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

In maSigPro: Significant Gene Expression Profile Differences in Time Course Gene Expression Data

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, [e-mail]

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

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. 8, 2020, 6:51 p.m.