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

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...