Integration and transfer learning of single-cell transcriptomes via cFIT

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 10; pp. 1 - 8
• Main Authors: Peng, Minshi, Li, Yue, Wamsley, Brie, Wei, Yuting, Roeder, Kathryn
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 09.03.2021
• Subjects: Animals; Biological Sciences; Brain; Datasets; Developmental stages; Exome Sequencing; Gene sequencing; Humans; Integration; Iterative methods; Knowledge management; Learning; Machine Learning; Mice; Physical Sciences; Ribonucleic acid; RNA; RNA-Seq; Single-Cell Analysis; Software; Transcription; Transcriptome; Transfer learning; transfer learning; single-cell RNA-seq; brain cells; data integration
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2024383118

Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets 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 datasets 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 common factor integration and transfer learning (cFIT) for capturing various batch effects across experiments, technologies, subjects, and even species. The proposed method models the shared information between various datasets by a common factor space while allowing for unique distortions and shifts in genewise expression in each batch. The model parameters are learned under an iterative nonnegative 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 with existing approaches, our method imposes weaker assumptions on the cell composition of each individual dataset; however, it is shown to be more reliable in preserving biological variations. We apply cFIT to multiple scRNA-seq datasets 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.