Papers: 10.1186/s13059-014-0527-7
https://doi.org/10.1186/s13059-014-0527-7
The importance of study design for detecting differentially abundant features in high-throughput experiments
Cited by: 12
Author(s): Huaien Luo, Juntao Li, Burton Kuan Hui Chia, Paul Robson, Niranjan Nagarajan
Published: over 10 years ago
Software Mentions 5
bioconductor: baySeq
Empirical Bayesian analysis of patterns of differential expression in count dataPapers that mentioned: 139
Very Likely Science (100)
bioconductor: edgeR
Empirical Analysis of Digital Gene Expression Data in RPapers that mentioned: 6,568
Very Likely Science (100)
bioconductor: NOISeq
Exploratory analysis and differential expression for RNA-seq dataPapers that mentioned: 321
Very Likely Science (100)
Very Likely Science (100)
Very Likely Science (90)