IsoModel: Detection of genes with Isoforms with different gene...

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

View source: R/IsoModel.R

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

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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, [email protected]

References

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 Also

p.vector, T.fit

Examples

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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

maSigPro documentation built on Nov. 1, 2018, 2:35 a.m.