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scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data

View ORCID ProfileBobby Ranjan, View ORCID ProfileFlorian Schmidt, Wenjie Sun, Jinyu Park, Mohammad Amin Honardoost, Joanna Tan, Nirmala Arul Rayan, Shyam Prabhakar
doi: https://doi.org/10.1101/2020.04.22.056473
Bobby Ranjan
1Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore - 138672
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  • ORCID record for Bobby Ranjan
Florian Schmidt
1Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore - 138672
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  • ORCID record for Florian Schmidt
Wenjie Sun
1Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore - 138672
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Jinyu Park
1Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore - 138672
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Mohammad Amin Honardoost
1Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore - 138672
2Department of Medicine, School of Medicine, National University of Singapore, Singapore
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Joanna Tan
1Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore - 138672
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Nirmala Arul Rayan
1Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore - 138672
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Shyam Prabhakar
1Systems Biology and Data Analytics, Genome Institute of Singapore, A*STAR, 60 Biopolis Street, Singapore - 138672
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  • For correspondence: prabhakars@gis.a-star.edu.sg
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Abstract

Clustering is a crucial step in the analysis of single-cell data. Clusters identified using unsupervised clustering are typically annotated to cell types based on differentially expressed genes. In contrast, supervised methods use a reference panel of labelled transcriptomes to guide both clustering and cell type identification. Supervised and unsupervised clustering strategies have their distinct advantages and limitations. Therefore, they can lead to different but often complementary clustering results. Hence, a consensus approach leveraging the merits of both clustering paradigms could result in a more accurate clustering and a more precise cell type annotation. We present scConsensus, an R framework for generating a consensus clustering by (i) integrating the results from both unsupervised and supervised approaches and (ii) refining the consensus clusters using differentially expressed (DE) genes. The value of our approach is demonstrated on several existing single-cell RNA sequencing datasets, including data from sorted PBMC sub-populations. scConsensus is freely available on GitHub at https://github.com/prabhakarlab/scConsensus.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://zenodo.org/record/3637700#.XqENP5kRWUn

  • https://www.github.com/prabhakarlab/scConsensus

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted April 24, 2020.
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scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data
Bobby Ranjan, Florian Schmidt, Wenjie Sun, Jinyu Park, Mohammad Amin Honardoost, Joanna Tan, Nirmala Arul Rayan, Shyam Prabhakar
bioRxiv 2020.04.22.056473; doi: https://doi.org/10.1101/2020.04.22.056473
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scConsensus: combining supervised and unsupervised clustering for cell type identification in single-cell RNA sequencing data
Bobby Ranjan, Florian Schmidt, Wenjie Sun, Jinyu Park, Mohammad Amin Honardoost, Joanna Tan, Nirmala Arul Rayan, Shyam Prabhakar
bioRxiv 2020.04.22.056473; doi: https://doi.org/10.1101/2020.04.22.056473

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