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Improving replicability in single-cell RNA-Seq cell type discovery with Dune.
Roux de Bézieux, Hector; Street, Kelly; Fischer, Stephan; Van den Berge, Koen; Chance, Rebecca; Risso, Davide; Gillis, Jesse; Ngai, John; Purdom, Elizabeth; Dudoit, Sandrine.
Afiliación
  • Roux de Bézieux H; Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
  • Street K; Center for Computational Biology, University of California, Berkeley, CA, USA.
  • Fischer S; Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
  • Van den Berge K; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  • Chance R; Department of Statistics, University of California, Berkeley, CA, USA.
  • Risso D; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium.
  • Gillis J; Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
  • Ngai J; Department of Statistical Sciences, University of Padova, Padova, Italy.
  • Purdom E; Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
  • Dudoit S; Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
BMC Bioinformatics ; 25(1): 198, 2024 May 24.
Article en En | MEDLINE | ID: mdl-38789920
ABSTRACT

BACKGROUND:

Single-cell transcriptome sequencing (scRNA-Seq) has allowed new types of investigations at unprecedented levels of resolution. Among the primary goals of scRNA-Seq is the classification of cells into distinct types. Many approaches build on existing clustering literature to develop tools specific to single-cell. However, almost all of these methods rely on heuristics or user-supplied parameters to control the number of clusters. This affects both the resolution of the clusters within the original dataset as well as their replicability across datasets. While many recommendations exist, in general, there is little assurance that any given set of parameters will represent an optimal choice in the trade-off between cluster resolution and replicability. For instance, another set of parameters may result in more clusters that are also more replicable.

RESULTS:

Here, we propose Dune, a new method for optimizing the trade-off between the resolution of the clusters and their replicability. Our method takes as input a set of clustering results-or partitions-on a single dataset and iteratively merges clusters within each partitions in order to maximize their concordance between partitions. As demonstrated on multiple datasets from different platforms, Dune outperforms existing techniques, that rely on hierarchical merging for reducing the number of clusters, in terms of replicability of the resultant merged clusters as well as concordance with ground truth. Dune is available as an R package on Bioconductor https//www.bioconductor.org/packages/release/bioc/html/Dune.html .

CONCLUSIONS:

Cluster refinement by Dune helps improve the robustness of any clustering analysis and reduces the reliance on tuning parameters. This method provides an objective approach for borrowing information across multiple clusterings to generate replicable clusters most likely to represent common biological features across multiple datasets.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Análisis de la Célula Individual / RNA-Seq Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Programas Informáticos / Análisis de la Célula Individual / RNA-Seq Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido