Papers: 10.1186/s13059-020-02104-1
https://doi.org/10.1186/s13059-020-02104-1
Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data
Cited by: 57
Author(s): Matteo Calgaro, Chiara Romualdi, Levi Waldron, Davide Risso, Nicola Vitulo
Published: almost 5 years ago
Software Mentions 17
bioconductor: ALDEx2
Analysis Of Differential Abundance Taking Sample and Scale Variation Into AccountPapers that mentioned: 136
Very Likely Science (100)
bioconductor: DESeq2
Differential gene expression analysis based on the negative binomial distributionPapers that mentioned: 9,583
Very Likely Science (100)
bioconductor: edgeR
Empirical Analysis of Digital Gene Expression Data in RPapers that mentioned: 6,568
Very Likely Science (100)
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bioconductor: metagenomeSeq
Statistical analysis for sparse high-throughput sequencingPapers that mentioned: 169
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bioconductor: phyloseq
Handling and analysis of high-throughput microbiome census dataPapers that mentioned: 1,702
Very Likely Science (75)
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bioconductor: zinbwave
Zero-Inflated Negative Binomial Model for RNA-Seq DataPapers that mentioned: 12
Very Likely Science (100)
cran: corncob
Count Regression for Correlated Observations with the Beta-BinomialPapers that mentioned: 4
Very Likely Science (93)
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pypi: songbird
Vanilla regression methods for microbiome differential abundance analysisPapers that mentioned: 4
Very Likely Science (100)