Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments
By Gordon K. Smyth. Statistical Applications in Genetics and Molecular Biology, 2004
11,931 citations recorded by the source corpus.
Subfields: Genetics, Management Science and Operations Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/e7b9bb433eb30eae9bbec7d4ae97903a6718c083
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In Genetics
- Gordon K. Smyth
- Wei Shi
- Matthew Stephens
- Yunshun Chen
- Charity W. Law
- Simon Anders
- Davis J. McCarthy
- Wolfgang Huber
In Management Science and Operations Research
- Bradley Efron
- Jiashun Jin
- Yunshun Chen
- Davis J. McCarthy
- Wolfgang Huber
- Simon Anders
- Charlotte Soneson
- Matthew Stephens
In Molecular Biology
- Gordon K. Smyth
- Aaron T. L. Lun
- Yunshun Chen
- Mark D. Robinson
- Charlotte Soneson
- John C. Marioni
- Wolfgang Huber
- Jeffrey T. Leek
Citations in the corpus
- Use of within-array replicate spots for assessing differential expression in microarray experiments (2005, positive): “When the number of arrays is small, the spike-in results were further by empirical Bayes smoothing of the sample variances as in Smyth (2004).”
- A Factor Model Approach to Multiple Testing Under Dependence (2009, methodology): “BH + ST: BH method applied on classical t-tests with estimation of m0 by Storey and Tibshirani (2003). BY: Benjamini and Yekutieli (2001)’s resam-”
- Rearrangement of CRLF2 in B-progenitor– and Down syndrome–associated acute lymphoblastic leukemia (2009, methodology): “The two-color array raw data were background-subtracted and normalized by locally weighted scatterplot smoothing using the Linear Models for Microarray Analysi”
- Tximeta: Reference sequence checksums for provenance identification in RNA-seq (2019, methodology): “223 The SummarizedExperiment object, se, that is returned by tximeta can then be 224 passed to various downstream packages such as DESeq2, edgeR, limma-voom, or 225 fishpond, with example code in the tximeta package vignette [32, 34–37].”
- Substrate Fate in Activated Macrophages: A Comparison between Innate, Classic, and Alternative Activation (2010, methodology): “Differential expression for the following comparisons was tested using limma Bioconductor package (29): 1) naive mature macrophage versus classic activated macrophage, and 2) alternatively activated macrophage.”
- Hepatitis B and D viruses exploit sodium taurocholate co-transporting polypeptide for species-specific entry into hepatocytes. (2014, background): “Evolutionary adaptation of viral envelope proteins to receptors are major determinants of host specificity and tissue tropism.(1) For many human pathogenic viruses, receptors are known, however, the identity of the hepatitis B virus (HBV)/hepatitis D virus (HDV) receptor remained elusive(2) until re”
- Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments (2010, methodology): “, 2003b) and then differential expression was determined by the R/Bioconductor package limma (Smyth, 2004), using the standard pipeline of lmFit and eBayes.”
- Reproducibility of computational workflows is automated using continuous analysis (2017, methodology): “This analysis quantified transcript counts using kallisto 26 , limma 27,28 and sleuth 29 ; performed principal component analysis and ran a differential expression analysis.”
- A highly conserved program of neuronal microexons is misregulated in autistic brains (2014, methodology): “the empirical Bayes log-odds of differential PSI/PSU/PIRs (Smyth, 2004) (as implemented in “ebayes”, from the limma package in R).”
- Foxp3 transcription-factor-dependent and -independent regulation of the regulatory T cell transcriptional signature. (2007, positive): “Arbitrary cutoffs are often set on fold change or some statistical metric corresponding to false-discovery rates (FDRs) (Smyth, 2004) subjectively deemed acceptable; yet such strategies leave out a substantial fraction of the true but subtle differences that fail to meet this threshold.”
- A comparison of statistical methods for detecting differentially expressed genes from RNA-seq data. (2012, positive): “Such strategies were used in microarray data analysis and many tests were developed that borrow information across genes to better estimate the variance (for example, [26] [5]) or both the mean expression and the variance [9].”
- The Microbiome and Butyrate Regulate Energy Metabolism and Autophagy in the Mammalian Colon (2011, positive): “For gene expression, data were analyzed with R/limma (Smyth, 2004), and FDR and FDR-adjusted p values were calculated.”