MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
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Abstract
Technological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.
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MOFA+ requires size factor normalization to prevent the first factors from being dominated by differences in total expression per sample.
“This approach requires size factor normalization to ensure that the first factors are not dominated by differences in total expression per sample.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [248]
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Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219 · see citing works
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
Comprehensive integration of single-cell dataPreprint
Stuart, T., Butler, A. W., Hoffman, P. J., Hafemeister, C., Papalexi, E., Mauck, W., Hao, Y.-H., Stoeckius, M., Smibert, P., & Satija, R. (2018) · bioRxiv · doi:10.1101/460147 · Cited by 13,858 · see citing works
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063 · see citing works
Human DNA methylomes at base resolution show widespread epigenomic differences
Lister, R., Pelizzola, M., Dowen, R. H., Hawkins, R. D., Hon, G., Tonti-Filippini, J., Nery, J. R., Lee, L., Ye, Z., Ngo, Q.-M., et al. (2009) · Nature · doi:10.1038/nature08514 · Cited by 4,673 · see citing works
Histone H3K27ac separates active from poised enhancers and predicts developmental state
Creyghton, M. P., Cheng, A. W., Welstead, G. G., Kooistra, T., Carey, B. W., Steine, E. J., Hanna, J., Lodato, M. A., Frampton, G. M., Sharp, P. A., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016071107 · Cited by 4,302 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1285773 · Cited by 3,974 · 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
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530 · see citing works
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416 · see citing works
Self-citationSingle-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · see citing works
Du, P., Zhang, X., Huang, C.-C., Jafari, N., Kibbe, W. A., Hou, L., & Lin, S. M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-587 · Cited by 2,136 · see citing works
Variational Inference: A Review for Statisticians
Blei, D. M., Kucukelbir, A., & McAuliffe, J. (2023) · Figshare · doi:10.6084/m9.figshare.5203696.v2 · Cited by 2,088 · see citing works
Single-cell RNA sequencing to explore immune cell heterogeneity
Papalexi, E., & Satija, R. (2018) · Nature Reviews Immunology · doi:10.1038/nri.2017.76 · Cited by 1,806 · see citing works
Stochastic variational inference
Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. W. (2013) · Journal of Machine Learning Research · doi:10.5555/2567709.2502622 · Cited by 1,802 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.2 · Cited by 1,586 · see citing works
Self-citationA step-by-step workflow for low-level analysis of single-cell RNA-seq data
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.1 · Cited by 1,511 · see citing works
Self-citationMulti-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets
Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510 · see citing works
Self-citationIntegrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
Modification of Enhancer Chromatin: What, How, and Why?
Calo, E., & Wysocka, J. (2013) · Molecular Cell · doi:10.1016/j.molcel.2013.01.038 · Cited by 1,457 · see citing works
GARCÍA-ALONSO, C. R., Pérez, L. M., & Fernández, J. C. F. (2011) · Annals of Operations Research · doi:10.1007/s10479-011-0841-3 · Cited by 1,357 · see citing works
Yates, A. D., Akanni, W. A., Amode, M. R., Barrell, D. G., Billis, K., Carvalho‐Silva, D., Cummins, C. A., Clapham, P., Fitzgerald, S. F., Gil, L., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1157 · Cited by 1,246 · see citing works
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 1,201 · 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
Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity
Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G. (2014) · Nature Methods · doi:10.1038/nmeth.3035 · Cited by 1,125 · see citing works
Self-citationA single-cell molecular map of mouse gastrulation and early organogenesis
Pijuan-Sala, B., Griffiths, J. A., Guibentif, C., Hiscock, T. W., Jawaid, W., Calero-Nieto, F. J., Mulas, C., Ibarra-Soria, X., Tyser, R. C. V., Ho, D. L. L., et al. (2019) · Nature · doi:10.1038/s41586-019-0933-9 · Cited by 1,033 · see citing works
Self-citationRamsahoye, B., Biniszkiewicz, D., Lyko, F., Clark, V. H., Bird, A., & Jaenisch, R. (2000) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.97.10.5237 · Cited by 1,018 · see citing works
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938 · see citing works
High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
Chen, S., Lake, B. B., & Zhang, K. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0290-0 · Cited by 810 · see citing works
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750 · see citing works
Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity
Angermueller, C., Clark, S. J., Lee, H. J., Macaulay, I. C., Teng, M. J., Hu, T. X., Krueger, F., Smallwood, S. A., Ponting, C. P., Voet, T., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3728 · Cited by 717 · see citing works
Self-citationAdvances in Variational Inference
Zhang, C., Bütepage, J., Kjellström, H., & Mandt, S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2889774 · Cited by 676 · see citing works
scGen predicts single-cell perturbation responses
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0494-8 · Cited by 661 · see citing works
Clark, S. J., Argelaguet, R., Kapourani, C.-A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03149-4 · Cited by 657 · see citing works
Self-citationSingle-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex
Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · Cited by 545 · see citing works
Guo, H., Zhu, P., Wu, X., Li, X., Wen, L., & Tang, F. (2013) · Genome Research · doi:10.1101/gr.161679.113 · Cited by 518 · see citing works
Integrated genome and transcriptome sequencing of the same cell
Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3129 · Cited by 502 · see citing works
Single-cell multiomics: multiple measurements from single cells
Macaulay, I. C., Ponting, C. P., & Voet, T. (2017) · Trends in Genetics · doi:10.1016/j.tig.2016.12.003 · Cited by 451 · see citing works
Multi-omics profiling of mouse gastrulation at single-cell resolution
Argelaguet, R., Clark, S. J., Mohammed, H., Stapel, L. C., Krueger, C., Kapourani, C.-A., Imaz-Rosshandler, I., Lohoff, T., Xiang, Y., Hanna, C. W., et al. (2019) · Nature · doi:10.1038/s41586-019-1825-8 · Cited by 450 · see citing works
Self-citationComputational assignment of cell-cycle stage from single-cell transcriptome data
Scialdone, A., Natarajan, K. N., Saraiva, L. R., Proserpio, V., Teichmann, S. A., Stegle, O., Marioni, J. C., & Buettner, F. (2015) · Methods · doi:10.1016/j.ymeth.2015.06.021 · Cited by 386 · see citing works
Self-citationSingle-cell multi-omics sequencing of mouse early embryos and embryonic stem cells
Guo, F., Li, L., Li, J., Wu, X., Hu, B., Zhu, P., Wen, L., & Tang, F. (2017) · Cell Research · doi:10.1038/cr.2017.82 · Cited by 374 · see citing works
The cis-regulatory dynamics of embryonic development at single-cell resolution
Cusanovich, D. A., Reddington, J. P., Garfield, D. A., Daza, R. M., Aghamirzaie, D., Marco-Ferreres, R., Pliner, H. A., Christiansen, L., Qiu, X., Steemers, F. J., et al. (2018) · Nature · doi:10.1038/nature25981 · Cited by 363 · see citing works
Mean Field Theory for Sigmoid Belief Networks
Saul, L. K., Jaakkola, T., & Jordan, M. I. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.251 · Cited by 331 · see citing works
Single-cell multiomics sequencing and analyses of human colorectal cancer
Bian, S., Hou, Y., Zhou, X., Li, X.-L., Yong, J., Wang, Y., Wang, W.-D., Yan, J., Hu, B.-Q., Guo, H.-S., et al. (2018) · Science · doi:10.1126/science.aao3791 · Cited by 326 · see citing works
Chen, L., Chen, K., Lavery, L. A., Baker, S. A., Shaw, C. A., Li, W., & Zoghbi, H. Y. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1505909112 · Cited by 301 · see citing works
Highly scalable generation of DNA methylation profiles in single cells
Mulqueen, R. M., Pokholok, D., Norberg, S. J., Torkenczy, K. A., Fields, A. J., Sun, D., Sinnamon, J. R., Shendure, J., Trapnell, C., O'Roak, B. J., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4112 · Cited by 289 · see citing works
Clark, S. J., Smallwood, S. A., Lee, H. J., Krueger, F., Reik, W., & Kelsey, G. (2017) · Nature Protocols · doi:10.1038/nprot.2016.187 · Cited by 270 · see citing works
scVAE: variational auto-encoders for single-cell gene expression data
Grønbech, C. H., Vording, M. F., Timshel, P. N., Sønderby, C. K., Pers, T. H., & Winther, O. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa293 · Cited by 268 · see citing works
Non-CG Methylation in the Human Genome
He, Y., & Ecker, J. R. (2015) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-090413-025437 · Cited by 263 · see citing works
Using single-cell genomics to understand developmental processes and cell fate decisions
Griffiths, J. A., Scialdone, A., & Marioni, J. C. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178046 · Cited by 250 · see citing works
Self-citationPott, S. (2017) · eLife · doi:10.7554/elife.23203 · Cited by 230 · see citing works
Single-cell multi-omics sequencing of human early embryos
Li, L., Guo, F., Gao, Y., Ren, Y., Yuan, P., Yan, L., Li, R., Lian, Y., Li, J., Hu, B., et al. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0123-2 · Cited by 209 · see citing works
Computational methods for Gene Regulatory Networks reconstruction and analysis: A review
Delgado-Chaves, F. M., & Gómez-Vela, F. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.10.006 · Cited by 209 · see citing works
Single-Cell (Multi)omics Technologies
Chappell, L., Russell, A. J. C., & Voet, T. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-091416-035324 · Cited by 206 · see citing works
Single-cell multimodal profiling reveals cellular epigenetic heterogeneity
Cheow, L. F., Courtois, E. T., Tan, Y., Viswanathan, R., Xing, Q. R., Tan, R. Z., Tan, D. S.-W., Robson, P., Loh, Y., Quake, S. R., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3961 · Cited by 193 · see citing works
Dynamic epigenomic landscapes during early lineage specification in mouse embryos
Zhang, Y., Xiang, Y.-L., Yin, Q.-Z., Du, Z., Peng, X., Wang, Q.-J., Fidalgo, M., Xia, W., Li, Y.-Y., Zhao, Z.-A., et al. (2017) · Nature Genetics · doi:10.1038/s41588-017-0003-x · Cited by 192 · see citing works
Chromatin accessibility dynamics reveal novel functional enhancers in C. elegansPreprint
Daugherty, A. C., Yeo, R. W., Buenrostro, J. D., Greenleaf, W. J., Kundaje, A. B., & Brunet, A. (2017) · Genome Research · doi:10.1101/gr.226233.117 · Cited by 173 · see citing works
Missing values in principal component analysis
Grung, B., & Manne, R. (1998) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(98)00031-8 · Cited by 160 · see citing works
High-throughput chromatin accessibility profiling at single-cell resolution
Mezger, A., Klemm, S. L., Mann, I. K., Brower, K. K., Mir, A. A., Bostick, M. L., Farmer, A. A., Fordyce, P. M., Linnarsson, S., & Greenleaf, W. J. (2018) · Nature Communications · doi:10.1038/s41467-018-05887-x · Cited by 149 · see citing works
Multi-omics of single cells: strategies and applications
Bock, C., Farlik, M., & Sheffield, N. C. (2016) · Trends in Biotechnology · doi:10.1016/j.tibtech.2016.04.004 · Cited by 146 · see citing works
Developmental enhancers revealed by extensive DNA methylome maps of zebrafish early embryos
Lee, H. J., Lowdon, R. F., Maricque, B. B., Zhang, B., Stevens, M. W., Li, D., Johnson, S. L., & Wang, T. (2015) · Nature Communications · doi:10.1038/ncomms7315 · Cited by 88 · see citing works
Application of single-cell genomics in cancer: promise and challenges
Wills, Q. F., & Mead, A. J. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv235 · Cited by 75 · see citing works
scVAE: Variational auto-encoders for single-cell gene expression dataPreprint
Grønbech, C. H., Vording, M. F., Timshel, P. N., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/318295 · Cited by 57 · see citing works
Zhang, L., & Zhang, S. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz488 · Cited by 46 · see citing works
Wiring together large single-cell RNA-seq sample collectionsPreprint
Barkas, N., Petukhov, V., Nikolaeva, D., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/460246 · Cited by 16 · see citing works
Argelaguet, R., Mohammed, H., Clark, S. J., Stapel, L. C., Krueger, C., Kapourani, C.-A., Xiang, Y., Hanna, C. W., Smallwood, S. A., Ibarra-Soria, X., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/519207 · Cited by 11 · see citing works
Self-citationArgelaguet, R., D., DamienArnol, et al. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3735161 · Cited by 5 · see citing works
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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
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
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
“The actual generated products, the proteins, take on either intracellular or extracellular tasks, and a subset of proteins are presented on the cell surface.”
BackgroundThe technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
The Immunology of Multisystem Inflammatory Syndrome in Children with COVID-19
Consiglio, C. R., Cotugno, N., Sardh, F., Pou, C., Amodio, D., Rodriguez, L., Tan, Z., Zicari, S., Ruggiero, A., Pascucci, G. R., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.016 · Cited by 919 · see citing works
A Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789 · see citing works
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works
Self-citationMultiVI: deep generative model for the integration of multi-modal data
Ashuach, T., Gabitto, M. I., Jordan, M. I., & Yosef, N. (2021) · doi:10.1101/2021.08.20.457057 · Cited by 385 · see citing works
State of the field in multi-omics research: from computational needs to data mining and sharing
Krassowski, M., Das, V., Sahu, S. K., & Misra, B. B. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.610798 · Cited by 351 · see citing works
Hotspot identifies informative gene modules across modalities of single-cell genomics
DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267 · see citing works
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Dimitrov, D., Schäfer, P. S. L., Farr, E., Rodriguez-Mier, P., Lobentanzer, S., Badia-i-Mompel, P., Dugourd, A., Tanevski, J., Ramirez Flores, R. O., & Saez-Rodriguez, J. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01469-w · Cited by 263 · see citing works
Papalexi, E., Mimitou, E. P., Butler, A. W., Foster, S., Bracken, B., Mauck, W. M., Wessels, H.-H., Hao, Y., Yeung, B. Z., Smibert, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00778-2 · Cited by 238 · see citing works
“However, integrative multi-modal approaches [33] could enable joint analysis of the transcriptome and protein levels when filtering non-perturbed cells and represent a promising future extension of our method.”
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
Self-citationSEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
Persad, S., Choo, Z.-N., Dien, C., Sohail, N., Masilionis, I., Chaligné, R., Nawy, T., Brown, C. C., Sharma, R., Pe’er, I., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01716-9 · Cited by 222 · see citing works
Computational methods for single-cell omics across modalities
Efremova, M., & Teichmann, S. A. (2020) · Nature Methods · doi:10.1038/s41592-019-0692-4 · Cited by 201 · see citing works
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181 · see citing works
“, 2018) and its more recent improvement, MOFA+ (Argelaguet et al., 2020) are unsupervised integrative approaches that may be understood as generalizations of principal components analysis (PCA) to multi-omics data.”
BackgroundCobolt: integrative analysis of multimodal single-cell sequencing data
Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168 · see citing works
Multiomic spatial landscape of innate immune cells at human central nervous system borders
Sankowski, R., Süß, P., Benkendorff, A., Böttcher, C., Fernandez-Zapata, C., Chhatbar, C., Cahueau, J., Monaco, G., Gasull, A. D., Khavaran, A., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02673-1 · Cited by 156 · see citing works
A Review of Integrative Imputation for Multi-Omics Datasets
Song, M., Greenbaum, J., Luttrell, J., Zhou, W., Wu, C., Shen, H., Gong, P., Zhang, C., & Deng, H.-W. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.570255 · Cited by 127 · see citing works
Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS
He, Z., Hu, S., Chen, Y., An, S., Zhou, J., Liu, R., Shi, J., Wang, J., Dong, G., Shi, J., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-02040-y · Cited by 84 · see citing works
Into the multiverse: advances in single-cell multiomic profiling
Ogbeide, S., Giannese, F., Mincarelli, L., & Macaulay, I. C. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.03.015 · Cited by 82 · see citing works
Computational methods for the integrative analysis of single-cell data
Forcato, M., Romano, O., & Bicciato, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa042 · Cited by 71 · see citing works
Cell type mapping reveals tissue niches and interactions in subcortical multiple sclerosis lesions
Lerma-Martin, C., Badia-i-Mompel, P., Ramirez Flores, R. O., Sekol, P., Schäfer, P. S. L., Riedl, C. J., Hofmann, A., Thäwel, T., Wünnemann, F., Ibarra-Arellano, M. A., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01796-z · Cited by 76 · see citing works
“For this, we designed an unsupervised approach using factor analysis 26 , combining gene expression and deconvoluted cell type proportions to define niches (Fig.”
InfluentialComputational methods for single-cell multi-omics integration and alignment
Stanojevic, S., Li, Y., Ristivojevic, A., & Garmire, L. X. (2022) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.013 · Cited by 74 · see citing works
The human microbiome in clinical translation: from bench to bedside
Bautista, J., Echeverría, C. E., Maldonado-Noboa, I., Ojeda-Mosquera, S., Hidalgo-Tinoco, C., & López-Cortés, A. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1632435 · Cited by 55 · see citing works
Multi-omics analysis reveals the key factors involved in the severity of the Alzheimer’s disease
Meng, L., Jin, H., Yulug, B., Altay, O., Li, X., Hanoglu, L., Cankaya, S., Coskun, E., Idil, E., Nogaylar, R., et al. (2024) · Alzheimer's Research & Therapy · doi:10.1186/s13195-024-01578-6 · Cited by 49 · see citing works
Zhou, Y., Luo, K., Liang, L., Chen, M., & He, X. (2023) · Nature Methods · doi:10.1038/s41592-023-02017-4 · Cited by 34 · see citing works
Computational estimation of quality and clinical relevance of cancer cell lines
Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J., & Iorio, F. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211017 · Cited by 31 · see citing works
CMOT: cross-modality optimal transport for multimodal inference
Alatkar, S. A., & Wang, D. (2023) · Genome biology · doi:10.1186/s13059-023-02989-8 · Cited by 24 · see citing works
MONET: Multi-omic module discovery by omic selection
Rappoport, N., Safra, R., & Shamir, R. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008182 · Cited by 26 · see citing works
“We also included MOFA+ [8], a widely used multi-PLOS omic dimension reduction method.”
MethodStabMap: Mosaic single cell data integration using non-overlapping features
Ghazanfar, S., Guibentif, C., & Marioni, J. C. (2022) · doi:10.1101/2022.02.24.481823 · Cited by 16 · see citing works
Self-citationAn immune world model for multiscale forecasting and therapeutic hypothesis generation
Cui, T., Wang, X., Li, Z., Ding, J., You, L., Xu, Y., Xu, W., Wu, F., Ying, K., Ouyang, W., et al. (2026) · arXiv · doi:10.48550/arxiv.2609.14709
- Authors
- Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O.
- Affiliations
- European Bioinformatics Institute (GB)
- European Molecular Biology Laboratory (DE)
- German Cancer Research Center (DE)
- Corresponding author
- Argelaguet, Ricard
- Marioni, John C.
- Stegle, Oliver
- Venue
- Genome Biology
- Published
- 2020-05-11
- Volume
- 21
- Issue
- 1
- Pages
- 111-111
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC7212577
- OpenAlex
- W3027051073
- Cited by
- 1,091
- References
- 72
- Citations by year
- 2026: 355 · 2025: 254 · 2024: 154 · 2023: 136 · 2022: 96 · 2021: 70 · 2020: 23 · 2019: 1 · 2012: 1
- FWCI
- 26.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Apollo
- Funders
- Deutsches Krebsforschungszentrum
- Cancer Research UK (22231, DRCPGM\100015)
- University of Cambridge
- Bundesministerium für Bildung und Forschung
- Ministry of Foreign Affairs
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- MOFA, multimodal data integration, single-cell multi-omics, variational inference, sparsity constraints, low-dimensional representation, single-cell data
- MeSH terms
- Animals, Factor Analysis, Statistical, Frontal Lobe, Sequence Analysis, RNA, DNA Methylation, Embryonic Development, Mice, Single-Cell Analysis
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