ArticleNature methods2019
A discriminative learning approach to differential expression analysis for single-cell RNA-seq.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed.
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- RAG suppresses group 2 innate lymphoid cells.eLife · 2025Article
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- Distinct gene regulatory dynamics drive skeletogenic cell fate convergence during vertebrate embryogenesis.Nature communications · 2025Article
- A Unique Signature for Cancer-Associated Fibroblasts in Melanoma Metastases.Pigment cell & melanoma research · 2025Article
- kallisto, bustools and kb-python for quantifying bulk, single-cell and single-nucleus RNA-seq.Nature protocols · 2025Review
- Accurate quantification of nascent and mature RNAs from single-cell and single-nucleus RNA-seq.Nucleic acids research · 2025Article
40 more citing papers are in PubMed but not listed here.
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4 authors.
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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.
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