limma: Linear Models for Microarray Data

By Gordon K. Smyth. 2005

6,420 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4b6835c8aa28eb6669e9043c7d778efeed918eb0

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In Molecular Biology

Citations in the corpus

Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, methodology): “The Wilcoxon rank-sum test is used for GSA in the popular limma R-package (32).”
A quantitative analysis of CLIP methods for identifying binding sites of RNA-binding proteins (2011, methodology): “To eliminate potential biases owing to differences in the shape of the distributions of expression, the two siRNA-treated samples were quantile-normalized with the normalize Quantile function of the limma R packag”
Systems Genetics Approach Identifies Gene Pathways and Adamts2 as Drivers of Isoproterenol-Induced Cardiac Hypertrophy and Cardiomyopathy in Mice (2016, methodology): “Analysis was conducted using the Neqc algorithm included in the limma R package (Smyth, 2005) and batch effects addressed using COMbat (Johnson et al., 2007).”
SCENIC: Single-cell regulatory network inference and clustering (2017, methodology): “Combat 59,60 and Limma 61,62 were run using the same logged and filtered matrix as in SCENIC.”
limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, methodology): “Both observationlevel (2,10,43) and sample-specific weights (11) can be used in an analysis.”
Ocean acidification alters skeletogenesis and gene expression in larval sea urchins (2010, methodology): “Normalization and data analysis were completed using R (R Development Core Team 2008) with the limma software package (Smyth 2005).”
Differential expression analysis for sequence count data (2010, methodology): “An empirical Bayes procedure, similar to the one originally developed for the limma package [24-26], determines how to combine these two sources of information optimally.”