Differential analysis of RNA-seq incorporating quantification uncertainty

By Harold Pimentel, Nicolas L. Bray, Suzette Puente, Páll Melsted, Lior S. Pachter. Nature Methods, 2016

1,365 citations recorded by the source corpus.

We describe sleuth (http://pachterlab.github.io/sleuth), a method for the differential analysis of gene expression data that utilizes bootstrapping in conjunction with response error linear modeling to decouple biological variance from inferential variance. sleuth is implemented in an interactive shiny app that utilizes kallisto quantifications and bootstraps for fast and accurate analysis of data from RNA-seq experiments.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c983edabba63e91cbd6fee4684657ab37981ecb4

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In Molecular Biology

1. Charlotte Soneson
2. Michael I. Love
3. Rob Patro
4. Mark D. Robinson
5. Lieven Clement
6. K. Van den Berge
7. Katharina M. Hembach
8. Simone Tiberi

Citations in the corpus

• Reproducibility of computational workflows is automated using continuous analysis (2017, methodology): “This analysis quantified transcript counts using kallisto 26 , limma 27,28 and sleuth 29 ; performed principal component analysis and ran a differential expression analysis.”
• RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “885 “inferential variance” and integrates this into the DE calculation through a variance components model on the log­transformed scale [166] .”