Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing

Patrick S. Stumpf, Xin Du, Haruka Imanishi, Yuya Kunisaki, Yuichiro Semba, Timothy Noble, Rosanna C.G. Smith, Matthew Rose-Zerili, Jonathan J. West, Richard O.C. Oreffo, Katayoun Farrahi, Mahesan Niranjan, Koichi Akashi, Fumio Arai, Ben D. MacArthur

研究成果: Contribution to journalArticle査読

1 被引用数 (Scopus)


Biomedical research often involves conducting experiments on model organisms in the anticipation that the biology learnt will transfer to humans. Previous comparative studies of mouse and human tissues were limited by the use of bulk-cell material. Here we show that transfer learning—the branch of machine learning that concerns passing information from one domain to another—can be used to efficiently map bone marrow biology between species, using data obtained from single-cell RNA sequencing. We first trained a multiclass logistic regression model to recognize different cell types in mouse bone marrow achieving equivalent performance to more complex artificial neural networks. Furthermore, it was able to identify individual human bone marrow cells with 83% overall accuracy. However, some human cell types were not easily identified, indicating important differences in biology. When re-training the mouse classifier using data from human, less than 10 human cells of a given type were needed to accurately learn its representation. In some cases, human cell identities could be inferred directly from the mouse classifier via zero-shot learning. These results show how simple machine learning models can be used to reconstruct complex biology from limited data, with broad implications for biomedical research.

ジャーナルCommunications Biology
出版ステータス出版済み - 12 2020

All Science Journal Classification (ASJC) codes

  • 医学(その他)
  • 生化学、遺伝学、分子生物学(全般)
  • 農業および生物科学(全般)


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