ArticleProceedings of the National Academy of Sciences of the United States of America2021
Integration and transfer learning of single-cell transcriptomes via cFIT.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- An integrated single cell and spatial transcriptomic map of human white adipose tissue.Nature communications · 2023Pooled it
- Rare coding variation provides insight into the genetic architecture and phenotypic context of autism.Nature genetics · 2022Pooled it
- Spatiotemporal analysis of autism gene enrichment implicates cortex, thalamus, and hypothalamus.bioRxiv : the preprint server for biology · 2026Article
- Integrating and mapping single-cell transcriptomics across the entire gene expression space.Briefings in bioinformatics · 2026Article
- TransST: transfer learning embedded spatial factor modeling of spatial transcriptomics data.BMC bioinformatics · 2025Article
- Microbiome data integration via shared dictionary learning.Nature communications · 2025Article
- MOTL: enhancing multi-omics matrix factorization with transfer learning.Genome biology · 2025Article
- Integrating single-cell data with biological variables.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- CODI: Enhancing machine learning-based molecular profiling through contextual out-of-distribution integration.PNAS nexus · 2024Article
- Integration mapping of cardiac fibroblast single-cell transcriptomes elucidates cellular principles of fibrosis in diverse pathologies.Science advances · 2024Article
- Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors.Cancer research · 2024Article
- Epithelial zonation along the mouse and human small intestine defines five discrete metabolic domains.Nature cell biology · 2024Article
- Systems immunology spanning tumors, lymph nodes, and periphery.Cell reports methods · 2023Review
- Comparison of gene expression in living and postmortem human brain.medRxiv : the preprint server for health sciences · 2023Article
- Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles.bioRxiv : the preprint server for biology · 2023Article
- Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data.NAR genomics and bioinformatics · 2023Article
- GenomicSuperSignature facilitates interpretation of RNA-seq experiments through robust, efficient comparison to public databases.Nature communications · 2022Article
- Biologically relevant transfer learning improves transcription factor binding prediction.Genome biology · 2021Article
- Cell type hierarchy reconstruction via reconciliation of multi-resolution cluster tree.Nucleic acids research · 2021Article
- Transfer learning between preclinical models and human tumors identifies a conserved NK cell activation signature in anti-CTLA-4 responsive tumors.Genome medicine · 2021Article
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5 authors.
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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 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.
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