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Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters

View ORCID ProfileSergii Domanskyi, Anthony Szedlak, Nathaniel T Hawkins, Jiayin Wang, Giovanni Paternostro, Carlo Piermarocchi
doi: https://doi.org/10.1101/539833
Sergii Domanskyi
1Department of Physics and Astronomy, Michigan State University, 48824 East Lansing, MI, USA.
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  • ORCID record for Sergii Domanskyi
  • For correspondence: domansk6@msu.edu
Anthony Szedlak
1Department of Physics and Astronomy, Michigan State University, 48824 East Lansing, MI, USA.
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Nathaniel T Hawkins
1Department of Physics and Astronomy, Michigan State University, 48824 East Lansing, MI, USA.
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Jiayin Wang
2Salgomed, Inc., 92014 Del Mar, CA, USA.
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Giovanni Paternostro
3Sanford Burnham Prebys Medical Research Institute, 92037 La Jolla, CA, USA.
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Carlo Piermarocchi
1Department of Physics and Astronomy, Michigan State University, 48824 East Lansing, MI, USA.
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Abstract

Background Single cell RNA sequencing (scRNA-seq) brings unprecedented opportunities for mapping the heterogeneity of complex cellular environments such as bone marrow, and provides insight into many cellular processes. Single cell RNA-seq, however, has a far larger fraction of missing data reported as zeros (dropouts) than traditional bulk RNA-seq. This makes difficult not only the clustering of cells, but also the assignment of the resulting clusters into predefined cell types based on known molecular signatures, such as the expression of characteristic cell surface markers.

Results We present a computational tool for processing single cell RNA-seq data that uses a voting algorithm to identify cells based on approval votes received by known molecular markers. Using a stochastic procedure that accounts for biases due to dropout errors and imbalances in the number of known molecular signatures for different cell types, the method computes the statistical significance of the final approval score and automatically assigns a cell type to clusters without an expert curator. We demonstrate the utility of the tool in the analysis of eight samples of bone marrow from the Human Cell Atlas. The tool provides a systematic identification of cell types in bone marrow based on a recently-published manually-curated cell marker database [1], and incorporates a suite of visualization tools that can be overlaid on a t-SNE representation. The software is freely available as a python package at https://github.com/sdomanskyi/DigitalCellSorter

Conclusions This methodology assures that extensive marker to cell type matching information is taken into account in a systematic way when assigning cell clusters to cell types. Moreover, the method allows for a high throughput processing of multiple scRNA-seq datasets, since it does not involve an expert curator, and it can be applied recursively to obtain cell sub-types. The software is designed to allow the user to substitute the marker to cell type matching information and apply the methodology to different cellular environments.

  • Abbreviations

    ARI
    Adjusted Rand Index
    BMMC
    Bone marrow mono-nuclear cells
    CD
    Clusters of Differentiation
    HCA
    Human Cell Atlas
    HCDM
    Human Cell Differentiation Molecules
    PBMC
    Peripheral blood mono-nuclear cells
    PCA
    Principal Component Analysis
    p-DCS
    Polled Digital Cell Sorter
    tSNE
    t-distributed Stochastic Neighbor Embedding
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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    Posted February 04, 2019.
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    Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters
    Sergii Domanskyi, Anthony Szedlak, Nathaniel T Hawkins, Jiayin Wang, Giovanni Paternostro, Carlo Piermarocchi
    bioRxiv 539833; doi: https://doi.org/10.1101/539833
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    Polled Digital Cell Sorter (p-DCS): Automatic identification of hematological cell types from single cell RNA-sequencing clusters
    Sergii Domanskyi, Anthony Szedlak, Nathaniel T Hawkins, Jiayin Wang, Giovanni Paternostro, Carlo Piermarocchi
    bioRxiv 539833; doi: https://doi.org/10.1101/539833

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