ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Identification of PBMC-related cells of single cell RNA sequence data

  • 1.

    School of computer science,Nanjing university of posts and telecommunications, Nanjing 210023, China

  • 2.

    Jiangsu Key Lab of Big Data Security& Intelligent Processing, Nanjing 210023, China

  • 3.

    Zhejiang Engineering Research Center of Intelligent Medicine, Wenzhou 325035, China

  • 4.

    Key Laboratory of Urban Land Resources Monitoring and Simulation, Shenzhen 518034, China

  • 5.

    Suzhou Privacy Information Technology Company, Suzhou 215011, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.07.019
  • Received Date: 03 June 2020
  • Accepted Date: 21 June 2020
  • Rev Recd Date: 21 June 2020
  • Publish Date: 31 July 2020
    Abstract

  • Abstract

    Cell type identification is one of the main tasks of single cell RNA sequencing. This paper proposes an automatic identification of cell types based on random forest (AICTRF) method to identify cell types in single-cell sequencing data. This method uses the random forest classification model for training, and then predicts unknown cell types according to the trained model. A random forest classification model was trained on human peripheral blood mononuclear cells (PBMC) sequencing data set to predict the cell types of related subtypes of human PBMC B cells. The results show that the proposed method can help researchers automatically identify cell types in single-cell sequencing data.

    Abstract

    Cell type identification is one of the main tasks of single cell RNA sequencing. This paper proposes an automatic identification of cell types based on random forest (AICTRF) method to identify cell types in single-cell sequencing data. This method uses the random forest classification model for training, and then predicts unknown cell types according to the trained model. A random forest classification model was trained on human peripheral blood mononuclear cells (PBMC) sequencing data set to predict the cell types of related subtypes of human PBMC B cells. The results show that the proposed method can help researchers automatically identify cell types in single-cell sequencing data.
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    • References(20)
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    CHEN P, LIN C, SCHOLKOPF B, et al. A tutorial on v-support vector machines[J]. Applied Stochastic Models in Business and Industry, 2005, 21(2): 111-136.
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    CUCCHIARA A, HOSMER D, LEMESHOW S. Applied logistic regression[J]. Technometrics, 1992, 34(3):358.
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    FREYTAG S, TIAN L, LONNSTEDT I, et al. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data[J]. 1000Research, 2018.
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    ZHANG X, LAN Y, XU J, et al. CellMarker: A manually curated resource of cell markers in human and mouse[J]. Nucleic Acids Research, 2019.
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    ZHOU Z H. Machine Learning[M]. Beijing: Tsinghua University Press, 2016:78-181.
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    SHIN S, PARK J S, KIM Y, et al. Differential gene expression profile in PBMCs from subjects with AERD and ATA: A gene marker for AERD[J]. Molecular Genetics and Genomics, 2012, 287(5): 361-371.
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    [1]
    PAPALEXI E, SATIJA R. Single-cell RNA sequencing to explore immune cell heterogeneity[J]. Nature Reviews Immunology, 2018, 18(1): 35-45.
    [2]
    RANTALAINEN M. Application of single-cell sequencing in human cancer[J]. Briefings in Functional Genomics, 2018, 17(4): 273-282.
    [3]
    POTTER S S. Single-cell RNA sequencing for the study of development, physiology and disease[J]. Nature Reviews Nephrology, 2018, 14(8): 479-492.
    [4]
    SVENSSON V, VENTOTORMO R, TEICHMANN S A, et al. Exponential scaling of single-cell RNA-seq in the past decade[J]. Nature Protocols, 2018, 13(4): 599-604.
    [5]
    VILLANI A, SATIJA R, REYNOLDS G, et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors[J]. Science, 2017： 356(6335).
    [6]
    GRUN D, LYUBIMOVA A, KESTER L, et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types[J]. Nature, 2015, 525(7568): 251-255.
    [7]
    TIROSH I, IZAR B, PRAKADAN S, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq[J]. Science, 2016, 352(6282): 189-196.
    [8]
    KERENSHAUL H, SPINRAD A, WEINER A, et al. A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease[J]. Cell, 2017, 169(7): 1276-1290.
    [9]
    DUO A, ROBINSON M D, SONESON C, et al. A systematic performance evaluation of clustering methods for single-cell RNA-seq data[J]. F1000Research, 2018.
    [10]
    KAKUSHADZE Z, YU W. *K-Means and Cluster Models for Cancer Signatures[J]. Biomolecular Detection and Quantification, 2017: 7-31.
    [11]
    SHLENS J. A tutorial on principal component analysis[J]. arXiv: Learning, 2014.
    [12]
    CUTLER D R, EDWARDS T C, BEARD K H, et al. Random forests for classification in ecology[J]. Ecology, 2007, 88(11): 2783-2792.
    [13]
    CHEN P, LIN C, SCHOLKOPF B, et al. A tutorial on v-support vector machines[J]. Applied Stochastic Models in Business and Industry, 2005, 21(2): 111-136.
    [14]
    CUCCHIARA A, HOSMER D, LEMESHOW S. Applied logistic regression[J]. Technometrics, 1992, 34(3):358.
    [15]
    FREYTAG S, TIAN L, LONNSTEDT I, et al. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data[J]. 1000Research, 2018.
    [16]
    ZHANG X, LAN Y, XU J, et al. CellMarker: A manually curated resource of cell markers in human and mouse[J]. Nucleic Acids Research, 2019.
    [17]
    ZHOU Z H. Machine Learning[M]. Beijing: Tsinghua University Press, 2016:78-181.
    [18]
    ZHANG X, MALLICK H, TANG Z, et al. Negative binomial mixed models for analyzing microbiome count data[J]. BMC Bioinformatics, 2017:18(1).
    [19]
    SHIN S, PARK J S, KIM Y, et al. Differential gene expression profile in PBMCs from subjects with AERD and ATA: A gene marker for AERD[J]. Molecular Genetics and Genomics, 2012, 287(5): 361-371.
    [23]
    CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: Synthetic minority over- sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16(1): 321-357. )
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