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

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

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

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

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

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

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

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

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

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 554

Dissecting and improving gene regulatory network inference using single-cell transcriptome dataPreprint

Xue, L., Wu, Y., & Lin, Y. (2023) · Genome Research · doi:10.1101/gr.277488.122 · Cited by 10

Unveiling causal regulatory mechanisms through cell-state parallax

Wu, A. P., Singh, R., Walsh, C. A., & Berger, B. (2025) · Nature Communications · doi:10.1038/s41467-025-61337-5 · Cited by 9

MAGNETO: Cell type marker panel generator from single-cell transcriptomic data

Tangherloni, A., Riva, S. G., Myers, B., Buffa, F. M., & Cazzaniga, P. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104510 · Cited by 8

A Cell Cycle‐Aware Network for Data Integration and Label Transferring of Single‐Cell RNA‐Seq and ATAC‐Seq

Liu, J., Ma, J., Wen, J., & Zhou, X. (2024) · Advanced Science · doi:10.1002/advs.202401815 · Cited by 8

Fatty Acid Metabolites and the Tumor Microenvironment as Potent Regulators of Cancer Stem Cell Signaling

Murai, T., & Matsuda, S. (2023) · Metabolites · doi:10.3390/metabo13060709 · Cited by 9

Joint inference of clonal structure using single-cell genome and transcriptome sequencing data

Bai, X., Duren, Z., Wan, L., & Xia, L. C. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae017 · Cited by 7

Joint Inference of Clonal Structure using Single-cell Genome and Transcriptome Sequencing DataPreprint

Bai, X., Duren, Z., Wan, L., & Xia, L. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.04.934455 · Cited by 7

Recent advances in plant translational genomics for crop improvement

Mathur, S., Singh, D. A., & Ranjan, R. (2024) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2023.11.009 · Cited by 7

How a cell decides its own fate: a single-cell view of molecular mechanisms and dynamics of cell-type specification

Mircea, M., & Semrau, S. (2021) · Biochemical Society Transactions · doi:10.1042/bst20210135 · Cited by 9

PancrESS – a meta-analysis resource for understanding cell-type specific expression in the human pancreas

Sturgill, D. M., Wang, L., & Arda, H. E. (2024) · BMC Genomics · doi:10.1186/s12864-024-09964-y · Cited by 7

Single-Cell Antibody Sequencing in Atherosclerosis Research

Saigusa, R., Durant, C. P., Suryawanshi, V., & Ley, K. F. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1924-7_46 · Cited by 7

Integrated Multimodal Omics and Dietary Approaches for the Management of Neurodegeneration

Murai, T., & Matsuda, S. (2023) · Epigenomes · doi:10.3390/epigenomes7030020 · Cited by 5

Proximogram—A multi-omics network-based framework to capture tissue heterogeneity integrating single-cell omics and spatial profiling

Krishnan, S., Ji, S., Elhossiny, A. M., Rao, A., Frankel, T. L., & Rao, A. U. K. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109082 · Cited by 6

Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data

Zhang, L., Lin, L., & Li, J. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011044 · Cited by 6

Multi-objective Optimization for Marker Panel Identification in Single-cell Data

Tangherloni, A., Riva, S. G., Myers, B., & Cazzaniga, P. (2022) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb55180.2022.9863042 · Cited by 5

scMoresDB: A comprehensive database of single-cell multi-omics data for human respiratory system

Chen, K., Han, Y., Wang, Y., Zhou, D., Wu, F., Cai, W., Zheng, S., Xiao, Q., Zhang, H., & Li, W. (2024) · iScience · doi:10.1016/j.isci.2024.109567 · Cited by 6

Taming Cell-to-Cell Heterogeneity in Acute Myeloid Leukaemia With Machine Learning

Sánchez-Corrales, Y. E., Pohle, R. V. C., Castellano, S., & Giustacchini, A. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.666829 · Cited by 6

Machine Learning and Artificial Intelligence-Based Clinical Decision Support for Modern Hematology

Zhang, C., Lam, B. D., Lucas, F. M., & Foy, B. H. (2025) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2025.07.011 · Cited by 4

OmiTrans: Generative Adversarial Networks Based Omics-to-omics Translation Framework

Zhang, X., & Guo, Y. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995537 · Cited by 5

Evaluation of Computational Methods for Single Cell Multi-Omics Integration

Wani, S. A., & Quadri, S. M. K. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.246 · Cited by 5

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Tisi, A., Palaniappan, S., & Maccarrone, M. (2023) · Biomolecules · doi:10.3390/biom13101534 · Cited by 5

Adaptive multi-view information bottleneck for multi-omics data clustering

Tian, Z., Wei, X., Lou, Z., Teng, Z., & Fu, S. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf717 · Cited by 3

Single-cell technologies for multimodal omics measurements

Bai, D., & Zhu, C. (2023) · Frontiers in Systems Biology · doi:10.3389/fsysb.2023.1155990 · Cited by 3

The role of single-cell sequencing in studying tumour evolution

Mossner, M., Baker, A., & Graham, T. A. (2021) · Faculty Reviews · doi:10.12703/r/10-49 · Cited by 5

BRAQUE: Bayesian Reduction for Amplified Quantization in UMAP Embedding

Dall’Olio, L., Bolognesi, M. M., Borghesi, S., Cattoretti, G., & Castellani, G. (2023) · Entropy · doi:10.3390/e25020354 · Cited by 4

Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional statesPreprint

Bao, F., Deng, Y. C., Wan, S., Wang, B., Dai, Q., Altschuler, S. J., & Wu, L. F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.05.284539 · Cited by 3

Iterative epigenomic analyses in the same single cellPreprint

Ohnuki, H., Venzon, D. J., Lobanov, A. V., & Tosato, G. (2021) · Genome Research · doi:10.1101/gr.269068.120 · Cited by 3

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

Cheng, M., Li, Z., & Costa, I. G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.19.476907 · Cited by 3

ScMOGAE: A Graph Convolutional Autoencoder-Based Multi-omics Data Integration Framework for Single-Cell Clustering

Zhou, B., Jiang, H., Wang, Y., Gu, Y., & Sun, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5128-0_26 · Cited by 3

Attention-Fusion Model for Multi-omics (AMMO) Data Integration in Lung Adenocarcinoma

Li, W., Muneer, A., Waqas, M., Zhou, X., & Wu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73360-4_6 · Cited by 2

The impact of asymmetrical gene expression on the development of spike morphology in Triticum aestivum

He, F., Liu, X., Ma, Q., Wan, W., Li, L., Li, K., Jia, Z., Zhang, S., Xu, R., & Ren, M. (2025) · iMetaOmics. · doi:10.1002/imo2.70047 · Cited by 2

ArchVelo: archetypal velocity modeling for single-cell multi-omic trajectories

Avdeeva, M., Walker, S. K., van der Veeken, J., Rudensky, A. Y., & Pritykin, Y. (2026) · Nature Communications · doi:10.1038/s41467-026-74000-4 · Cited by 3

HyperSTAR: Unveiling Tissue Structure and Tumor Microenvironment from Spatial Omics by Hypergraph Learning

Liao, Y., Zhang, C., Wang, Z., Qi, F., Huang, W., Cai, S., Li, J., Chen, J., Gasser, R. B., Yuan, Z., et al. (2025) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf128 · Cited by 3

Single-cell protein-DNA interactomics and multiomics tools for deciphering genome regulation

Xiong, H., Wang, R., & He, A. (2023) · National Science Open · doi:10.1360/nso/20220057 · Cited by 3

Single-cell multi-omics in the medicinal plant Catharanthus roseus: a new era of next generation therapeutics

Dangwal, M., Suri, G. S., & Kaur, G. (2024) · Journal of Plant Biochemistry and Biotechnology · doi:10.1007/s13562-024-00913-0 · Cited by 2

The combined use of scRNA-seq and network propagation highlights key features of pan-cancer Tumor-Infiltrating T cells

Mangelinck, A., Molitor, E., Marchiq, I., Alaoui, L., Bouaziz, M., Andrade-Pereira, R., Darville, H., Becht, É., & Lefèbvre, C. (2024) · PLoS ONE · doi:10.1371/journal.pone.0315980 · Cited by 2

Advances in single-cell multi-omics methods and their applications in developmental biology

Cheng, X., Rui, Y. A. N., & Guo, F. (2020) · Scientia Sinica Vitae · doi:10.1360/ssv-2020-0244 · Cited by 2

High-resolution sample size enrichment of single-cell multi-modal low-throughput Patch-seq datasets

Martini, L., Bardini, R., Savino, A., & Carlo, S. D. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995529 · Cited by 2

MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology

Olson, R. H., Kalafut, N. C., & Wang, D. (2023) · Patterns · doi:10.1016/j.patter.2023.100847 · Cited by 2

Fate-resolved gene regulatory signatures of individual B lymphocytes in the early stages of Epstein-Barr Virus infectionPreprint

SoRelle, E. D., Dai, J., Reinoso-Vizcaíno, N. M., Barry, A. P., Chan, C. C. W., & Luftig, M. A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.23.481342 · Cited by 2

MatchCLOT: Single-Cell Modality Matching with Contrastive Learning and Optimal TransportPreprint

Gossi, F., Pati, P., Martinelli, A. L., & Rapsomaniki, M. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.16.516751 · Cited by 2

TriTan: An efficient triple non-negative matrix factorisation method for integrative analysis of single-cell multiomics dataPreprint

Ma, X., Lijing Lin, A., Zhao, Q., & Iqbal, M. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.14.549059 · Cited by 2

CellFeature: Cell and Feature Co-Embedding from Single-Cell Multi-Omics with Heterogeneous Graph Model

Li, E., Gao, L., & Ye, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821837 · Cited by 1

Integrating multi-omics data for next-generation cancer research and precision medicine

Biswas, S., Acharya, S., Prajapat, A., Sen, P., Desai, S., Giri, S., Matthiesen, R., CAPCI, T., Chaubey, G., & Suravajhala, P. (2026) · Clinical & Translational Oncology · doi:10.1007/s12094-026-04368-8 · Cited by 1

AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency

Jafari, E., Johnson, T. S., Wang, Y., Liu, Y., Huang, K., & Wang, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac683 · Cited by 1

Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning

Yeh, C., Chen, Z.-G., Liou, C.-Y., & Chen, M. (2023) · Bioengineering · doi:10.3390/bioengineering10090996 · Cited by 1

Single-Cell RNA-Seq Data Clustering: Highlighting Computational Challenges and Considerations

Nossier, M., Moussa, S. M., & Badr, N. L. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385639 · Cited by 1

TriTan: an efficient triple nonnegative matrix factorization method for integrative analysis of single-cell multiomics data

Ma, X., Lijing Lin, A., Zhao, Q., & Iqbal, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae615 · Cited by 1

Formation of ICT competencies of scientific and pedagogical staff under conditions of the COVID-19 Pandemic

Ткач, А., Kurysh, N., Dobrovolskyi, V., Katerusha, О., & Kondratenko, I. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6n2.10802 · Cited by 1

Multipotent Embryonic Lung Progenitors: Foundational Units of In Vitro and In Vivo Lung Organogenesis

Ikonomou, L., Yampolskaya, M., & Mehta, P. (2023) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-26625-6_4 · Cited by 1

Unsupervised cellular phenotypic hierarchy enables spatial intratumor heterogeneity characterization, recurrence-associated microdomains discovery, and harnesses network biology from hyperplexed in-situ fluorescence images of colorectal carcinomaPreprint

Furman, S. A., Stern, A. M., Uttam, S., Taylor, D. L., Pullara, F., & Chennubhotla, S. C. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.02.322529 · Cited by 1

CoTECH for single-cell joint detection of transcriptome and chromatin occupancyPreprint

Luo, Y., Xiong, H., Wang, Q., Yu, X., & He, A. (2021) · Research Square · doi:10.21203/rs.3.pex-1300/v1 · Cited by 1

Massively parallel single-cell profiling of transcriptome and multiple epigenetic proteins in cell fate regulationPreprint

Xiong, H., Wang, Q., Li, C. C., & He, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.04.535478 · Cited by 1

LGALS2 and EGR1: markers of endometriosis for predictive, preventive and personalized medicinePreprint

Jiang, H., Sun, Q., Huang, Z., Chen, H., Chen, L., & He, X. (2023) · Research Square · doi:10.21203/rs.3.rs-2726180/v1 · Cited by 1

MANGEM: A Web App for Multimodal Analysis of Neuronal Gene Expression, Electrophysiology and MorphologyPreprint

Olson, R. H., Kalafut, N. C., & Wang, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4493251 · Cited by 1

Unveiling Tissue Structure and Tumor Microenvironment from Spatial Omics by Hypergraph LearningPreprint

Liao, Y., Zhang, C., Wang, Z., Qi, F., Huang, W., Cai, S., Li, J., Chen, J., Yuan, Z., Song, J., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4875103 · Cited by 1

Spatial Transcriptomics Reveals CXCL12 ⁺ Fibroblasts as Central Immune Organizers through CXCR4 Signaling in Abdominal Aortic AneurysmPreprint

Lévy-Lambert, D., Ramirez, J. L., Shaikh, S., Diaz, C. D. J., Huang, A. G., Combes, A. J., Fragiadakis, G. K., Fidler, T. P., & Oskowitz, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.24.690328 · Cited by 1

Hybrid Clustering of single-cell gene expression and cell spatial information via integrated NMF and k-meansPreprint

Oh, S., Park, H., & Zhang, X. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.15.383281

Stochastic simulation of enzymatic kinetics for 13C isotope labeling at the single-cell scale

Luo, H., Shen, T., & Xie, X. (2022) · Reaction Kinetics Mechanisms and Catalysis · doi:10.1007/s11144-022-02262-2

Guided-topic modelling of single-cell transcriptomes enables sub-cell-type and disease-subtype deconvolution of bulk transcriptomesPreprint

Swapna, L. S., Huang, M., & Li, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.22.521640

InClust+: the multimodal version of inClust for multimodal data integration, imputation, and cross modal generationPreprint

Wang, L., Nie, R., Cai, Y., Wang, A., Zhang, H., Zhang, J., & Cai, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.13.532376

In situ veritas: combining omics and multiplex imaging can facilitate the detection and characterization of cell-cell interactions in tissues

Ritter, U. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1155057

Decentralized Smart Healthcare Systems Using Blockchain and AI

Choudhury, T., Khanna, A., Chatterjee, P., Um, J., & Bhattacharya, A. (2023) · doi:10.1002/9781394229512.ch8

CUT&tag: A powerful epigenetic tool for chromatin profilingPreprint

Li, P., fu, Z., Jiang, S., Sun, Y., zheng, S., & Zong, L. (2023) · doi:10.22541/au.169203623.39699591/v1

Prediction of distinct populations of innate lymphoid cells by transcriptional profiles

Dong, H., Du, Z., Ma, H., Zhou, Z., Yang, H., & Wang, Z. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1227452

The statistical physics of single cell genomics

Lemus, E. H. (2023) · Molecular Physics · doi:10.1080/00268976.2023.2297818

MoSCHG: Multi-omics Single-cell Classification based on Heterogeneous Graphs and Supervised Contrastive Learning

Chen, X., Xie, C., Zhang, Y., He, S., Bo, X., & Zhang, Z. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385631 · Cited by 2

Additional file 1 of Feature selection revisited in the single-cell era

Yang, P., Huang, H., & Liu, C. (2021) · Figshare · doi:10.6084/m9.figshare.17104024

Additional file 3 of 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) · Figshare · doi:10.6084/m9.figshare.19738259

Addressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL

Shi, M., & Li, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae703

Unravelling Causal Genetic Biomarkers of Alzheimer’s Disease via Neuron to Gene-token Backtracking in Neural Architecture: A Groundbreaking Reverse-Gene-Finder Approach

Li, V. O. K., Yang, H., & Lam, J. C. K. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i17.34032 · Cited by 3

Plant Single-Cell Epigenomics: Updated Methods and Current Applications

Suruthi, G. S., Anand, A. V., Rajesh, M., Gowtham, P., Begum, T. N., Haniff, S. M., Sampathkumar, P., & Robert, A. A. (2025) · CABI climate change series. · doi:10.1079/9781800626102.0005

scProca: A Cross-Attention-Enhanced Deep Generative Model for Single-Cell Transcriptomics and Proteomics Integration and Imputation

Xiong, J., Zheng, S., Gong, F., Ma, L., & Wan, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3615771 · Cited by 1

Single Cell Mosaic Integration via Regulatory Graph Guided Variational Cross-modal Fusion

Lv, J., Wang, Z., Zhou, Z., & Zhang, Y. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227268

MISF: Multimodal Data Integration Through Adaptive Similarity Learning and Matrix Factorization

Zhou, F., Chen, X., Jiang, Y., & Guan, J. (2025) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbbio.2025.3645231

Additional file 2 of Feature selection revisited in the single-cell era

Yang, P., Huang, H., & Liu, C. (2021) · Figshare · doi:10.6084/m9.figshare.17104027

Additional file 2 of PancrESS – a meta-analysis resource for understanding cell-type specific expression in the human pancreas

Sturgill, D. M., Wang, L., & Arda, H. E. (2024) · Figshare · doi:10.6084/m9.figshare.25026016

Additional file 4 of 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) · Figshare · doi:10.6084/m9.figshare.19738262

Additional file 2 of InClust+: the deep generative framework with mask modules for multimodal data integration, imputation, and cross-modal generation

Wang, L., Nie, R., Miao, X., Cai, Y., Wang, A., Zhang, H., Zhang, J., & Cai, J. (2024) · Figshare · doi:10.6084/m9.figshare.25059609

Additional file 4 of PancrESS – a meta-analysis resource for understanding cell-type specific expression in the human pancreas

Sturgill, D. M., Wang, L., & Arda, H. E. (2024) · Figshare · doi:10.6084/m9.figshare.26669244

Additional file 2 of MUON: multimodal omics analysis framework

Bredikhin, D., Kats, I., & Stegle, O. (2022) · Figshare · doi:10.6084/m9.figshare.19106191

Additional file 2 of CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation

Hu, Z., Ahmed, A. A., & Yau, C. (2021) · Figshare · doi:10.6084/m9.figshare.17194050

Additional file 1 of MUON: multimodal omics analysis framework

Bredikhin, D., Kats, I., & Stegle, O. (2022) · Figshare · doi:10.6084/m9.figshare.19106188

Additional file 1 of CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation

Hu, Z., Ahmed, A. A., & Yau, C. (2021) · Figshare · doi:10.6084/m9.figshare.17194047

Additional file 2 of MultiMAP: dimensionality reduction and integration of multimodal data

Jain, M. S., Polański, K., Conde, C. D., Chen, X., Park, J., Mamanova, L., Knights, A. J., Botting, R. A., Stephenson, E., Haniffa, M., et al. (2021) · Figshare · doi:10.6084/m9.figshare.17304971

Additional file 1 of 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) · Figshare · doi:10.6084/m9.figshare.19738253

Additional file 1 of MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Yu, H., & Welch, J. D. (2021) · Figshare · doi:10.6084/m9.figshare.14634020

Additional file 3 of MultiMAP: dimensionality reduction and integration of multimodal data

Jain, M. S., Polański, K., Conde, C. D., Chen, X., Park, J., Mamanova, L., Knights, A. J., Botting, R. A., Stephenson, E., Haniffa, M., et al. (2021) · Figshare · doi:10.6084/m9.figshare.17304977

Additional file 2 of MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks

Yu, H., & Welch, J. D. (2021) · Figshare · doi:10.6084/m9.figshare.14634023

Additional file 1 of MultiMAP: dimensionality reduction and integration of multimodal data

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