getDS {maSigPro} | R Documentation

## Extract lists of significant isoforms from Differentially Spliced Genes (DSG)

### Description

`getDS` creates lists of significant isoforms from Differentially Spliced Genes (DSG)

### Usage

      getDS(Model, vars="all", rsq=0.4)

### Arguments

`Model` | a `IsoModel` object
`vars` | argument of the `get.siggenes` function applied to isoforms
`rsq` | cut-off level at the R-squared value for the stepwise regression fit. Only isoforms with R-squared more than rsq are selected

### Details

There are 3 possible values for the vars argument: "all", "each" and "groups". See `get.siggenes`.

### Value

In the console a summary of the selection is printed.

`Model` | a `IsoModel` object to be used in the following steps
`get2` | a `get.siggenes` object to be used in the following steps
`DSG` | Names of the selected genes: Differentially Spliced Genes
`DET` | Names of the selected Isoforms: Differentally Expressed Transcripts
`List0` | a list with the names of Differentially Spliced Genes without Isoforms with R-squared higher than rsq
`NumIso.by.gene` | Number of selected Isoforms for each Differentially Spliced Gene

### 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

`get.siggenes`, `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)
    Myget$DSG
    Myget$DET

    see <- seeDS(Myget, cluster.all=FALSE, k=6)
    table <- tableDS(see)
    table$IsoTable

[Package maSigPro version 1.66.0 Index]