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DAtest: a framework for choosing differential abundance or expression method
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1038/nmeth.2658openalex-snapshot
Differential abundance analysis for microbial marker-gene surveys
inferredcites →https://doi.org/10.1093/bioinformatics/btp616openalex-snapshot
edgeR : a Bioconductor package for differential expression analysis of digital gene expression data
inferredcites →https://doi.org/10.1093/nar/gkv007openalex-snapshot
limma powers differential expression analyses for RNA-sequencing and microarray studies
inferredcites →https://doi.org/10.1177/0962280211428386openalex-snapshot
Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data
inferredcites →https://doi.org/10.1186/gb-2013-14-9-r95openalex-snapshot
Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data
inferredcites →https://doi.org/10.1186/s13059-014-0550-8openalex-snapshot
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
inferredcites →https://doi.org/10.1186/s40168-016-0208-8openalex-snapshot
Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies
inferredcites →https://doi.org/10.1186/s40168-017-0237-yopenalex-snapshot
Normalization and microbial differential abundance strategies depend upon data characteristics
inferredcites →https://doi.org/10.3402/mehd.v26.27663openalex-snapshot
Analysis of composition of microbiomes: a novel method for studying microbial composition
What points at this work
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