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.

生物医学研究通常涉及对模型生物进行实验，期望所学到的生物学知识能够转移到人类身上。先前对小鼠和人体组织的比较研究因使用大体积细胞材料而受到限制。在这里，我们展示了迁移学习——机器学习的一个分支，涉及将信息从一个域传递到另一个域——可以使用从单细胞 RNA 测序获得的数据来有效地绘制物种之间的骨髓生物学图谱。我们首先训练了一个多类逻辑回归模型来识别小鼠骨髓中的不同细胞类型，实现与更复杂的人工神经网络相当的性能。此外，它能够以 83% 的总体准确度识别单个人类骨髓细胞。然而，一些人类细胞类型不容易识别，这表明生物学上存在重要差异。当使用人类数据重新训练小鼠分类器时，需要不到 10 个给定类型的人类细胞来准确学习其表示。在某些情况下，人类细胞身份可以通过零样本学习直接从小鼠分类器推断出来。这些结果展示了如何使用简单的机器学习模型从有限的数据中重建复杂的生物学，这对生物医学研究具有广泛的影响。