Dictionary learning for integrative, multimodal and scalable single-cell analysis

Hao, Yuhan; Stuart, Tim; Kowalski, Madeline H.; Choudhary, Saket; Hoffman, Paul; Hartman, Austin; Srivastava, Avi; Molla, Gesmira; Madad, Shaista; Fernandez-Granda, Carlos; Satija, Rahul

doi:10.1038/s41587-023-01767-y

Cited by 5,631Single-cell and spatial transcriptomicsBioinformatics and Genomic NetworksGenomics and Chromatin Dynamics

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Open access

Hao, Yuhan, Stuart, Tim, Kowalski, Madeline H., Choudhary, Saket, Hoffman, Paul, Hartman, Austin, Srivastava, Avi, Molla, Gesmira +3 more·2024·Nature Biotechnology

DOI 10.1038/s41587-023-01767-yPMC PMC10928517

Published version of a preprint

Open this paper in CoChat — free account Open PDF

Abstract

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1038/s41587-023-01767-y

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

SCTransform accounts for differences in library size across cell types.
“Datasets were then normalized and scaled using Seurat 5.0.1 SCTransform to account for differences in library size across cell types [31].”
From APOE Christchurch enhances a disease-associated microglial response to plaque but suppresses response to tau pathology (2025) · cites this paper as [31]
The Results sections on reference mapping and community-scale integration discuss aligning and annotating cell populations, not SCTransform’s normalization behavior. Searches for SCTransform, library size, and sequencing-depth normalization found no passage in our copy addressing whether SCTransform accounts for library-size differences across cell types. (LLM, conf=0.70)
Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849 · 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
Self-citation
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321 · cited 1× · see citing works
“Second, the key advance we present here is a transformation to project datasets profiling different modalities to be represented by a shared set of atoms.”
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · see citing works
Self-citation
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 3,326 · 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
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors
Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413 · see citing works
Self-citation
Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum
Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E.-A. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., et al. (2011) · Science · doi:10.1126/science.1198704 · Cited by 2,279 · see citing works
The human cell atlas
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citation
Deep profiling of mouse splenic architecture with CODEX multiplexed imaging
Goltsev, Y., Samusik, N., Kennedy-Darling, J., Bhate, S., Hale, M., Vazquez, G., Black, S., & Nolan, G. P. (2018) · Cell · doi:10.1016/j.cell.2018.07.010 · Cited by 1,501 · cited 1× · see citing works
“For example, performing bridge integration on spatially resolved unimodal datasets (for example, CODEX 74 ) could help to better characterize the spatial localization of scRNA-seq-defined cell types in large tissue sections.”
Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis
Reyfman, P. A., Walter, J. M., Joshi, N., Anekalla, K. R., McQuattie-Pimentel, A. C., Chiu, S., Fernandez, R., Akbarpour, M., Chen, C.-I., Ren, Z., et al. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201712-2410oc · Cited by 1,203 · see citing works
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
Construction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087 · see citing works
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691 · cited 1× · see citing works
“This strategy is enabled by the curation and public release of reference datasets as well as the development of new computational tools, including statistical learning 4–7 and deep learning-based approaches 8,9 , that have been successfully applied toward this goal.”
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Single-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
Single-cell CUT&Tag profiles histone modifications and transcription factors in complex tissues
Bartosovic, M., Kabbe, M., & Castelo-Branco, G. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00869-9 · Cited by 483 · see citing works
Joint profiling of histone modifications and transcriptome in single cells from mouse brain
Zhu, C., Zhang, Y., Li, Y. E., Lucero, J., Behrens, M. M., & Ren, B. (2021) · Nature Methods · doi:10.1038/s41592-021-01060-3 · Cited by 301 · see citing works
Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq)
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
SEACells 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
NEAT-seq: simultaneous profiling of intra-nuclear proteins, chromatin accessibility and gene expression in single cells
Chen, A. F., Parks, B., Kathiria, A. S., Ober-Reynolds, B., Goronzy, J. J., & Greenleaf, W. J. (2022) · Nature Methods · doi:10.1038/s41592-022-01461-y · Cited by 139 · see citing works

Flagged first, then by citations

Research works printed but not linked: 56

[1]Kent WJ BLAT—the BLAST-like alignment tool. Genome research 12, 656– 664 (2002). 11932250 PMC187518
[5]Kang JB Efficient and precise single-cell reference atlas mapping with Symphony. Nat Commun 12, 5890 (2021). 34620862 PMC8497570
[6]Kiselev VY, Yiu A & Hemberg M scmap: projection of single-cell RNA-seq data across data sets. Nat Methods 15, 359– 362 (2018). 29608555
[7]Domínguez Conde C Cross-tissue immune cell analysis reveals tissue-specific adaptations and clonal architecture in humans. bioRxiv, 2021.2004.2028.441762 (2021).
[11]Hu BC The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. Nature 574, 187– 192 (2019). 31597973 PMC6800388
[13]Buenrostro JD Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486– 490 (2015). 26083756 PMC4685948
[14]Cusanovich DA Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910– 914 (2015). 25953818 PMC4836442
[16]Wu SJ Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression. Nat Biotechnol 39, 819– 824 (2021). 33846646 PMC8277750
[19]Stuart T. Comprehensive integration of single-cell data. Cell 177, 1888– 1902. e1821 (2019). 31178118 PMC6687398
[20]Barkas N. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat Methods 16, 695– 698 (2019). 31308548 PMC6684315
[21]Welch JD Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity. Cell 177, 1873– 1887 e1817 (2019). 31178122 PMC6716797
[22]Lara-Astiaso D. Immunogenetics. Chromatin state dynamics during blood formation. Science 345, 943– 949 (2014). 25103404 PMC4412442
[23]Chen S, Lake BB & Zhang K High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat Biotechnol 37, 1452– 1457 (2019). 31611697 PMC6893138
[25]Zhu C. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat Struct Mol Biol 26, 1063– 1070 (2019). 31695190 PMC7231560
[27]Xiong H, Luo Y, Wang Q, Yu X & He A Single-cell joint detection of chromatin occupancy and transcriptome enables higher-dimensional epigenomic reconstructions. Nat Methods 18, 652– 660 (2021). 33958790
[28]Luo C. Single nucleus multi-omics links human cortical cell regulatory genome diversity to disease risk variants. bioRxiv, 2019.2012.2011.873398 (2019).
[29]Clark SJ scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat Commun 9, 781 (2018). 29472610 PMC5823944
[30]Stoeckius M. Simultaneous epitope and transcriptome measurement in single cells. Nature methods 14, 865– 868 (2017). 28759029 PMC5669064
[31]Chung H. Joint single-cell measurements of nuclear proteins and RNA in vivo. Nat Methods 18, 1204– 1212 (2021). 34608310 PMC8532076
[34]Aharon M, Elad M & Bruckstein A K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on signal processing 54, 4311– 4322 (2006).
[35]Rams M & Conrad TOF Dictionary learning allows model-free pseudotime estimation of transcriptomic data. BMC Genomics 23, 56 (2022). 35033004 PMC8760643
[36]Ramirez I, Sprechmann P & Sapiro G in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 3501– 3508 (IEEE, 2010).
[37]Zhang Q & Li B in 2010 IEEE computer society conference on computer vision and pattern recognition 2691– 2698 (IEEE, 2010). PMC3461202 23028208
[38]Korsunsky I. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods 16, 1289– 1296 (2019). 31740819 PMC6884693
[40]Hie B, Bryson B & Berger B Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol 37, 685– 691 (2019). 31061482 PMC6551256
[43]Granja JM Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat Biotechnol 37, 1458– 1465 (2019). 31792411 PMC7258684
[44]Luecken MD in Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021).
[46]See P. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356 (2017). PMC7611082 28473638
[47]Paul F. Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors. Cell 163, 1663– 1677 (2015). 26627738
[48]Zheng S, Papalexi E, Butler A, Stephenson W & Satija R Molecular transitions in early progenitors during human cord blood hematopoiesis. Mol Syst Biol 14, e8041 (2018). 29545397 PMC5852373
[49]Ashuach T, Gabitto MI, Jordan MI & Yosef N MultiVI: deep generative model for the integration of multi-modal data. bioRxiv, 2021.2008.2020.457057 (2021). PMC10406609 37386189
[50]Gong B, Zhou Y & Purdom E Cobolt: integrative analysis of multimodal single-cell sequencing data. Genome Biol 22, 351 (2021). 34963480 PMC8715620
[51]Bishop CM & Nasrabadi NM Pattern recognition and machine learning, Vol. 4. (Springer, 2006).
[53]Bakken TE Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598, 111– 119 (2021). 34616062 PMC8494640
[54]Hie B, Cho H, DeMeo B, Bryson B & Berger B Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape. Cell Syst 8, 483– 493 e487 (2019). 31176620 PMC6597305
[55]DeMeo B & Berger B Hopper: a mathematically optimal algorithm for sketching biological data. Bioinformatics 36, i236– i241 (2020). 32657375 PMC7355272
[56]Hicks SC, Liu R, Ni Y, Purdom E & Risso D mbkmeans: Fast clustering for single cell data using mini-batch k-means. PLoS computational biology 17, e1008625 (2021). 33497379 PMC7864438
[57]Clarkson KL & Woodruff DP Low-rank approximation and regression in input sparsity time. Journal of the ACM (JACM) 63, 1– 45 (2017).
[58]Schiller HB The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease. Am J Respir Cell Mol Biol 61, 31– 41 (2019). 30995076 PMC6604220
[59]Svensson V, da Veiga Beltrame E & Pachter L A curated database reveals trends in single-cell transcriptomics. Database (Oxford) 2020 (2020). PMC7698659 33247933
[61]Plasschaert LW A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature 560, 377– 381 (2018). 30069046 PMC6108322
[62]Tian Y. Single-cell immunology of SARS-CoV-2 infection. Nat Biotechnol 40, 30– 41 (2022). 34931002 PMC9414121
[63]Lee JS & Shin EC The type I interferon response in COVID-19: implications for treatment. Nat Rev Immunol 20, 585– 586 (2020). 32788708 PMC8824445
[64]Wilk AJ A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat Med 26, 1070– 1076 (2020). 32514174 PMC7382903
[65]Ahern DJ A blood atlas of COVID-19 defines hallmarks of disease severity and specificity. MedRxiv (2021). PMC8776501 35216673
[66]Rudensky AY Regulatory T cells and Foxp3. Immunol Rev 241, 260– 268 (2011). 21488902 PMC3077798
[67]Thimme R. Increased expression of the NK cell receptor KLRG1 by virus-specific CD8 T cells during persistent antigen stimulation. J Virol 79, 12112– 12116 (2005). 16140789 PMC1212638
[68]Kurioka A. MAIT cells are licensed through granzyme exchange to kill bacterially sensitized targets. Mucosal immunology 8, 429– 440 (2015). 25269706 PMC4288950
[69]Bjorklund AK The heterogeneity of human CD127(+) innate lymphoid cells revealed by single-cell RNA sequencing. Nat Immunol 17, 451– 460 (2016). 26878113
[70]Consortium, T.T.S. & Quake SR The Tabula Sapiens: a multiple organ single cell transcriptomic atlas of humans. bioRxiv, 2021.2007.2019.452956 (2021). PMC9812260 35549404
[72]Li H. Fly Cell Atlas: a single-cell transcriptomic atlas of the adult fruit fly. bioRxiv, 2021.2007.2004.451050 (2021).
[73]Plant Cell Atlas, C. Vision, challenges and opportunities for a Plant Cell Atlas. Elife 10 (2021). PMC8423441 34491200
[74]Lareau CA Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat Biotechnol 37, 916– 924 (2019). 31235917 PMC10299900
[75]Datlinger P. Ultra-high-throughput single-cell RNA sequencing and perturbation screening with combinatorial fluidic indexing. Nat Methods 18, 635– 642 (2021). 34059827 PMC7612019
[77]Li Z. Single-cell lipidomics with high structural specificity by mass spectrometry. Nat Commun 12, 2869 (2021). 34001877 PMC8129106
[78]Capolupo L. Sphingolipid Control of Fibroblast Heterogeneity Revealed by Single-Cell Lipidomics. bioRxiv, 2021.2002.2023.432420 (2021).

Sorted by citations

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
Self-citation
High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer
Oliveira, M. F. D., Romero, J. P., Chung, M., Williams, S. R., Gottscho, A. D., Gupta, A., Pilipauskas, S. E., Mohabbat, S., Raman, N., Sukovich, D. J., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02193-3 · Cited by 321 · see citing works
Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging
Bandyopadhyay, S., Duffy, M. P., Ahn, K. J., Sussman, J. H., Pang, M., Smith, D., Duncan, G., Zhang, I., Huang, J., Lin, Y., et al. (2024) · Cell · doi:10.1016/j.cell.2024.04.013 · Cited by 300 · see citing works
Transplantation of chemically induced pluripotent stem-cell-derived islets under abdominal anterior rectus sheath in a type 1 diabetes patient.
Wang, S., Du, Y., Zhang, B., Meng, G., Liu, Z., Liew, S. Y., Liang, R., Zhang, Z., Cai, X., Wu, S., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.004 · Cited by 281 · see citing works
Infiltrating CD8+ T cells exacerbate Alzheimer’s disease pathology in a 3D human neuroimmune axis model
Jorfi, M., Park, J., Hall, C. K., Lin, C.-C. J., Chen, M., von Maydell, D., Kruskop, J. M., Kang, B., Choi, Y., Prokopenko, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01415-3 · Cited by 214 · see citing works
CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data
Program, C. S.-C. B., Abdulla, S., Aevermann, B., Assis, P., Badajoz, S., Bell, S. M., Bezzi, E., Cakir, B., Chaffer, J., Chambers, S., et al. (2023) · doi:10.1101/2023.10.30.563174 · Cited by 142 · see citing works
The Human Microglia Atlas (HuMicA) unravels changes in disease-associated microglia subsets across neurodegenerative conditions
Martins-Ferreira, R., Calafell-Segura, J., Leal, B., Rodríguez-Ubreva, J., Martínez-Saez, E., Mereu, E., Pinho E Costa, P., Laguna, A., & Ballestar, E. (2025) · Nature Communications · doi:10.1038/s41467-025-56124-1 · Cited by 123 · see citing works
A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118 · see citing works
“Loki accurately predicted highly variable gene expression, as demonstrated by the spatial distribution of the predicted gene expression (Extended Data Fig.”
Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling
Oliveira, M. F., Romero, J. P., Chung, M., Williams, S., Gottscho, A. D., Gupta, A., Pilipauskas, S. E., Mohabbat, S., Raman, N., Sukovich, D., et al. (2024) · doi:10.1101/2024.06.04.597233 · Cited by 95 · see citing works
Spatially restricted and ontogenically distinct hepatic macrophages are required for tissue repair
De Ponti, F. F., Bujko, A., Liu, Z., Collins, P. J., Schuermans, S., Maueroder, C., Amstelveen, S., Thoné, T., Martens, L., McKendrick, J. G., et al. (2025) · Immunity · doi:10.1016/j.immuni.2025.01.002 · Cited by 94 · see citing works
Evaluating batch correction methods for image-based cell profiling
Arevalo, J., Su, E., van Dijk, R., Carpenter, A. E., & Singh, S. (2023) · doi:10.1101/2023.09.15.558001 · Cited by 89 · see citing works
Resolving tissue complexity by multimodal spatial omics modeling with MISO
Coleman, K., Schroeder, A., Loth, M., Zhang, D., Park, J. H., Sung, J.-Y., Blank, N., Cowan, A. J., Qian, X., Chen, J., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02574-2 · Cited by 87 · see citing works
A constitutive interferon-high immunophenotype defines response to immunotherapy in colorectal cancer
Acha-Sagredo, A., Andrei, P., Clayton, K., Taggart, E., Antoniotti, C., Woodman, C. A., Afrache, H., Fourny, C., Armero, M., Moinudeen, H. K., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2024.12.008 · Cited by 71 · see citing works
Mechanisms of Response and Tolerance to Active RAS Inhibition in <i>KRAS</i> -Mutant Non–Small Cell Lung Cancer
Araujo, H. A., Pechuan-Jorge, X., Zhou, T., Do, M. T., Hu, X., Rojas Alvarez, F. R., Salvatierra, M. E., Ibarguen, H. P., Lee, R., Raghulan, R., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-24-0421 · Cited by 70 · see citing works
HIF regulates multiple translated endogenous retroviruses: Implications for cancer immunotherapy
Jiang, Q., Braun, D. A., Clauser, K. R., Ramesh, V., Shirole, N. H., Duke-Cohan, J. E., Nabilsi, N., Kramer, N. J., Forman, C., Lippincott, I. E., et al. (2025) · Cell · doi:10.1016/j.cell.2025.01.046 · Cited by 68 · see citing works
Normalization for sampled count data
Booeshaghi, A. S., Hallgrímsdóttir, I. B., Gálvez-Merchán, Á., & Pachter, L. (2022) · doi:10.1101/2022.05.06.490859 · Cited by 39 · see citing works
Seq-Scope: Repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics
Kim, Y., Cheng, W., Cho, C.-S., Hwang, Y., Si, Y., Park, A., Schrank, M., Hsu, J.-E., Anacleto, A., Xi, J., et al. (2025) · Nature Protocols · doi:10.1038/s41596-024-01065-0 · Cited by 44 · see citing works
Lymphoid gene expression supports neuroprotective microglia function
Ayata, P., Crowley, J. M., Challman, M. F., Sahasrabuddhe, V., Gratuze, M., Werneburg, S., Ribeiro, D., Hays, E. C., Durán-Laforet, V., Faust, T. E., et al. (2025) · Nature · doi:10.1038/s41586-025-09662-z · Cited by 32 · see citing works
APOE Christchurch enhances a disease-associated microglial response to plaque but suppresses response to tau pathology
Tran, K. M., Kwang, N. E., Butler, C. A., Gomez-Arboledas, A., Kawauchi, S., Mar, C., Chao, D., Barahona, R. A., Da Cunha, C., Tsourmas, K. I., et al. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-024-00793-x · Cited by 28 · see citing works
Lhx2 specifically expressed in HSCs promotes liver regeneration and inhibits liver fibrosis
Tao, J., Wu, Z., Liang, Y., Wang, J., Tang, M., Huang, S., Jiang, F., Zhou, G., Guo, L., Yuan, S., et al. (2025) · Hepatology · doi:10.1097/hep.0000000000001201 · Cited by 19 · see citing works
Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability
Bai, D., Cao, Z., Attada, N., Song, J., & Zhu, C. (2025) · Nature Methods · doi:10.1038/s41592-025-02632-3 · Cited by 16 · see citing works
Vaccine-induced CD8+ T cells are key to protection from SARS-CoV-2
Antoun, E., Peng, Y., & Dong, T. (2023) · Nature Immunology · doi:10.1038/s41590-023-01621-y · Cited by 14 · see citing works
scDrugMap: benchmarking large foundation models for drug response prediction
Wang, Q., Pan, Y., Zhou, M., Tang, Z., Wang, Y., Wang, G., & Song, Q. (2025) · Nature Communications · doi:10.1038/s41467-025-67481-2 · Cited by 12 · see citing works
Molecular and cellular processes disrupted in the early postnatal Down syndrome prefrontal cortex
Risgaard, R. D., Hanthanan Arachchilage, K., Knaack, S. A., Hosseini, M., Chen, R. J., Kumarage, P., Schmidt, D. K., Huang, X., Sheng, J., Wang, C. J., et al. (2026) · Science · doi:10.1126/science.aea1549 · Cited by 12 · see citing works
Chromatin remodeller Chd7 is developmentally regulated in the neural crest by tissue-specific transcription factors
Williams, R. M., Taylor, G., Ling, I. T. C., Candido-Ferreira, I., Fountain, D. M., Mayes, S., Ateş-Kalkan, P. S., Haug, J. O., Price, A. J., McKinney, S. A., et al. (2024) · PLOS Biology · doi:10.1371/journal.pbio.3002786 · Cited by 12 · see citing works
Cellular senescence in human liver under normal aging and cancer
Karpova, A., Li, X., Peng, C.-W., Gallant, K. L., Rapp, D. R., Alligood, D. M., Houston, A. J., Park, A., Targino da Costa, A. L. N., Chou, W.-H., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2025.101133 · Cited by 13 · see citing works
Apolipoprotein E abundance is elevated in the brains of individuals with Down syndrome–Alzheimer’s disease
Farrell, C., Buhidma, Y., Mumford, P., Heywood, W. E., Hällqvist, J., Flores-Aguilar, L., Andrews, E. J., Rahimzadah, N., Taso, O. S., Doran, E., et al. (2025) · Acta Neuropathologica · doi:10.1007/s00401-025-02889-0 · Cited by 9 · see citing works
Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing
Kersey, H. N., Acri, D. J., Dabin, L. C., Hartigan, K. A., Mustaklem, R., Park, J. H., & Kim, J. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101337 · Cited by 8 · see citing works
Spatial lipidomics reveals demyelination and remyelination dynamics in the mouse brain
Opielka, M., Urbanowicz, K., Konieczna-Wolska, K., Müller, E., Krajewski, O., Feucherolles, M., Zhou, Q., Bienkowski, M., Frache, G., Hopf, C., et al. (2025) · Journal of Lipid Research · doi:10.1016/j.jlr.2025.100912 · Cited by 8 · see citing works
Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression
Nassiri, I., Kwok, A. J., Bhandari, A., Bull, K. R., Garner, L. C., Klenerman, P., Webber, C., Parkkinen, L., Lee, A. W., Wu, Y., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae085 · Cited by 8 · see citing works
The RNA exosome maintains cellular RNA homeostasis by controlling transcript abundance in the brain
Higginson, L. A., Wang, X., He, K., Torstrick, M., Kim, M., Wijeratne, H. R. S., Runnebohm, A. M., Benayoun, B. A., MacLean, A., Chanfreau, G. F., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2025.116729 · Cited by 7 · see citing works
Microglial states associate with lesion dynamics in multiple sclerosis
van den Bosch, A. M. R., Khoo, J. H., Lu, Z., Liang, H., Wever, D., Pu, L., Eggen, B. J. L., Uhlén, M., Smolders, J., Hamann, J., et al. (2026) · Cell Reports · doi:10.1016/j.celrep.2026.117538 · Cited by 6 · see citing works
Peroxisomal integrity in demyelination-associated microglia enables cellular debris clearance and myelin renewal in mice
Barnes-Vélez, J. A., Zhang, X., Peña Señeriz, Y. L., Scott, K. A., Guan, Y., & Hu, J. (2026) · Journal of Clinical Investigation · doi:10.1172/jci179985 · Cited by 6 · see citing works
Integrated multi omics and machine learning reveal mitochondrial immunometabolic networks in sepsis associated encephalopathy
Zhang, Z., Qiu, X., Zeng, X., Liu, X., Lu, J., Xu, C., Huang, J., Zhao, C., & Zhan, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-18650-2 · Cited by 6 · see citing works
Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT
He, F., Fei, R., Krull, J. E., Yu, Y., Zhang, X., Wang, X., Cheng, H., Gao, M., Su, L., Chen, Y., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01170-z · Cited by 5 · see citing works
The Therapeutic Potential of Farm Dust Extracts in a Mouse Model of Eosinophilic Inflammation
Korkmaz, R. Ü., Omony, J., Tan, X., Klotz, M., Dragunas, G., Chen, S., Shankhwar, S., Ertüz, Z., Müller, C., Ragab, M., et al. (2026) · Allergy · doi:10.1111/all.70121 · Cited by 4 · see citing works
annATAC: automatic cell type annotation for scATAC-seq data based on language model
Cui, L., Wang, F., Li, H., Liu, Q., Zhou, M., & Wang, G. (2025) · BMC Biology · doi:10.1186/s12915-025-02244-5 · Cited by 3 · see citing works
Deletion of neuronal <i>Idol</i> ameliorates Alzheimer's disease–related pathologies via APOE receptors
Karahan, H., Hartigan, K., Al‐Amin, M. M., John, S. K., McCord, B., Wijeratne, H. R. S., Acri, D. J., Smith, D. C., Dabin, L. C., Cordero, H. M. R., et al. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70949 · Cited by 2 · see citing works
“71 Similarly, APOE2 exacerbated TDP-43 proteinopathy, 72 a pathology that is seen in 57% of patients with AD.”
A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics
Wang, Y., Zang, C., Li, Z., Guo, C. C., Lai, D., & Wei, P. (2026) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013956 · Cited by 2 · see citing works
PLCG2 downregulation impairs synaptic function and increases Alzheimer’s disease hallmarks in neuronal cultures
Coulon, A., Rabiller, F., Takalo, M., Roy, A., Pelletier, A., Martiskainen, H., Siedlecki-Wullich, D., Lannette-Weimann, N., Majerníková, N., Grenon, A., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02709-5 · Cited by 1 · see citing works
mTORC1 drives cell-autonomous astrocyte reactivity in tuberous sclerosis
Li, T. L., Blair, J. D., Yoo, T., Grant, G. A., Hockemeyer, D., Porter, B. E., & Bateup, H. S. (2026) · Nature · doi:10.1038/s41586-026-11054-w · Cited by 1 · see citing works
Myeloid cells contribute to bystander CD8 T cell accumulation in metabolic-associated steatohepatitis and are sufficient for fibrosis
Lebeaupin, C., Donahue, K. L., Lal, S., Christensen, S. M., Asano, S., Wadsworth, M. H., Bound, K., De, S., McMahon, J., Schlerman, F., et al. (2025) · bioRxiv (preprint) · doi:10.64898/2025.12.17.694415
Tau Ablation Rewires Brain Cell Programs in Health and Restores Function in Disease
Chimal-Juárez, E., Patel, H., Jury-Garfe, N., Dabin, L. C., Vidal, R., Kim, J., & Lasagna-Reeves, C. A. (2026) · bioRxiv · doi:10.64898/2026.05.12.724684
scChat: A Large Language Model-Powered Co-Pilot for Contextualized Single-Cell RNA Sequencing Analysis
Chiu, H.-H., Varghese, A., Shao, K., Lu, Y.-C., Nahar, R., Chen, H., Deng, Q., Bao, X., & Li, C. (2024) · doi:10.1101/2024.10.01.616063 · Cited by 6 · see citing works

Authors: Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., & Satija, R.
Affiliations: New York Genome Center (US)
NYU Langone Health (US)
New York University (US)
Corresponding author: Satija, Rahul
ORCID: Hao, Yuhan
Stuart, Tim
Kowalski, Madeline H.
Choudhary, Saket
Hoffman, Paul
Hartman, Austin
Srivastava, Avi
Molla, Gesmira
Madad, Shaista
Satija, Rahul
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2023-05-25
Volume: 42
Issue: 2
Pages: 293-304
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-023-01767-y
PMC: PMC10928517
OpenAlex: W4378212544
Cited by: 5,631
References: 23
Citations by year: 2026: 2814 · 2025: 2053 · 2024: 699 · 2023: 53 · 2022: 8 · 2021: 2
FWCI: 488.32
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Biotechnology (publishedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Chan Zuckerberg Initiative (HCA-A1704-01895, EOSS-0000000082)
National Institutes of Health (1dp2hg009623-01, 5k99hg011489-02, OT2OD026673, OT2OD033760, K99HG011489-01)
National Human Genome Research Institute (DP2-HG-009623, K99 HG011489, RM1HG011014)
National Cancer Institute (K99CA267677, U24CA268108)
National Institute of Neurological Disorders and Stroke (UG3NS132139, R35NS097404)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD096770)
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 › Genomics and Chromatin Dynamics
Keywords: dictionary learning, single-cell data integration, chromatin accessibility, histone modifications, DNA methylation, protein abundance, sketching, mass cytometry, immune cell profiles
MeSH terms: Humans, Software, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, Transcriptome

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Record

About these checks