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For: Hao Y, Stuart T, Kowalski M, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-granda C, Satija R. Dictionary learning for integrative, multimodal, and scalable single-cell analysis.. [DOI: 10.1101/2022.02.24.481684] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
Number Citing Articles
1 Zhang Z, Sun H, Mariappan R, Chen X, Chen X, Jain MS, Efremova M, Teichmann SA, Rajan V, Zhang X. scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection. Nat Commun 2023;14:384. [PMID: 36693837 DOI: 10.1038/s41467-023-36066-2] [Reference Citation Analysis]
2 van Buijtenen E, Janssen W, Vink P, Habraken MJM, Wingens LJA, van Elsas A, Huck WTS, van Buggenum JAGL, van Eenennaam H. Integrated single-cell (phospho-)protein and RNA detection uncovers phenotypic characteristics and active signal transduction of human antibody secreting cells. Mol Cell Proteomics 2023;:100492. [PMID: 36623694 DOI: 10.1016/j.mcpro.2023.100492] [Reference Citation Analysis]
3 Wang Y, Sun X, Zhao H. Benchmarking automated cell type annotation tools for single-cell ATAC-seq data. Front Genet 2022;13:1063233. [PMID: 36583014 DOI: 10.3389/fgene.2022.1063233] [Reference Citation Analysis]
4 Li H, Zhang Z, Squires M, Chen X, Zhang X. scMultiSim: simulation of multi-modality single cell data guided by cell-cell interactions and gene regulatory networks.. [DOI: 10.1101/2022.10.15.512320] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Wang Y, Sun X, Zhao H. Benchmarking Automated Cell Type Annotation Tools for Single-cell ATAC-seq Data.. [DOI: 10.1101/2022.10.05.511014] [Reference Citation Analysis]
6 Clark IC, Fontanez KM, Meltzer RH, Xue Y, Hayford C, May-zhang A, D’amato C, Osman A, Zhang JQ, Hettige P, Delley CL, Weisgerber DW, Replogle JM, Jost M, Phong KT, Kennedy VE, Peretz CAC, Kim EA, Song S, Karlon W, Weissman JS, Smith CC, Gartner ZJ, Abate AR. Microfluidics-free single-cell genomics with templated emulsification.. [DOI: 10.1101/2022.06.10.495582] [Reference Citation Analysis]
7 Zhang Z, Sun H, Mariappan R, Chen X, Chen X, Jain MS, Efremova M, Teichmann SA, Rajan V, Zhang X. scMoMaT: Mosaic integration of single cell multi-omics data using matrix tri-factorization.. [DOI: 10.1101/2022.05.17.492336] [Reference Citation Analysis]
8 Fulcher JM, Markillie LM, Mitchell HD, Williams SM, Engbrecht KM, Moore RJ, Cantlon-bruce J, Bagnoli JW, Seth A, Paša-tolić L, Zhu Y. Parallel measurement of transcriptomes and proteomes from same single cells using nanodroplet splitting.. [DOI: 10.1101/2022.05.17.492137] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Myers RM, Izzo F, Kottapalli S, Prieto T, Dunbar A, Bowman RL, Mimitou EP, Stahl M, El Ghaity-beckley S, Arandela J, Raviram R, Ganesan S, Mekerishvili L, Hoffman R, Chaligné R, Abdel-wahab O, Smibert P, Marcellino B, Levine RL, Landau DA. Integrated Single-Cell Genotyping and Chromatin Accessibility Charts JAK2V617F Human Hematopoietic Differentiation.. [DOI: 10.1101/2022.05.11.491515] [Reference Citation Analysis]
10 Booeshaghi AS, Hallgrímsdóttir IB, Gálvez-merchán Á, Pachter L. Depth normalization for single-cell genomics count data.. [DOI: 10.1101/2022.05.06.490859] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
11 van Buijtenen E, Janssen W, Vink P, Habraken MJ, Wingens LJA, van Elsas A, Huck WT, van Buggenum JA, van Eenennaam H. Integrated single-cell (phospho-)protein and RNA detection uncovers phenotypic characteristics of human antibody secreting cells.. [DOI: 10.1101/2022.03.31.486501] [Reference Citation Analysis]