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Evaluating single-cell cluster stability using the Jaccard similarity index

Ming Tang, Yasin Kaymaz, Brandon Logeman, Stephen Eichhorn, ZhengZheng S. Liang, Catherine Dulac, Timothy B. Sackton
doi: https://doi.org/10.1101/2020.05.26.116640
Ming Tang
1FAS informatics Group, Harvard University
2Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University and Howard Hughes Medical Institute
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Yasin Kaymaz
1FAS informatics Group, Harvard University
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Brandon Logeman
2Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University and Howard Hughes Medical Institute
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Stephen Eichhorn
3Department of Chemistry, Harvard University
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ZhengZheng S. Liang
2Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University and Howard Hughes Medical Institute
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Catherine Dulac
2Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University and Howard Hughes Medical Institute
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Timothy B. Sackton
1FAS informatics Group, Harvard University
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  • For correspondence: sackton@g.harvard.edu
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Abstract

Motivation One major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor, and the resolution parameters, among others.

Results Here, we present a set of tools to evaluate cluster stability by subsampling, which can guide parameter choice and aid in biological interpretation. The R package scclusteval and the accompanying Snakemake workflow implement all steps of the pipeline: subsampling the cells, repeating the clustering with Seurat, and estimation of cluster stability using the Jaccard similarity index. The Snakemake workflow takes advantage of high-performance computing clusters and dispatches jobs in parallel to available CPUs to speed up the analysis. The scclusteval package provides functions to facilitate the analysis of the output, including a series of rich visualizations.

Availability R package scclusteval: https://github.com/crazyhottommy/scclusteval Snakemake workflow: https://github.com/crazyhottommy/pyflow_seuratv3_parameter

Contact tsackton{at}g.harvard.edu, tangming2005{at}gmail.com

Supplementary information Supplementary data are available at Bioinformatics online.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/crazyhottommy/scclusteval

  • https://osf.io/rfbcg/

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 4.0 International license.
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Evaluating single-cell cluster stability using the Jaccard similarity index
Ming Tang, Yasin Kaymaz, Brandon Logeman, Stephen Eichhorn, ZhengZheng S. Liang, Catherine Dulac, Timothy B. Sackton
bioRxiv 2020.05.26.116640; doi: https://doi.org/10.1101/2020.05.26.116640
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Evaluating single-cell cluster stability using the Jaccard similarity index
Ming Tang, Yasin Kaymaz, Brandon Logeman, Stephen Eichhorn, ZhengZheng S. Liang, Catherine Dulac, Timothy B. Sackton
bioRxiv 2020.05.26.116640; doi: https://doi.org/10.1101/2020.05.26.116640

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