limmaGUI: A graphical user interface for linear modeling of microarray data
By James M. Wettenhall, Gordon K. Smyth. Bioinform., 2004
759 citations recorded by the source corpus.
Subfields: Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/b3e8692d191f0db75bdd8252e8e94901d05242b9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- Charity W. Law
- Wei Shi
- Diabetes-Ling Wu
- Matthew E. Ritchie
- Belinda Phipson
- Gordon K. Smyth
- Yifang Hu
- Yunshun Chen
In Molecular Biology
- Gordon K. Smyth
- Wei Shi
- Charity W. Law
- Belinda Phipson
- Matthew E. Ritchie
- Diabetes-Ling Wu
- Yifang Hu
- Aaron T. L. Lun
Citations in the corpus
- Aging Hematopoietic Stem Cells Decline in Function and Exhibit Epigenetic Dysregulation (2007, methodology): “A quadratic polynomial was fit to the profile of each gene by use of LIMMA [52], enabling us to analyze progressive changes as well as peaks and valleys in expression over time.”
- limma: Linear Models for Microarray Data (2005, methodology): “Packages limmaGUI and affylmGUI are also available which provides graphical user interfaces to the most commonly used functions in limma [29].”
- limma powers differential expression analyses for RNA-sequencing and microarray studies (2015, methodology): “The limma package is used as a building block or as the underlying computational engine by a number of software projects designed to provide user-interfaces for gene expression data analysis including limmaGUI (66), affylmGUI (67), WebArray (68), RACE (69), CarmaWEB (70), Goulphar (71), MAGMA (72), ”
- Gene expression profiling of the tumor microenvironment during breast cancer progression (2009, methodology): “Gene expression changes in the stromal and epithelial compartments during breast cancer progression We compared the gene expression patterns of the tumor epithelium and stroma at each stage of progression (DCIS or IDC) with their respective normal state using the limma (linear models of microarrays)”
- Comparison of Label-free Methods for Quantifying Human Proteins by Shotgun Proteomics*S (2005, methodology): “p values for assessing significance of changes were determined by using an empirical Bayes approach and were corrected for multiple testing using the false discovery rate (25, 26).”