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 (May 2024) Improving replicability in single-cell RNA-Seq cell type discovery with Dune. BMC Bioinformatics, 25 (1). p. 198. ISSN 1471-2105 (Public Dataset)

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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.

Item Type:	Paper
Subjects:	bioinformatics > genomics and proteomics > annotation bioinformatics bioinformatics > genomics and proteomics > annotation > gene expression profiling annotation bioinformatics > genomics and proteomics > genetics & nucleic acid processing bioinformatics > genomics and proteomics Investigative techniques and equipment bioinformatics > computational biology > algorithms organism description > animal Investigative techniques and equipment > assays bioinformatics > computational biology organism description > animal > mammal > primates > hominids organism description > animal > mammal > primates > hominids > human organism description > animal > mammal organism description > animal > mammal > primates Investigative techniques and equipment > assays > RNA-seq bioinformatics > genomics and proteomics > genetics & nucleic acid processing > transcriptomes
CSHL Authors:	Gillis, Jesse
Communities:	CSHL labs > Gillis Lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	24 May 2024
Date Deposited:	28 May 2024 14:32
Last Modified:	28 May 2024 14:32
PMCID:	PMC11127396
Related URLs:	Publisher
Dataset ID:	https://hemberg-lab.github.io/scRNA.seq.datasets/human/pancreas/ https://assets.nemoarchive.org/dat-k7p82j4 https://assets.nemoarchive.org/dat-55mowp9 https://github.com/HectorRDB/Dune_Paper http://www.bioconductor.org/packages/release/bioc/html/Dune.html
URI:	https://repository.cshl.edu/id/eprint/41567

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