seeDS: Wrapper function for visualization of significant isoforms...
In maSigPro: Significant Gene Expression Profile Differences in Time Course Gene Expression Data

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

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.

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

`get`	a `getDS` object a cluster of flat Isoform
`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.
`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.
`plot.mDSG`	TRUE to make a cluster with the Isoforms belonging to monoIsoform genes
`k`	number of clusters for data partioning
`k.mclust`	TRUE for computing the optimal number of clusters with Mclust algorithm
`cluster.method`	clustering method for data partioning. Currently `"hclust"`, `"kmeans"` and `"Mclust"` are supported

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.

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

Maria Jose Nueda, mj.nueda@ua.es

Nueda, M.J., Martorell, J., Marti, C., Tarazona, S and Conesa, A. 2017. Identification and visualization of differential isoform expression in RNA-Seq time series. In preparation.

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.genes, IsoModel

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