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Published July 2017 | Supplemental Material + Submitted
Journal Article Open

Differential analysis of RNA-seq incorporating quantification uncertainty

Abstract

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.

Additional Information

© 2017 Macmillan Publishers Limited, part of Springer Nature. Received 27 January 2017; Accepted 04 May 2017; Published online 05 June 2017. 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. Author Contributions: H.P. led the development of the sleuth statistical model and was assisted by S.P., N.L.B., P.M., and L.P. The method comparison and testing framework was designed by H.P., N.L.B., P.M., and L.P. The interactive sleuth live software was designed and implemented by H.P., as was the sleuth R package. H.P. automated production of the results. H.P., N.L.B., P.M., and L.P. analyzed results and wrote the paper. Data availability statement: The Bottomly data set is available at the NCBI Gene Expression Omnibus (GSE26024, accession nos. SRR099223–SRR099243). The Trapnell et al. data set (Fig. 1b) is available at the NCBI Gene Expression Omnibus (GSE37704, accession nos. SRR493366–SRR493371). The GEUVADIS data set is available at the European Nucleotide Archive (accession no. ERP001942). The authors declare no competing financial interests.

Attached Files

Submitted - 058164.full.pdf

Supplemental Material - nmeth.4324-S1.pdf

Supplemental Material - nmeth.4324-S2.zip

Supplemental Material - nmeth_4324-SF1.jpg

Supplemental Material - nmeth_4324-SF2.jpg

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