Smyth, G.K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.
Smyth, G. K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.
Smyth, G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments.
Smyth, G.K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments.
The chosen seed genes were ensured for significant differential expression in at least 15 experiments.
A consistent differential expression in cancer was observed.
Hardcastle, T. J. & Kelly, K. A. baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.
Genes showing differential expression in either Li-treated animals or KO animals were included in the analysis.
Support is evaluated using: (a) RNA-seq tumour-normal differential expression in The Cancer Genome Atlas (TCGA).
Zhou, X., Lindsay, H. & Robinson, M. D. Robustly detecting differential expression in RNA sequencing data using observation weights.
