Limitations of cell embedding metrics assessed using drifting islands

De Sande, BV et al. Applications of unilateral RNA arrangement in drug discovery and development. NAT. Rev. Discov. 22496-520 (2023).
Google Academic
Zhang, MJ et al. Polenic enrichment distinguishes disease relationships of individual cells in single-celled RNA-SEQ data. NAT. Genet. 541572–1580 (2022).
Google Academic
Rood, Je et al. The effect of human cell atlas on medicine. NAT. Med. 282486-2496 (2022).
Google Academic
Rood, Je et al. Human cell atlas from a cell census to a basic model. Nature 6371065-1071 (2025).
Google Academic
Hao, Y. et al. Integrated analysis of multimodal single -celled data. Cell 1843573-3587.E29 (2021).
Google Academic
Theodoris, CV et al. Transfer learning provides estimates in network biology. Nature 618616-624 (2023).
Google Academic
Heimberg, G. et al. A basic model of a cell atlas for the diarrhebible quest of similar human cells. Nature 6381085-1094 (2025).
Google Academic
Rosen, Y. et al. Universal Cell Burial: A basic model for cell biology. Stand out Biorxiv https://doi.org/10.1101/2023.11.28.568918 (2023).
Cui, H. et al. SCGPT: Towards creating a basic model for single -celled multiple omics using the general AI. NAT. Method 211470–1480 (2024).
Google Academic
Hao, M. et al. Large -scale basic model on single -celled transcriptomic. NAT. Method 211481–1491 (2024).
Google Academic
Luecken, MD et al. Comparison of data integration at atlas level in single -celled genomics. NAT. Method 1941-50 (2022).
Google Academic
Tran, HTN et al. A criterion of collective impact correction methods for single -celled RNA sequence data. Genome Biol. 2112 (2020).
Google Academic
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 62047-60 (2023).
Google Academic
Liu, T., Li, K., Wang, Y., Li, H. & Zhao, H. Evaluation of the Benefits of Basic Models in Single Cell Data Analysis. Stand out Biorxiv https://doi.org/10.1101/2023.09.08.55192 (2023).
Kedzierska, Kz, Crawford, L., Amini, AP & LU, AX Zero-Swot assessment reveals the limitations of single-celled basic models. Genome Biol. 26101 (2025).
Google Academic
Zhang, H., Cisse, M., Dauphin, Yn and Lopez-Paz, D. Mixup: Beyond the minimization of empirical risk. Stand out https://arxiv.org/abs/1710.09412 (2018).
Siletti, K. et al. Transcriptomic diversity of cell types in adult human brain. Science 382EADD7046 (2023).
Google Academic
Kumar, T. et al. Adult human breast spatially dissolved single -celled genomic horse. Nature 620181–191 (2023).
Google Academic
Wang, SK et al. Single -celled multioma and deep learning of the human retina call causal variants in complex eye diseases. Cell genome. 2100164 (2022).
Google Academic
Elmentaite, R. et al. The unilateral sequence of the developing human intestine reveals transcriptional connections to Crohn’s disease in childhood. Giant. Cell 55771-783.E5 (2020).
Google Academic
Knight-Schrijver, VR et al. A single -celled comparison of adult and fetal human epicardies defines age -related changes in epicardial activity. NAT. Cardiovask. Res. 11215–1229 (2022).
Google Academic
O, P. et al. A human fetal lung cell atlas reveals proximal -distal differentiation gradients and key regulators of epithelial fate. Cell 1854841-4860.E25 (2022).
Google Academic
Solé-Boldo, L. et al. Single -celled transcriptoms of the human skin reveal the loss of age -related fibroblast priming. Commune. Biol. 3188 (2020).
Google Academic
Heumos, L. et al. The best practices for single -celled analysis in modalities. NAT. Rev. Genet. 24550-572 (2023).
Google Academic
Korsunsky, I. et al. Fast, sensitive and accurate integration of single -celled data with harmony. NAT. Method 161289–1296 (2019).
Google Academic
Hie, B., Bryson, B. & Berger, B. Effective integration of heterogeneous single -celled transcripts using Scanorama. NAT. Biotechnol. 37685-691 (2019).
Google Academic
Polański, K. et al. BBKNN: Rapid collective alignment of single cell transcripts. Bioinformatics 36964-965 (2020).
Google Academic
Haghverdi, L. et al. Collective effects in single -celled RNA sequence data are corrected by the matching of the nearest neighbors. NAT. Biotechnol. 36421–427 (2018).
Google Academic
Lopez, R. et al. Deep productive modeling for single -celled transcriptomic. NAT. Method 151053-1058 (2018).
Google Academic
XU, C. et al. Probably compliance and disclosure of single -celled transcriptomic data with deep productive models. Mole. SYST. Biol. 17E9620 (2021).
Google Academic
Lotfollahi, M., Wolf, Fa & Theis, FJ SCGEN envisages uniform perturbation responses. NAT. Method 16715–721 (2019).
Google Academic
De Donno, C. et al. The integration of single -celled data clusters at the population level provides a multi -scale analysis between samples. NAT. Method 201683-1692 (2023).
Google Academic
Khosla, P. et al. Controlled learning. Inside Developments in Neural Information Processing Systems 33 (Eds Larochelle, H. et al.) 18661-18673 (Neurips, 2020).
Hoffer, E. & Aileon, N. Trilateral Networking Deep Metric Learning. Inside Similarity -based pattern recognition: simbad 2015 (Eds waiver, A. et al.) 84-92 (Springer, 2015).
Sikkema, L. et al. An integrated cell Atlas of the human lung in health and disease. NAT. Med. 291563–1577 (2023).
Google Academic
XU, C. et al. Automatic cell type compliance and integration in human cell atlas data clusters. Cell 1865876-5891.E20 (2023).
Google Academic
Wolf, FA, Angerer, P. & Theis, Fj Scanpy: Large -scale single -celled gene expression data analysis. Genome Biol. 1915 (2018).
Google Academic
Visualization of data using Van der Maaten, L. & Hinton, G. T-Sne. J. Mach. To learn. Res. 92579-2605 (2008).
Google Academic
Becht, E. et al. Dimensional reduction for visualization of single -celled data using UMAP. NAT. Biotechnol. 3738-44 (2019).
Gayoso, A. et al. A Python Library for the probability of single -celled omic data. NAT. Biotechnol. 40163-166 (2022).
Google Academic
Su, Y. et al. Multi-OICS solves a sharp disease shift between light and moderate Covid-19. Cell 1831479–1495.E20 (2020).
Google Academic
Luecken, M. et al. Atlas level data integration in single-celled genomic comparison-integration task data clusters. figure https://doi.org/10.6084/m9.figshare.12420968 (2022).