Papers: 10.1186/s12859-015-0794-7
https://doi.org/10.1186/s12859-015-0794-7
Evaluation of methods for differential expression analysis on multi-group RNA-seq count data
Cited by: 47
Author(s): Min Tang, Jianqiang Sun, Kentaro Shimizu, Koji Kadota
Published: over 9 years ago
Software Mentions 13
bioconductor: baySeq
Empirical Bayesian analysis of patterns of differential expression in count dataPapers that mentioned: 139
Very Likely Science (100)
bioconductor: compcodeR
RNAseq data simulation, differential expression analysis and performance comparison of differential expression methodsPapers that mentioned: 11
Very Likely Science (100)
bioconductor: DESeq2
Differential gene expression analysis based on the negative binomial distributionPapers that mentioned: 9,583
Very Likely Science (100)
Very Likely Science (100)
bioconductor: EBSeq
An R package for gene and isoform differential expression analysis of RNA-seq dataPapers that mentioned: 261
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bioconductor: edgeR
Empirical Analysis of Digital Gene Expression Data in RPapers that mentioned: 6,568
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bioconductor: TCC
TCC: Differential expression analysis for tag count data with robust normalization strategiesPapers that mentioned: 67
Very Likely Science (100)
cran: PoissonSeq
Papers that mentioned: 30
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Very Likely Science (75)
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pypi: voom
A python message bus that you can put 4 million volts through.Papers that mentioned: 136
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