(R) Gaga: a parsimonious and flexible model for differential expression analysis Bioconductor version: Release (2.10) Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).
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References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Hong, Zhaoping; Lian, Heng: BOPA: A Bayesian hierarchical model for outlier expression detection (2012)
- Lund, Steven P.; Nettleton, Dan: The importance of distinct modeling strategies for gene and gene-specific treatment effects in hierarchical models for microarray data (2012)
- Newton, Michael A.; Chung, Lisa M.: Gamma-based clustering via ordered means with application to gene-expression analysis (2010)
- Rossell, David: Gaga: a parsimonious and flexible model for differential expression analysis (2009)