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

ABSTRACT: 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 h

SUBMITTER: Stumpf PS 

PROVIDER: S-EPMC7718277 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature