This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication).
|Author||Juan Pablo Acosta, Liliana Lopez-Kleine|
|Bioconductor views||DifferentialExpression GeneExpression Microarray PrincipalComponent TimeCourse mRNAMicroarray|
|Date of publication||None|
|Maintainer||Juan Pablo Acosta <email@example.com>|
ac: Artificial Components for Gene Expression Data
acde-package: Artificial Components Detection of Differentially Expressed...
bcaFDR: BCa Confidence Upper Bound for the FDR
fdr: False Discovery Rate Computation
phytophthora: Gene Expression Data for Tomato Plants Inoculated with...
plot.STP: Plot Method for Single Time Point Analysis
plot.TC: Plot Method for Time Course Analysis
print.STP: Print Method for Single Time Point Analysis
print.TC: Print Method for Time Course Analysis
qval: Q-Values Computation
stp: Single Time Point Analysis for Detecting Differentially...
tc: Time Course Analysis for Detecting Differentially Expressed...
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