Automated analysis of protein subcellular location in time series images

By Yanhua Hu, Elvira Osuna-Highley, Juchang Hua, T. Nowicki, Robert Stolz, C. McKayle, R. Murphy. Bioinform., 2010

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The transcription factors Blimp-1 and IRF4 jointly control the differentiation and function of effector regulatory T cells (2011, methodology): “For further analysis and to account for intergene correlations, ROAST gene set test”
From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline (2016, background): “If the aim is to test for particular gene expression signatures or particular pathways, a more nuanced approach is to conduct a roast or fry gene set test [25].”
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR (2016, methodology): “Two of the packages, edgeR and limma, are well established while Glimma is the new kid on the block.”
It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR (2016, methodology): “Another downstream step uses the rotation gene set test (ROAST) [19].”
voom: precision weights unlock linear model analysis tools for RNA-seq read counts (2014, methodology): “A ROAST gene set test confirmed that these genes collectively are significantly upregulated in males ( P = 0.0001).”