seeDS {maSigPro} | R Documentation

## Wrapper function for visualization of significant isoforms from Differentially Spliced Genes

### Description

`seeDS` This function provides visualisation tools for Significant Isoforms in a time course experiment. The function calls the `see.genes` function for selected Isoforms. This cluster will be the reference in `tableDS` function to identify the trends that follows the isoforms of a specific gene.

### Usage

     seeDS(get, rsq=0.4, cluster.all=TRUE, plot.mDSG=FALSE, k=6,
     cluster.method="hclust", k.mclust=FALSE, ...)

### Arguments

`get` | a `getDS` object a cluster of flat Isoform
`rsq` | Required when cluster.all=TRUE. It is the cut-off level at the R-squared value for detecting significant isoforms of all the genome.
`cluster.all` | TRUE to make the cluster with significant isoforms of all the genome. FALSE to make the cluster with significant isoforms of Differentially Spliced Genes.
`plot.mDSG` | TRUE to make a cluster with the Isoforms belonging to monoIsoform genes
`k` | number of clusters for data partioning
`cluster.method` | clustering method for data partioning. Currently `"hclust"`, `"kmeans"` and `"Mclust"` are supported
`k.mclust` | TRUE for computing the optimal number of clusters with Mclust algorithm
`...` | other graphical function argument

### Details

The cluster reference can be made with significant isoforms of all the genome or with the isoforms belonging to the Differentially Spliced Genes.

Alternatively a cluster of monoIsoforms can be asked.

Next a partioning of the data is generated using a clustering method.

The results of the clustering are visualized in two plots as in `see.genes`.

### Value

Experiment wide Isoform profiles and by group profiles plots are generated for each data cluster in the graphical device.

`Model` | a `IsoModel` object to be used in the following steps
`get` | a `get.siggenes` object to be used in the following steps
`NumIso.by.gene` | Number of selected Isoforms for each Differentially Spliced Gene
`cut` | vector indicating gene partioning into clusters
`names.genes` | vector with the name of the gene each selected isoform belongs to

### 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., Alberto 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

`see.genes`, `IsoModel`

### 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]