eLife

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   10.1101/2025.03.18.643855

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   10.1101/2025.03.13.643043

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   10.1101/2023.02.14.528587

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   10.1101/2025.03.07.642061

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   10.1101/2023.10.13.562270

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   10.1101/2023.07.12.548710

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   10.1101/2025.02.21.639428

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   10.1101/2024.12.12.627956

9. Unsupervised reference-free inference reveals unrecognized regulated transcriptomic complexity in human single cells

   This article has 7 authors:

   1. Roozbeh Dehghannasiri
   2. George Henderson
   3. Rob Bierman
   4. Tavor Baharav
   5. Kaitlin Chaung
   6. Peter Wang
   7. Julia Salzman

   This article has been curated by 1 group:

   • Curated by eLife

     eLife Assessment

     This study presents a valuable advance for the analysis of gene expression variation at the level of individual cells by introducing a novel reference-free framework that can detect splicing, fusion, editing, immune-receptor diversity and repeated elements in sequencing data. The evidence supporting these claims is solid, with rigorous validation on simulated datasets and extensive analysis of full-length single-cell sequencing data demonstrating improved performance over existing methods. This work will be of particular interest to researchers developing methods for high-resolution transcriptome analysis and to those studying cellular heterogeneity in health and disease.

   Reviewed by eLife, Arcadia Science

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    10.1101/2025.03.19.642122