Published February 2019
| Supplemental Material
Journal Article
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A discriminative learning approach to differential expression analysis for single-cell RNA-seq
Chicago
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Abstract
Single-cell RNA-seq makes it possible to characterize the transcriptomes of cell types across different conditions and to identify their transcriptional signatures via differential analysis. Our method detects changes in transcript dynamics and in overall gene abundance in large numbers of cells to determine differential expression. When applied to transcript compatibility counts obtained via pseudoalignment, our approach provides a quantification-free analysis of 3′ single-cell RNA-seq that can identify previously undetectable marker genes.
Additional Information
© 2019 Springer Nature Publishing AG. Received 06 July 2018; Accepted 13 December 2018; Published 21 January 2019. Code availability: The code required to conduct the simulations and reproduce the analyses is available at https://github.com/pachterlab/NYMP_2018. We also have provided the Github repository that was zipped at the time of manuscript acceptance as Supplementary Software. Data availability: The myogenesis dataset (Trapnell et al.(10)) is available on the conquer database and on GEO as series GSE52529. The dataset on embryogenesis is available on the conquer database (Petropoulos et al.(22). The 10x PBMC dataset is available from the 10x Genomics Support website(19). We thank N. Bray, J. Gehring and V. Svensson for discussion and comments on the manuscript, and H. Pimentel for assisting with the simulations. We thank A. Butler and R. Satija for implementing this method in Seurat. V.N., L.Y. and L.P. are partially funded by NIH R012017-0569. Author Contributions: V.N. developed the model during discussions with L.Y. and L.P, and analyzed the 10x PBMC dataset. L.Y. performed the simulations and analyzed the embryo SMART-Seq dataset. P.M. developed kallisto genomebam and assisted with analysis. All authors contributed extensively to the interpretation of the results and writing of the manuscript. The authors declare no competing interests.Attached Files
Supplemental Material - 41592_2018_303_MOESM1_ESM.pdf
Supplemental Material - 41592_2018_303_MOESM2_ESM.pdf
Supplemental Material - 41592_2018_303_MOESM3_ESM.zip
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Additional details
- Eprint ID
- 92421
- DOI
- 10.1038/s41592-018-0303-9
- Resolver ID
- CaltechAUTHORS:20190123-095919155
- NIH
- R012017-0569
- Created
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2019-01-23Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field