Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning

被引：115
|
作者：
Deng, Yue [1 ]
Bao, Feng [2 ]
Dai, Qionghai [2 ]
Wu, Lani F. [1 ]
Altschuler, Steven J. [1 ]
机构：
[1] Univ Calif San Francisco, Dept Pharmaceut Chem, San Francisco, CA 94143 USA
[2] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Dept Automat, Beijing, Peoples R China
来源：
NATURE METHODS | 2019年 / 16卷 / 04期
关键词：
NETWORK;
D O I：
10.1038/s41592-019-0353-7
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.
引用
收藏
页码：311 / +
页数：7
相关论文
共 50 条
  • [1] Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning
    Yue Deng
    Feng Bao
    Qionghai Dai
    Lani F. Wu
    Steven J. Altschuler
    Nature Methods, 2019, 16 : 311 - 314
  • [2] Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data
    Huang, Yixuan
    Zhang, Peng
    BRIEFINGS IN BIOINFORMATICS, 2021, 22 (05)
  • [3] Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data (vol 2021)
    Huang, Yixuan
    Zhang, Peng
    BRIEFINGS IN BIOINFORMATICS, 2021, 22 (06)
  • [4] scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network
    Shao, Xin
    Yang, Haihong
    Zhuang, Xiang
    Liao, Jie
    Yang, Penghui
    Cheng, Junyun
    Lu, Xiaoyan
    Chen, Huajun
    Fan, Xiaohui
    NUCLEIC ACIDS RESEARCH, 2021, 49 (21) : E122
  • [5] Clinical, cell-type, and architectural characterization of AFX and PDS using single-cell and spatial transcriptomics
    Klein, J. C.
    Hosler, G.
    Hon, G.
    JOURNAL OF INVESTIGATIVE DERMATOLOGY, 2024, 144 (08) : S144 - S144
  • [6] Acoel Single-Cell Transcriptomics: Cell Type Analysis of a Deep Branching Bilaterian
    Duruz, Jules
    Kaltenrieder, Cyrielle
    Ladurner, Peter
    Bruggmann, Remy
    Martinez, Pedro
    Sprecher, Simon G.
    MOLECULAR BIOLOGY AND EVOLUTION, 2021, 38 (05) : 1888 - 1904
  • [7] Single-cell transcriptomics revealed cell-type specific responses to fungal infection
    Tang, B.
    Ding, P.
    Feng, L.
    Ma, W.
    MOLECULAR PLANT-MICROBE INTERACTIONS, 2024, 37 (05) : 133 - 133
  • [8] Deep learning applications in single-cell genomics and transcriptomics data analysis
    Erfanian, Nafiseh
    Heydari, A. Ali
    Feriz, Adib Miraki
    Ianez, Pablo
    Derakhshani, Afshin
    Ghasemigol, Mohammad
    Farahpour, Mohsen
    Razavi, Seyyed Mohammad
    Nasseri, Saeed
    Safarpour, Hossein
    Sahebkar, Amirhossein
    BIOMEDICINE & PHARMACOTHERAPY, 2023, 165
  • [9] scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics
    Zheng, Chuanyang
    Wang, Yixuan
    Cheng, Yuqi
    Wang, Xuesong
    Wei, Hongxin
    King, Irwin
    Li, Yu
    BRIEFINGS IN BIOINFORMATICS, 2024, 25 (03)
  • [10] Batch alignment of single-cell transcriptomics data using deep metric learning
    Yu, Xiaokang
    Xu, Xinyi
    Zhang, Jingxiao
    Li, Xiangjie
    NATURE COMMUNICATIONS, 2023, 14 (01)
12345