IDclust: Iterative clustering for unsupervised identification of cell types with single cell transcriptomics and epigenomics Article Swipe
Pacôme Prompsy
,
Mélissa Saichi
,
Félix Raimundo
,
Céline Vallot
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/nargab/lqae174
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/nargab/lqae174
The increasing diversity of single-cell datasets require systematic cell type characterization. Clustering is a critical step in single-cell analysis, heavily influencing downstream analyses. However, current unsupervised clustering algorithms rely on biologically irrelevant parameters that require manual optimization and fail to capture hierarchical relationships between clusters. We developed IDclust, a framework that identifies clusters with significant biological features at multiple resolutions using biologically meaningful thresholds like fold change, adjusted P-value and fraction of expressing cells. By iteratively processing and clustering subsets of the dataset, IDclust guarantees that all clusters found have significantly different features and stops only when no more interpretable cluster is found. It also creates a hierarchy of clusters, enabling visualization of the hierarchical relationships between different clusters. Analyzing multiple single-cell transcriptomic reference datasets, IDclust achieves superior clustering accuracy compared to state of the art algorithms. We showcase its utility by identifying previously unannotated clusters and identifying branching patterns in scATAC datasets. Using it’s unsupervised nature and ability to analyze different -omics, we compare the resolution of different histone marks in multi-omic paired-tag dataset. Overall, IDclust automates single-cell exploration, facilitates cell type annotation and provides a biologically interpretable basis for clustering.
Related Topics
Concepts
Cluster analysis
Computer science
Hierarchical clustering
Annotation
Epigenomics
Hierarchy
Data mining
Identification (biology)
Visualization
Artificial intelligence
Computational biology
Pattern recognition (psychology)
Biology
Market economy
Economics
Botany
Gene
DNA methylation
Biochemistry
Gene expression
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/nargab/lqae174
- OA Status
- gold
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405619992
All OpenAlex metadata
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https://openalex.org/W4405619992Canonical identifier for this work in OpenAlex
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https://doi.org/10.1093/nargab/lqae174Digital Object Identifier
- Title
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IDclust: Iterative clustering for unsupervised identification of cell types with single cell transcriptomics and epigenomicsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-28Full publication date if available
- Authors
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Pacôme Prompsy, Mélissa Saichi, Félix Raimundo, Céline VallotList of authors in order
- Landing page
-
https://doi.org/10.1093/nargab/lqae174Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1093/nargab/lqae174Direct OA link when available
- Concepts
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Cluster analysis, Computer science, Hierarchical clustering, Annotation, Epigenomics, Hierarchy, Data mining, Identification (biology), Visualization, Artificial intelligence, Computational biology, Pattern recognition (psychology), Biology, Market economy, Economics, Botany, Gene, DNA methylation, Biochemistry, Gene expressionTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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