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.
European Nucleotide Archive
Gene Expression Omnibus
Sequence Read Archive
H.P. and L.P. were partially supported by NIH grant nos. R01 DK094699 and R01 HG006129. We thank D. Li, A. Tseng, and P. Sturmfels for help with implementing some of the interactive features in sleuth.
Integrated supplementary information
Sleuth software used in the article along with the analysis to reproduce figures.