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Journal article

CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation

Abstract:

Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors, such as batch effects and biologically relevant variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across samples. Here, we present CIDER, a meta-clustering workflow based on inter-group similarity measures. We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration ap...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1186/s13059-021-02561-2

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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Role:
Author
ORCID:
0000-0002-1688-6032
More by this author
Role:
Author
ORCID:
0000-0001-7615-8523
Publisher:
BioMed Central
Journal:
Genome Biology More from this journal
Volume:
22
Issue:
1
Article number:
337
Publication date:
2021-12-13
Acceptance date:
2021-11-29
DOI:
EISSN:
1474-760X
Language:
English
Keywords:
Pubs id:
1225454
Local pid:
pubs:1225454
Deposit date:
2021-12-16

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