Package: maSigPro 1.79.0

Maria Jose Nueda

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

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.

Authors:Ana Conesa and Maria Jose Nueda

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maSigPro.pdf |maSigPro.html✨
maSigPro/json (API)

# Install 'maSigPro' in R:
install.packages('maSigPro', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • ISOdata - RNA-Seq dataset example for isoforms
  • ISOdesign - Experimental design for ISOdata dataset example
  • NBdata - RNA-Seq dataset example
  • NBdesign - Experimental design for RNA-Seq example
  • 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

On BioConductor:maSigPro-1.79.0(bioc 3.21)maSigPro-1.78.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

microarrayrna-seqdifferential expressiontimecourse

5.19 score 78 scripts 598 downloads 119 mentions 24 exports 7 dependencies

Last updated 2 months agofrom:4df82681f4. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 18 2024
R-4.5-winOKDec 18 2024
R-4.5-linuxOKDec 18 2024
R-4.4-winOKDec 18 2024
R-4.4-macOKDec 18 2024
R-4.3-winOKDec 18 2024
R-4.3-macOKDec 18 2024

Exports:average.rowsget.siggenesgetDSgetDSPatternsi.rankIsoModelIsoPlotmake.design.matrixmaSigProUsersGuidep.vectorPlotGroupsPlotProfilesPodiumChangepositionreg.coeffssee.genesseeDSstepbackstepforsuma2VennT.fittableDStwo.ways.stepbacktwo.ways.stepfor

Dependencies:admiscBiobaseBiocGenericsgenericsMASSmclustvenn

maSigPro Vignette

Rendered frommaSigPro.Rnwusingutils::Sweaveon Dec 18 2024.

Last update: 2020-04-09
Started: 2013-10-21

Readme and manuals

Help Manual

Help pageTopics
Average rows by match and indexaverage.rows
Gene expression data potato abiotic stressdata.abiotic
Experimental design potato abiotic stressedesign.abiotic
Experimental design with a shared timeedesignCT
Experimental design with different replicatesedesignDR
Extract significant genes for sets of variables in time series gene expression experimentsget.siggenes
Extract lists of significant isoforms from Differentially Spliced Genes (DSG)getDS
Lists of genes with Isoforms in different clustersgetDSPatterns
Ranks a vector to indexi.rank
RNA-Seq dataset example for isoformsISOdata
Experimental design for ISOdata dataset exampleISOdesign
Detection of genes with Isoforms with different gene expression in time course experimentsIsoModel
Plotting the isoform profiles of a specific gene by groupsIsoPlot
Make a design matrix for regression fit of time series gene expression experimentsmake.design.matrix
View maSigPro User's GuidemaSigProUsersGuide
RNA-Seq dataset exampleNBdata
Experimental design for RNA-Seq exampleNBdesign
Make regression fit for time series gene expression experimentsp.vector
Function for plotting gene expression profile at different experimental groupsPlotGroups
Function for visualization of gene expression profilesPlotProfiles
Detection of Genes with switchs of their major isoformsPodiumChange
Column position of a variable in a data frameposition
Calculate true variables regression coefficientsreg.coeffs
Wrapper function for visualization of gene expression values of time course experimentssee.genes
Wrapper function for visualization of significant isoforms from Differentially Spliced GenesseeDS
Fitting a linear model by backward-stepwise regressionstepback
Fitting a linear model by forward-stepwise regressionstepfor
Creates a Venn Diagram from a matrix of characterssuma2Venn
Makes a stepwise regression fit for time series gene expression experimentsT.fit
Identification of Mayor and minor Isoforms in the clusterstableDS
Fitting a linear model by backward-stepwise regressiontwo.ways.stepback
Fitting a linear model by forward-stepwise regressiontwo.ways.stepfor