Computational methods for single-cell omics across modalities

Comprehensive Integration of Single-Cell Data

Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works

Spatial reconstruction of single-cell gene expression data

Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · see citing works

Scenic: single-cell regulatory network inference and clustering.

Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works

CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449 · see citing works

Simultaneous epitope and transcriptome measurement in single cells

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098 · see citing works

Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity

Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works

MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data

Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works

Single-cell spatial reconstruction reveals global division of labour in the mammalian liver

Halpern, K. B., Shenhav, R., Matcovitch-Natan, O., Tóth, B., Lemze, D., Golan, M., Massasa, E. E., Baydatch, S., Landen, S., Moor, A. E., et al. (2017) · Nature · doi:10.1038/nature21065 · Cited by 1,059 · see citing works

Multiplexed quantification of proteins and transcripts in single cells

Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832 · see citing works

Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics

Ludwig, L. S., Lareau, C. A., Ulirsch, J. C., Christian, E., Muus, C., Li, L. H., Pelka, K., Ge, W. H., Oren, Y., Brack, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.022 · Cited by 534 · see citing works

High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin

Achim, K., Pettit, J.-B., Saraiva, L. R., Gavriouchkina, D., Larsson, T., Arendt, D., & Marioni, J. C. (2015) · Nature Biotechnology · doi:10.1038/nbt.3209 · Cited by 395 · see citing works

Single-Cell Transcriptomics Meets Lineage Tracing

Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375 · see citing works

The Drosophila embryo at single-cell transcriptome resolution

Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N., & Zinzen, R. P. (2017) · Science · doi:10.1126/science.aan3235 · Cited by 371 · see citing works

Conditional density-based analysis of T cell signaling in single-cell data

Krishnaswamy, S., Spitzer, M. H., Mingueneau, M., Bendall, S. C., Litvin, O., Stone, E., Pe’er, D., & Nolan, G. P. (2014) · Science · doi:10.1126/science.1250689 · Cited by 225 · see citing works

Profiling of pluripotency factors in single cells and early embryos

Hainer, S. J., Boskovic, A., McCannell, K. N., Rando, O. J., & Fazzio, T. (2019) · Cell · doi:10.1016/j.cell.2019.03.014 · Cited by 171 · see citing works

Transcript-indexed ATAC-seq for precision immune profiling

Satpathy, A. T., Saligrama, N., Buenrostro, J. D., Wei, Y., Wu, B., Rubin, A. J., Granja, J. M., Lareau, C. A., Li, R., Qi, Y., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0008-8 · Cited by 152 · see citing works

Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA

Xu, J., Nuno, K. A., Litzenburger, U. M., Qi, Y., Corces, M. R., Majeti, R., & Chang, H. Y. (2019) · eLife · doi:10.7554/elife.45105 · Cited by 150 · see citing works

MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Welch, J. D., Hartemink, A. J., & Prins, J. F. (2017) · Genome Biology · doi:10.1186/s13059-017-1269-0 · Cited by 140 · see citing works

Cell-type-specific signaling networks in heterocellular organoids

Qin, X., Sufi, J., Vlckova, P., Kyriakidou, P., Acton, S. E., Li, V. S. W., Nitz, M., & Tape, C. J. (2020) · Nature Methods · doi:10.1038/s41592-020-0737-8 · Cited by 130 · see citing works

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

Lopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M. I., & Yosef, N. (2019) · arXiv · doi:10.48550/arxiv.1905.02269 · Cited by 105 · see citing works

Nested effects models for high-dimensional phenotyping screens

Markowetz, F. M., Kostka, D., Troyanskaya, O. G., & Spang, R. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm178 · Cited by 103 · see citing works

Single-Cell Multi-omics: An Engine for New Quantitative Models of Gene Regulation

Packer, J. S., & Trapnell, C. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.06.001 · Cited by 84 · see citing works

CellPhoneDB v2.0: Inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexesPreprint

Efremova, M., Vento-Tormo, M., Teichmann, S. A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/680926 · Cited by 71 · see citing works

Statistical single cell multi-omics integration

Colomé‐Tatché, M., & Theis, F. J. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.01.003 · Cited by 70 · see citing works

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63 · see citing works

Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expressionPreprint

Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia‐Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/630996 · Cited by 57 · see citing works

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell dataPreprint

Argelaguet, R., Arnol, D., Bredikhin, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/837104 · Cited by 23 · see citing works

Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variantsPreprint

McCarthy, D. J., Rostom, R., Huang, Y., Kunz, D. J., Danecek, P., Bonder, M. J., Hagai, T., Wang, W., Gaffney, D. J., Simons, B. D., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/413047 · Cited by 13 · see citing works

A Joint Model of RNA Expression and Surface Protein Abundance in Single CellsPreprint

Gayoso, A., Lopez, R., Steier, Z., Regier, J., Streets, A., & Yosef, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/791947 · Cited by 13 · see citing works

Single cell network analysis with a mixture of Nested Effects Models

Pirkl, M., & Beerenwinkel, N. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty602 · Cited by 12 · see citing works

Single-Cell Signalling Analysis of Heterocellular OrganoidsPreprint

Qin, X. Q., Sufi, J., Vlckova, P., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/659896 · Cited by 8 · see citing works

CRISPR Screening in Single Cells

Henriksson, J. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9240-9_23 · Cited by 3 · see citing works

Integrated analysis of multimodal single-cell data

Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works

Fibrosis: from mechanisms to medicines

Henderson, N. C., Rieder, F., & Wynn, T. A. (2020) · Nature · doi:10.1038/s41586-020-2938-9 · Cited by 1,868 · see citing works

Methods and applications for single-cell and spatial multi-omics

Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244 · see citing works

Spatial omics and multiplexed imaging to explore cancer biology

Lewis, S. M., Asselin-Labat, M.-L., Nguyen, Q. H., Berthelet, J., Tan, X., Wimmer, V. C., Mérino, D., Rogers, K. L., & Naik, S. H. (2021) · Nature Methods · doi:10.1038/s41592-021-01203-6 · Cited by 609 · see citing works

Joint probabilistic modeling of single-cell multi-omic data with totalVI

Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592 · see citing works

Single cell transcriptomics comes of age

Aldridge, S., & Teichmann, S. A. (2020) · Nature Communications · doi:10.1038/s41467-020-18158-5 · Cited by 360 · see citing works

Single-cell RNA sequencing in cardiovascular development, disease and medicine

Paik, D. T., Cho, S., Tian, L., Chang, H. Y., & Wu, J. C. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-0359-y · Cited by 306 · see citing works

Single-Cell Transcriptome Analysis in Plants: Advances and Challenges

Shaw, R., Tian, X., & Xu, J. (2020) · Molecular Plant · doi:10.1016/j.molp.2020.10.012 · Cited by 296 · see citing works

Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes

Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works

MUON: multimodal omics analysis framework

Bredikhin, D., Kats, I., & Stegle, O. (2022) · Genome Biology · doi:10.1186/s13059-021-02577-8 · Cited by 216 · see citing works

Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID

Cortal, A., Martignetti, L., Six, E. M., & Rausell, A. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00896-6 · Cited by 155 · see citing works

LifeTime and improving European healthcare through cell-based interceptive medicine

Rajewsky, N., Almouzni, G., Gorski, S. A., Aerts, S., Amit, I., Bertero, M. G., Bock, C., Bredenoord, A. L., Cavalli, G., Chiocca, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2715-9 · Cited by 156 · see citing works

Understanding cell‐cell communication and signaling in the colorectal cancer microenvironment

AlMusawi, S., Ahmed, M., & Nateri, A. S. (2021) · Clinical and Translational Medicine · doi:10.1002/ctm2.308 · Cited by 150 · see citing works

scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

Song, D., Wang, Q., Yan, G., Liu, T., Sun, T., & Li, J. J. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01772-1 · Cited by 148 · see citing works

Next-Generation Lineage Tracing and Fate Mapping to Interrogate Development

VanHorn, S., & Morris, S. A. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.10.021 · Cited by 133 · see citing works

Unsupervised topological alignment for single-cell multi-omics integration

Cao, K., Bai, X., Hong, Y., & Wan, L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa443 · Cited by 130 · see citing works

Systems biology for crop improvement

Pazhamala, L. T., Kudapa, H., Weckwerth, W., Millar, A. H., & Varshney, R. K. (2021) · The Plant Genome · doi:10.1002/tpg2.20098 · Cited by 133 · see citing works

Statistical mechanics meets single-cell biology

Teschendorff, A. E., & Feinberg, A. P. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00341-z · Cited by 117 · see citing works

Single-cell transcriptomics for the assessment of cardiac disease

Miranda, A. M. A., Janbandhu, V., Maatz, H., Kanemaru, K., Cranley, J., Teichmann, S. A., Hübner, N., Schneider, M. D., Harvey, R. P., & Noseda, M. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00805-7 · Cited by 117 · see citing works

Computational strategies for single-cell multi-omics integration

Adossa, N. A., Khan, S., Rytkönen, K. T., & Elo, L. L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.060 · Cited by 106 · see citing works

A unified computational framework for single-cell data integration with optimal transport

Cao, K., Gong, Q., Hong, Y., & Wan, L. (2022) · Nature Communications · doi:10.1038/s41467-022-35094-8 · Cited by 105 · see citing works

Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona

Cao, K., Hong, Y., & Wan, L. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab594 · Cited by 106 · see citing works

A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data

Li, G., Fu, S., Wang, S., Zhu, C., Duan, B., Tang, C., Chen, X., Chuai, G., Wang, P. Q., & Liu, Q. (2022) · Genome biology · doi:10.1186/s13059-021-02595-6 · Cited by 100 · see citing works

Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions

Xiong, H., Luo, Y., Wang, Q., Yu, X., & He, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01129-z · Cited by 100 · see citing works

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Li, Y., Ma, L., Wu, D., & Chen, G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab024 · Cited by 101 · see citing works

Multiplex bioimaging of single-cell spatial profiles for precision cancer diagnostics and therapeutics

Allam, M., Cai, S., & Coskun, A. F. (2020) · npj Precision Oncology · doi:10.1038/s41698-020-0114-1 · Cited by 93 · see citing works

Feature selection revisited in the single-cell era

Yang, P., Huang, H. M., & Liu, C. (2021) · Genome biology · doi:10.1186/s13059-021-02544-3 · Cited by 81 · see citing works

Enhancing scientific discoveries in molecular biology with deep generative models

Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199198 · Cited by 82 · see citing works

Big data, integrative omics and network biology

Tolani, P., Gupta, S., Yadav, K., Aggarwal, S., & Yadav, A. K. (2021) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2021.03.006 · Cited by 83 · see citing works

Multimodal single cell data integration challenge: results and lessons learned

Lance, C., Luecken, M. D., Burkhardt, D. B., Cannoodt, R., Rautenstrauch, P., Laddach, A., Ubingazhibov, A., Cao, Z.-J., Deng, K., Khan, S., et al. (2022) · doi:10.1101/2022.04.11.487796 · Cited by 81 · see citing works

MultiMAP: dimensionality reduction and integration of multimodal data

Jain, M. S., Polański, K., Conde, C. D., Chen, X. X., Park, J.-E., Mamanova, L., Knights, A. J., Botting, R. A., Stephenson, E., Haniffa, M. A., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02565-y · Cited by 77 · see citing works

Bi-order multimodal integration of single-cell data

Dou, J., Liang, S., Mohanty, V., Miao, Q., Huang, Y., Liang, Q., Cheng, X., Kim, S., Choi, J., Li, Y., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02679-x · Cited by 62 · see citing works

Differentiation and activation of fibroblastic reticular cells

Lütge, M., Pikor, N. B., & Ludewig, B. (2021) · Immunological Reviews · doi:10.1111/imr.12981 · Cited by 61 · see citing works

Links between Nutrition, Infectious Diseases, and Microbiota: Emerging Technologies and Opportunities for Human-Focused Research

Cassotta, M., Forbes-Hernández, T. Y., Iglesias, R. C., Ruíz, R., Elexpuru-Zabaleta, M., Giampieri, F., & Battino, M. A. (2020) · Nutrients · doi:10.3390/nu12061827 · Cited by 57 · see citing works

Introducing single cell stereo-sequencing technology to transform the plant transcriptome landscape

Bawa, G., Liu, Z., Yu, X., Tran, L. P., & Sun, X. (2023) · Trends in Plant Science · doi:10.1016/j.tplants.2023.10.002 · Cited by 57 · see citing works

Single-cell joint profiling of multiple epigenetic proteins and gene transcription

Xiong, H., Wang, Q., Li, C. C., & He, A. (2024) · Science Advances · doi:10.1126/sciadv.adi3664 · Cited by 56 · see citing works

Integration of single-cell multi-omics for gene regulatory network inference

Hu, X., Hu, Y., Wu, F., Leung, R. W. T., & Qin, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.033 · Cited by 53 · see citing works

Network Approaches for Dissecting the Immune System

Shi, H., Yan, K., Ding, L., Qian, C., Chi, H., & Yu, J. (2020) · iScience · doi:10.1016/j.isci.2020.101354 · Cited by 50 · see citing works

Cut&tag: a powerful epigenetic tool for chromatin profiling

Fu, Z., Jiang, S., Sun, Y., zheng, S., Zong, L., & Li, P. (2023) · Epigenetics · doi:10.1080/15592294.2023.2293411 · Cited by 51 · see citing works

From Brain Organoids to Networking Assembloids: Implications for Neuroendocrinology and Stress Medicine

Makrygianni, E. A., & Chrousos, G. P. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.621970 · Cited by 47 · see citing works

Benchmarking single-cell multi-modal data integrations

Fu, S., Wang, S.-G., Si, D., Li, G., Gao, Y., & Liu, Q. (2025) · Nature Methods · doi:10.1038/s41592-025-02737-9 · Cited by 46 · see citing works

scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell–cell interactions

Li, H., Zhang, Z., Squires, M., Chen, X. X., & Zhang, X. (2025) · Nature Methods · doi:10.1038/s41592-025-02651-0 · Cited by 61 · see citing works

scLink: Inferring Sparse Gene Co-Expression Networks from Single-Cell Expression Data

Li, W. V., & Li, Y. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.11.006 · Cited by 45 · see citing works

Machine learning for single-cell genomics data analysis

Raimundo, F., Meng-Papaxanthos, L., Vallot, C., & Vert, J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.04.006 · Cited by 42 · see citing works

Single‐cell omics: Overview, analysis, and application in biomedical science

Stein, C. M., Weiskirchen, R., Damm, F., & Strzelecka, P. M. (2021) · Journal of Cellular Biochemistry · doi:10.1002/jcb.30134 · Cited by 42 · see citing works

Computational Methods for Single-Cell Imaging and Omics Data Integration

Watson, E. R., Fard, A. T., & Mar, J. C. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2021.768106 · Cited by 41 · see citing works

Single-Cell Multiomics

Flynn, E., Almonte-Loya, A., & Fragiadakis, G. K. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020422-050645 · Cited by 40 · see citing works

MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Yu, H., & Welch, J. D. (2021) · Genome Biology · doi:10.1186/s13059-021-02373-4 · Cited by 38 · see citing works

Multi-task learning from multimodal single-cell omics with Matilda

Liu, C., Huang, H. M., & Yang, P. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad157 · Cited by 36 · see citing works

Integration of gene expression and DNA methylation data across different experiments

Itai, Y., Rappoport, N., & Ron Shamir, A. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad566 · Cited by 35 · see citing works

LACE: Inference of cancer evolution models from longitudinal single-cell sequencing data

Ramazzotti, D., Angaroni, F., Maspero, D., Ascolani, G., Castiglioni, I., Piazza, R., Antoniotti, M., & Graudenzi, A. (2021) · Journal of Computational Science · doi:10.1016/j.jocs.2021.101523 · Cited by 35 · see citing works

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Vlachavas, E., Bohn, J. R., Ückert, F., & Nürnberg, S. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22062822 · Cited by 36 · see citing works

Applications of Single-Cell Omics to Dissect Tumor Microenvironment

Guo, T., Li, W., & Cai, X. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.548719 · Cited by 33 · see citing works

Joint learning of data recovering and graph contrastive denoising for incomplete multi-view clustering

Wang, H., Wang, Q., Miao, Q., & Ma, X. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102155 · Cited by 32 · see citing works

Machine learning methods to model multicellular complexity and tissue specificity

Sealfon, R. S. G., Wong, A. K., & Troyanskaya, O. G. (2021) · Nature Reviews Materials · doi:10.1038/s41578-021-00339-3 · Cited by 30 · see citing works

sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network

Xu, Y., Begoli, E., & McCord, R. P. (2022) · npj Systems Biology and Applications · doi:10.1038/s41540-022-00245-6 · Cited by 30 · see citing works

MOJITOO: a fast and universal method for integration of multimodal single-cell data

Cheng, M., Li, Z., & Costa, I. G. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac220 · Cited by 28 · see citing works

Deciphering Organoids: High-Dimensional Analysis of Biomimetic Cultures

Qin, X., & Tape, C. J. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.10.013 · Cited by 27 · see citing works

Single-cell omics: A new direction for functional genetic research in human diseases and animal models

Kong, S., Li, R., Tian, Y., Zhang, Y., Lu, Y., Ou, Q., Gao, P., Li, K., & Zhang, Y. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1100016 · Cited by 27 · see citing works

Elucidation of Biological Networks across Complex Diseases Using Single-Cell Omics

Li, Y., Ma, A., Mathé, E. A., Li, L., Liu, B., & Ma, Q. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.08.004 · Cited by 25 · see citing works

Single-Cell Analysis of the Transcriptome and Epigenome

Mazan-Mamczarz, K., Ha, J., De, S., & Sen, P. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1831-8_3 · Cited by 25 · see citing works

A Comprehensive Review of Multimodal Large Language Models for Medical Imaging and Omics Data

Vavekanand, R. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10504-y · Cited by 29 · see citing works

Unraveling Human Brain Development and Evolution Using Organoid Models

Fernandes, S., Klein, D., & Marchetto, M. C. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.737429 · Cited by 26 · see citing works

Unbiased integration of single cell multi-omics dataPreprint

Dou, J., Liang, S., Mohanty, V., Cheng, X., Kim, S., Choi, J., Li, Y., Rezvani, K., Chen, R., & Chen, K. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.11.422014 · Cited by 25 · see citing works

Systems Human Immunology and AI: Immune Setpoint and Immune Health

Lei, Y., & Tsang, J. S. (2025) · Annual Review of Immunology · doi:10.1146/annurev-immunol-090122-042631 · Cited by 25 · see citing works

Application of single-cell multi-omics approaches in horticulture research

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Fractional re-distribution among cell motility states during ageing

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