maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments.
|Author||Ana Conesa <email@example.com>, Maria Jose Nueda <firstname.lastname@example.org>|
|Date of publication||None|
|Maintainer||Maria Jose Nueda <email@example.com>|
|License||GPL (>= 2)|
average.rows: Average rows by match and index
data.abiotic: Gene expression data potato abiotic stress
edesign.abiotic: Experimental design potato abiotic stress
edesignCT: Experimental design with a shared time
edesignDR: Experimental design with different replicates
get.siggenes: Extract significant genes for sets of variables in time...
i.rank: Ranks a vector to index
make.design.matrix: Make a design matrix for regression fit of time series gene...
maSigPro: Wrapping function for identifying significant differential...
maSigProUsersGuide: View maSigPro User's Guide
NBdata: RNA-Seq dataset example
NBdesign: Experimental design for RNA-Seq example
PlotGroups: Function for plotting gene expression profile at different...
PlotProfiles: Function for visualization of gene expression profiles
position: Column position of a variable in a data frame
p.vector: Make regression fit for time series gene expression...
reg.coeffs: Calculate true variables regression coefficients
see.genes: Wrapper function for visualization of gene expression values...
stepback: Fitting a linear model by backward-stepwise regression
stepfor: Fitting a linear model by forward-stepwise regression
suma2Venn: Creates a Venn Diagram from a matrix of characters
T.fit: Makes a stepwise regression fit for time series gene...
two.ways.stepback: Fitting a linear model by backward-stepwise regression
two.ways.stepfor: Fitting a linear model by forward-stepwise regression