Abstract
Single-cell RNA sequencing (scRNA-seq) is an emerging technology for profiling the gene expression of thousands of cells at the single cell resolution. Currently, the labeling of cells in an scRNA-seq dataset is performed by manually characterizing clusters of cells or by fluorescence-activated cell sorting (FACS). Both methods have inherent drawbacks: The first depends on the clustering algorithm used and the knowledge and arbitrary decisions of the annotator, and the second involves an experimental step in addition to the sequencing and cannot be incorporated into the higher throughput scRNA-seq methods. We therefore suggest a different approach for cell labeling, namely, classifying cells from scRNA-seq datasets by using a model transferred from different (previously labeled) datasets. This approach can complement existing methods, and-in some cases-even replace them. Such a transfer-learning framework requires selecting informative features and training a classifier. The specific implementation for the framework that we propose, designated ''CaSTLe-classification of single cells by transfer learning,'' is based on a robust feature engineering workflow and an XGBoost classification model built on these features. Evaluation of CaSTLe against two benchmark feature-selection and classification methods showed that it outperformed the benchmark methods in most cases and yielded satisfactory classification accuracy in a consistent manner. CaSTLe has the additional advantage of being parallelizable and well suited to large datasets. We showed that it was possible to classify cell types using transfer learning, even when the databases contained a very small number of genes, and our study thus indicates the potential applicability of this approach for analysis of scRNA-seq datasets.