Rigorous integration of single-cell ATAC-seq data using regularized barycentric mapping

Machine Learning


  • Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21–29 (2015).

    Google Scholar 

  • Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).

    Google Scholar 

  • Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).

    Google Scholar 

  • Gehring, J., Hwee Park, J., Chen, S., Thomson, M. & Pachter, L. Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nat. Biotechnol. 38, 35–38 (2020).

    Google Scholar 

  • Almanzar, N. et al. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).

    Google Scholar 

  • Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat. Methods 16, 1289–1296 (2019).

    Google Scholar 

  • Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Google Scholar 

  • Chazarra-Gil, R., van Dongen, S., Kiselev, V. Y. & Hemberg, M. Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench. Nucleic Acids Res. 49, e42–e42 (2021).

    Google Scholar 

  • Chen, S. et al. RA3 is a reference-guided approach for epigenetic characterization of single cells. Nat. Commun. 12, 2177 (2021).

    Google Scholar 

  • Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).

    Google Scholar 

  • Ashuach, T., Reidenbach, D. A., Gayoso, A. & Yosef, N. PeakVI: a deep generative model for single-cell chromatin accessibility analysis. Cell Rep. Methods 2, 100182 (2022).

    Google Scholar 

  • Kopp, W., Akalin, A. & Ohler, U. Simultaneous dimensionality reduction and integration for single-cell atac-seq data using deep learning. Nat. Mach. Intell. 4, 162–168 (2022).

    Google Scholar 

  • Xiong, L. et al. Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space. Nat. Commun. 13, 6118 (2022).

    Google Scholar 

  • Yuan, H. & Kelley, D. R. scBasset: sequence-based modeling of single-cell ATAC-seq using convolutional neural networks. Nat. Methods 19, 1088–1096 (2022).

    Google Scholar 

  • McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. Preprint at https://arxiv.org/abs/1802.03426 (2020).

  • Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    Google Scholar 

  • Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022).

    Google Scholar 

  • Grandi, F. C., Modi, H., Kampman, L. & Corces, M. R. Chromatin accessibility profiling by ATAC-seq. Nat. Protoc. 17, 1518–1552 (2022).

    Google Scholar 

  • Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).

    Google Scholar 

  • Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).

    Google Scholar 

  • Wang, A. et al. Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. eLife 9, e62522 (2020).

    Google Scholar 

  • Yuan, W. et al. Temporally divergent regulatory mechanisms govern neuronal diversification and maturation in the mouse and marmoset neocortex. Nat. Neurosci. 25, 1049–1058 (2022).

    Google Scholar 

  • Wilcoxon, F., Katti, S. K. & Wilcox, R. A. in Critical Values and Probability Levels for the Wilcoxon Rank Sum Test and the Wilcoxon Signed Rank Test, Vol. 1. 171–259 (American Cyanamid, 1963).

  • Chen, Y. T. & Zou, J. GenePT: a simple but hard-to-beat foundation model for genes and cells built from chatgpt. Preprint at bioRxiv https://doi.org/10.1101/2023.10.16.562533 (2024).

  • Danese, A. et al. EpiScanpy: integrated single-cell epigenomic analysis. Nat. Commun. 12, 5228 (2021).

    Google Scholar 

  • Abdelaal, T. et al. A comparison of automatic cell identification methods for single-cell RNA sequencing data. Genome Biol. 20, 194 (2019).

    Google Scholar 

  • Li, Z. et al. Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen. Nat. Commun. 12, 6386 (2021).

    Google Scholar 

  • Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).

    Google Scholar 

  • Slowikowski, K., Hu, X. & Raychaudhuri, S. SNPsea: an algorithm to identify cell types, tissues and pathways affected by risk loci. Bioinformatics 30, 2496–2497 (2014).

    Google Scholar 

  • Manz, M. G. & Boettcher, S. Emergency granulopoiesis. Nat. Rev. Immunol. 14, 302–314 (2014).

    Google Scholar 

  • Rock, J. R. & Hogan, B. L. Epithelial progenitor cells in lung development, maintenance, repair, and disease. Annu. Rev. Cell Dev. Biol. 27, 493–512 (2011).

    Google Scholar 

  • Fonsatti, E., Altomonte, M., Nicotra, M. R., Natali, P. G. & Maio, M. Endoglin (CD105): a powerful therapeutic target on tumor-associated angiogenetic blood vessels. Oncogene 22, 6557–6563 (2003).

    Google Scholar 

  • Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    Google Scholar 

  • Dendrou, C. A., Fugger, L. & Friese, M. A. Immunopathology of multiple sclerosis. Nat. Rev. Immunol. 15, 545–558 (2015).

    Google Scholar 

  • Friese, M. A. & Fugger, L. Pathogenic CD8+ T cells in multiple sclerosis. Ann. Neurol. 66, 132–141 (2009).

    Google Scholar 

  • Lu, L. et al. Regulation of activated CD4+ T cells by NK cells via the Qa-1–NKG2A inhibitory pathway. Immunity 26, 593–604 (2007).

    Google Scholar 

  • McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Google Scholar 

  • Chatzileontiadou, D. S., Sloane, H., Nguyen, A. T., Gras, S. & Grant, E. J. The many faces of CD4+ T cells: Immunological and structural characteristics. Int. J. Mol. Sci. 22, 73 (2020).

    Google Scholar 

  • Zhu, J., Yamane, H. & Paul, W. E. Differentiation of effector CD4 T cell populations. Annu. Rev. Immunol. 28, 445–489 (2009).

    Google Scholar 

  • Sakaguchi, S., Yamaguchi, T., Nomura, T. & Ono, M. Regulatory T cells and immune tolerance. Cell 133, 775–787 (2008).

    Google Scholar 

  • Smith-Garvin, J. E., Koretzky, G. A. & Jordan, M. S. T cell activation. Annu. Rev. Immunol. 27, 591–619 (2009).

    Google Scholar 

  • Gordon, S. & Taylor, P. R. Monocyte and macrophage heterogeneity. Nat. Rev. Immunol. 5, 953–964 (2005).

    Google Scholar 

  • Wang, Y. et al. The essential role of transcription factor Pitx3 in preventing mesodiencephalic dopaminergic neurodegeneration and maintaining neuronal subtype identities during aging. Cell Death Dis. 12, 1008 (2021).

    Google Scholar 

  • Pei, J. et al. Integrated analysis reveals FLI1 regulates the tumor immune microenvironment via its cell-type-specific expression and transcriptional regulation of distinct target genes of immune cells in breast cancer. BMC Genomics 25, 250 (2024).

    Google Scholar 

  • Goodnight, A. V. et al. Chromatin accessibility and transcription dynamics during in vitro astrocyte differentiation of Huntington’s Disease Monkey pluripotent stem cells. Epigenet. Chromatin 12, 67 (2019).

    Google Scholar 

  • Demetci, P., Santorella, R., Sandstede, B., Noble, W. S. & Singh, R. SCOT: single-cell multi-omics alignment with optimal transport. J. Comput. Biol. 29, 3–18 (2022).

    MathSciNet 

    Google Scholar 

  • Cang, Z. & Nie, Q. Inferring spatial and signaling relationships between cells from single cell transcriptomic data. Nat. Commun. 11, 2084 (2020).

    Google Scholar 

  • Cao, K., Hong, Y. & Wan, L. Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona. Bioinformatics 38, 211–219 (2021).

    Google Scholar 

  • Cao, K., Gong, Q., Hong, Y. & Wan, L. A unified computational framework for single-cell data integration with optimal transport. Nat. Commun. 13, 7419 (2022).

    Google Scholar 

  • Samaran, J., Peyré, G. & Cantini, L. scConfluence: single-cell diagonal integration with regularized inverse optimal transport on weakly connected features. Nat. Commun. 15, 7762 (2024).

    Google Scholar 

  • Villani, C. Optimal Transport: Old and New vol. 338 (Springer, 2009).

  • Peyré, G. & Cuturi, M. Computational optimal transport. Found. Trends Mach. Learn. 11, 355–607 (2019).

    Google Scholar 

  • Fefferman, C., Mitter, S. & Narayanan, H. Testing the manifold hypothesis. J. Am. Math. Soc. 29, 983–1049 (2016).

    MathSciNet 

    Google Scholar 

  • Jost, J. Riemannian Geometry and Geometric Analysis (Springer, 2017).

  • Tenenbaum, J. B., De Silva, V. & Langford, J. C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000).

    Google Scholar 

  • Reuter, M., Biasotti, S., Giorgi, D., Patanè, G. & Spagnuolo, M. Discrete Laplace–Beltrami operators for shape analysis and segmentation. Comput. Graph. 33, 381–390 (2009).

    Google Scholar 

  • Belkin, M. & Niyogi, P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15, 1373–1396 (2003).

    Google Scholar 

  • Cui, Z., Chang, H., Shan, S. & Chen, X. Generalized unsupervised manifold alignment. In Advances in Neural Information Processing Systems (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. & Weinberger, K. Q.) 27, 2429–2437 (Curran Associates, Inc., 2014).

  • Perrot, M., Courty, N., Flamary, R. & Habrard, A. Mapping estimation for discrete optimal transport. In Advances in Neural Information Processing Systems (eds Lee, D., Sugiyama, M., Luxburg, U., Guyon, I. & Garnett, R.) 29, 4197–4205 (Curran Associates, Inc., 2016).

  • Zhou, P. et al. Towards theoretically understanding why SGD generalizes better than ADAM in deep learning. Adv. Neural Inf. Process. Syst. 33, 21285–21296 (2020).

    Google Scholar 

  • Fatras, K., Sejourne, T., Flamary, R. & Courty, N. Unbalanced minibatch optimal transport: applications to domain adaptation. Proc. 38th Int. Conf. Mach. Learn. 139, 3186–3197 (2021).

    Google Scholar 

  • Hendrycks, D. & Gimpel, K. Gaussian error linear units (GELUs). Preprint at https://arxiv.org/abs/1606.08415 (2016).

  • Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Google Scholar 

  • Zhu, S., Hua, H. & Chen, S. Rigorous integration of single-cell ATAC-seq data using regularized barycentric mapping. Zenodo https://doi.org/10.5281/zenodo.14924285 (2025).



  • Source link

    Leave a Reply

    Your email address will not be published. Required fields are marked *