A discriminative learning approach to differential expression analysis for single-cell RNA-seq

Vasilis Ntranos, Lynn Yi, Páll Melsted, Lior Pachter*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)163-166
Number of pages4
JournalNature Methods
Volume16
Issue number2
DOIs
Publication statusPublished - 1 Feb 2019

Bibliographical note

Publisher Copyright:
© 2019, The Author(s), under exclusive licence to Springer Nature America, Inc.

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