldfilter` |
fold expression difference between minor isoforms and the most expressed isoform to exclude minor isoforms from analysis. Default NULL |

`counts` |
a logical indicating whether your data are counts |

`family` |
the distribution function to be used in the glm model. It must be specified as a function: gaussian(), poisson(), negative.binomial(theta)... If NULL family will be negative.binomial(theta) when counts=TRUE or gaussian() when counts=FALSE |

`theta` |
theta parameter for negative.binomial family |

`epsilon` |
argument to pass to |

`rownames(design)`

and `colnames(data)`

must be identical vectors and indicate experimental condition names.

`rownames(data)`

should contain unique isoform IDs.

`colnames(design)`

are the given names for the variables in the regression model.

`data` |
input data matrix to be used in the following steps |

`gen` |
input gen vector to be used in the following steps |

`design` |
input design matrix to be used in the following steps |

`DSG` |
Names of the selected genes: Differentially Spliced Genes |

`pvector` |
p.vector output of isoforms that belong to selected.genes |

`Tfit` |
Tfit output of isoforms that belong to selected.genes |

Maria Jose Nueda, mj.nueda@ua.es

Nueda, M.J., Martorell, J., Marti, C., Tarazona, S., Conesa, A. 2018. Identification and visualization of differential isoform expression in RNA-seq time series. Bioinformatics. 34, 3, 524-526.

Nueda, M.J., Tarazona, S., Conesa, A. 2014. Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics, 30, 2598-602.

Conesa, A., Nueda M.J., Ferrer, A., Talon, T. 2006. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102.

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