cFIT: Integration and transfer learning of single cell transcriptomes, illustrated by fetal brain cell development

Abstract Large, comprehensive collections of scRNA-seq data sets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these data sets or transfer knowledge from one to the other to better understand cellular identity and functions. Here, we present a simple yet surprisingly effective method named cFIT for capturing various batch effects across experiments, technologies, subjects, and even species. The proposed method models the shared information between various data sets by a common factor space, while allowing for unique distortions and shifts in gene-wise expression in each batch. The model parameters are learned under an iterative non-negative matrix factorization (NMF) framework and then used for synchronized integration from across-domain assays. In addition, the model enables transferring via low-rank matrix from more informative data to allow for precise identification in data of lower quality. Compared to existing approaches, our method imposes weaker assumptions on the cell composition of each individual data set, however, is shown to be more reliable in preserving biological variations. We apply cFIT to multiple scRNA-seq data sets of developing brain from human and mouse, varying by technologies and developmental stages. The successful integration and transfer uncover the transcriptional resemblance across systems. The study helps establish a comprehensive landscape of brain cell type diversity and provides insights into brain development.