IsoModel {maSigPro} | R Documentation

## Detection of genes with Isoforms with different gene expression in time course experiments

### Description

`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.

### Usage

    IsoModel(data, gen, design = NULL, Q = 0.05, min.obs = 6, minorFoldfilter = NULL,
        counts = FALSE, family = NULL, theta = 10, epsilon = 1e-05)

### Arguments

`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 `make.design.matrix` function
`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 `glm.control`, convergence tolerance in the iterative process to estimate de glm model

### Details

`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.

### Value

`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

### Author(s)

Maria Jose Nueda, mj.nueda@ua.es

### References

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.

### See Also

`p.vector`, `T.fit`

### Examples

    data(ISOdata)
    data(ISOdesign)
    mdis <- make.design.matrix(ISOdesign)
    MyIso <- IsoModel(data=ISOdata[,-1], gen=ISOdata[,1], design=mdis, counts=TRUE)
    Myget <- getDS(MyIso)
    see <- seeDS(Myget, cluster.all=FALSE, k=6)
    table <- tableDS(see)
    table$IsoTable

[Package maSigPro version 1.66.0 Index]