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  • 标题:Structure-preserving visualisation of high dimensional single-cell datasets
  • 本地全文:下载
  • 作者:Benjamin Szubert ; Jennifer E. Cole ; Claudia Monaco
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2019
  • 卷号:9
  • 期号:1
  • 页码:1-10
  • DOI:10.1038/s41598-019-45301-0
  • 出版社:Springer Nature
  • 摘要:Single-cell technologies offer an unprecedented opportunity to effectively characterize cellular heterogeneity in health and disease. Nevertheless, visualisation and interpretation of these multi-dimensional datasets remains a challenge. We present a novel framework, ivis, for dimensionality reduction of single-cell expression data. ivis utilizes a siamese neural network architecture that is trained using a novel triplet loss function. Results on simulated and real datasets demonstrate that ivis preserves global data structures in a low-dimensional space, adds new data points to existing embeddings using a parametric mapping function, and scales linearly to hundreds of thousands of cells. ivis is made publicly available through Python and R interfaces on https://github.com/beringresearch/ivis .
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