Integration and transfer learning of single-cell transcriptomes via cFIT.

ABSTRACT: 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 d

SUBMITTER: Peng M 

PROVIDER: S-EPMC7958425 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature