SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data
Open accessAbstract
Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene–peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.
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All tested metacell approaches were suitable for downstream network analysis in the CD34+ hematopoietic stem and progenitor cell dataset.
“We applied hdWGCNA to a dataset of CD34+ hematopoietic stem and progenitor stem cells16 using two additional metacell approaches14,16 and found that all approaches were suitable for downstream network analysis (Figure S2; Data S1).”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [16]
SEACells and Metacell2 do not account for cell labels in their aggregation procedures, which may result in a number of metacells containing transcriptomes from differently labeled cells.
“Notably, SEACells and Metacell2 do not account for cell labels in their aggregation procedures, which may result in a number of metacells containing transcriptomes from differently labeled cells.”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [16]
To reduce the influence of stochastic DNA damage signal, single cells were grouped into 300 metacells based on transcriptome profiles using SEACell before the DNA damage-gene expression correlation analysis.
“To minimize the influences from stochastic DNA damage signal, we used the SEACell software59 to group the single cells into 300 metacells based on transcriptome profiles.”
From Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability (2025) · cites this paper as [59]
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Dictionary learning for integrative, multimodal and scalable single-cell analysis
Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631 · see citing works
hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773 · see citing works
Gene regulatory network inference in the era of single-cell multi-omics
Badia-i-Mompel, P., Wessels, L., Müller‐Dott, S., Trimbour, R., Flores, R. O. R., Argelaguet, R., & Sáez-Rodríguez, J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00618-5 · Cited by 475 · see citing works
Lineage plasticity in prostate cancer depends on JAK/STAT inflammatory signaling
Chan, J. M., Zaidi, S., Love, J. R., Zhao, J. L., Setty, M. N., Wadosky, K. M., Gopalan, A., Choo, Z.-N., Persad, S., Choi, J., et al. (2022) · Science · doi:10.1126/science.abn0478 · Cited by 350 · see citing works
Self-citationProfiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 328 · see citing works
Automatic cell-type harmonization and integration across Human Cell Atlas datasets
Xu, C., Prete, M., Webb, S., Jardine, L., Stewart, B. J., Hoo, R., He, P., Meyer, K. B., & Teichmann, S. A. (2023) · Cell · doi:10.1016/j.cell.2023.11.026 · Cited by 289 · see citing works
Epigenetic plasticity cooperates with cell-cell interactions to direct pancreatic tumorigenesis
Burdziak, C., Alonso‐Curbelo, D., Walle, T., Reyes, J. S., Barriga, F. M., Haviv, D., Xie, Y., Zhao, Z., Zhao, C. J., Chen, H.-A., et al. (2023) · Science · doi:10.1126/science.add5327 · Cited by 207 · see citing works
Self-citationThe Human Cell Atlas from a cell census to a unified foundation model
Rood, J., Wynne, S., Robson, L., Hupalowska, A., Randell, J., Teichmann, S. A., & Regev, A. (2024) · Nature · doi:10.1038/s41586-024-08338-4 · Cited by 168 · see citing works
OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing
Zeng, Z., Ma, Y., Hu, L., Tan, B., Liu, P., Wang, Y., Xing, C., Xiong, Y., & Du, H. (2024) · Nature Communications · doi:10.1038/s41467-024-50194-3 · Cited by 102 · see citing works
Supervised discovery of interpretable gene programs from single-cell data
Kunes, R., Walle, T., Land, M., Nawy, T., & Pe’er, D. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01940-3 · Cited by 96 · see citing works
Self-citationEstablishing a conceptual framework for holistic cell states and state transitions
Rafelski, S. M., & Theriot, J. A. (2024) · Cell · doi:10.1016/j.cell.2024.04.035 · Cited by 92 · see citing works
Predicting cellular responses to perturbation across diverse contexts with State
Adduri, A. K., Gautam, D., Bevilacqua, B., Naghipourfar, M., Imran, A., Shah, R., Teyssier, N., Verma, R., Carpenter, C., Eraslan, B., et al. (2026) · Cell · doi:10.1016/j.cell.2026.07.052 · Cited by 100 · see citing works
Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas
Hrovatin, K., Bastidas-Ponce, A., Bakhti, M., Zappia, L., Büttner, M., Salinno, C., Sterr, M., Böttcher, A., Migliorini, A., Lickert, H., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00876-x · Cited by 82 · see citing works
The future of rapid and automated single-cell data analysis using reference mapping
Lotfollahi, M., Hao, Y., Theis, F. J., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.03.009 · Cited by 77 · see citing works
He, Y., Deng, Z., Chen, J., Li, W., Zhou, J., Xiang, F., & Hu, X. (2024) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2024.06.024 · Cited by 75 · see citing works
Metacells untangle large and complex single-cell transcriptome networks
Bilous, M., Tran, L., Cianciaruso, C., Gabriel, A. A., Michel, H., Carmona, S. J., Pittet, M. J., & Gfeller, D. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04861-1 · Cited by 74 · see citing works
Targeting the immune privilege of tumor-initiating cells to enhance cancer immunotherapy
Yang, C. G., Geng, H., Yang, X., Ji, S., Liu, Z., Feng, H., Li, Q., Zhang, T., Zhang, S., Ma, X., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.10.008 · Cited by 74 · see citing works
Ageing limits stemness and tumorigenesis by reprogramming iron homeostasis
Zhuang, X., Wang, Q., Joost, S., Ferrena, A., Humphreys, D. T., Li, Z., Blum, M., Krause, K., Ding, S., Landais, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-08285-0 · Cited by 70 · see citing works
Self-citationMultiscale footprints reveal the organization of cis-regulatory elements
Yan, H., Horlbeck, M. A., Zhang, R., Ma, S., Shrestha, R., Kartha, V. K., Duarte, F. M., Hock, C., Savage, R. E., Labade, A. S., et al. (2025) · Nature · doi:10.1038/s41586-024-08443-4 · Cited by 80 · see citing works
Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age
Wells, S. B., Rainbow, D. B., Mark, M., Szabo, P. A., Ergen, C., Caron, D. P., Maceiras, A. R., Rahmani, E., Benuck, E., Amiri, V. V. P., et al. (2025) · Nature Immunology · doi:10.1038/s41590-025-02241-4 · Cited by 66 · see citing works
Li, Y., Wang, X., Zhou, X., Chen, H., Song, S., Deng, L., Yao, Y., & Yin, X. (2025) · Nature Communications · doi:10.1038/s41467-024-55567-2 · Cited by 52 · see citing works
Kim, S., Lee, J., Koh, I. G., Ji, J., Kim, H. J., Kim, E., Park, J., Park, J.-E., & An, J. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01328-6 · Cited by 44 · see citing works
A single-nucleus transcriptomic atlas of the adult Aedes aegypti mosquito
Goldman, O. V., DeFoe, A. E., Qi, Y., Jiao, Y., Weng, S., Wick, B. D., Houri-Zeevi, L., Lakhiani, P., Morita, T., Razzauti, J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.10.008 · Cited by 43 · see citing works
Self-citationImaz-Rosshandler, I., Rode, C., Guibentif, C., Harland, L. T. G., Ton, M.-L. N., Dhapola, P., Keitley, D., Argelaguet, R., Calero‐Nieto, F. J., Nichols, J., et al. (2023) · Development · doi:10.1242/dev.201867 · Cited by 41 · see citing works
In vivo single-cell CRISPR uncovers distinct TNF programmes in tumour evolution
Renz, P. F., Ghoshdastider, U., Baghai Sain, S., Valdivia-Francia, F., Khandekar, A., Ormiston, M., Bernasconi, M., Duré, C., Kretz, J. A., Lee, M., et al. (2024) · Nature · doi:10.1038/s41586-024-07663-y · Cited by 40 · see citing works
Quantifying cell-state densities in single-cell phenotypic landscapes using Mellon
Otto, D. J., Jordan, C., Dury, B., Dien, C., & Setty, M. N. (2024) · Nature Methods · doi:10.1038/s41592-024-02302-w · Cited by 38 · see citing works
Self-citationEngineering mtDNA Deletions by Reconstituting End-Joining in Human Mitochondria
Fu, Y., Land, M., Kavlashvili, T., Cui, R., Kim, M., DeBitetto, E., Lieber, T., Ryu, K. W., Choi, E., Masilionis, I., et al. (2024) · Cell · doi:10.1016/j.cell.2025.02.009 · Cited by 36 · see citing works
Self-citationHafemeister, C., & Halbritter, F. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.28.534443 · Cited by 33 · see citing works
Epigenomic insights into common human disease pathology
Bell, C. G. (2024) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-024-05206-2 · Cited by 32 · see citing works
Loers, J. U., & Vermeirssen, V. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae382 · Cited by 33 · see citing works
Mapping enhancer–gene regulatory interactions from single-cell data
Sheth, M. U., Qiu, W., Ma, X. R., Gschwind, A. R., Jagoda, E., Tan, A. S., Galante, J., Ray, J., Amgalan, D., Einarsson, H., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02695-8 · Cited by 29 · see citing works
Chinese Immune Multi-Omics Atlas
Yin, J., Zheng, Y., Huang, Z., Zhou, W., Yuan, Y., Cai, P., Bai, Y., Yang, S., Gao, Y., Duan, S., et al. (2026) · Science · doi:10.1126/science.adt3130 · Cited by 34 · see citing works
Dietrich, A., Merotto, L., Pelz, K., Eder, B., Zackl, C., Reinisch, K., Edenhofer, F., Marini, F., Sturm, G., List, M., et al. (2026) · Genome biology · doi:10.1186/s13059-026-03955-w · Cited by 27 · see citing works
Bosone, C., Castaldi, D., Burkard, T. R., Guzman, S. J., Wyatt, T., Cheroni, C., Caporale, N., Bajaj, S., Bagley, J. A., Li, C. V., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02412-5 · Cited by 28 · see citing works
Phospho-seq: integrated, multi-modal profiling of intracellular protein dynamics in single cells
Blair, J. D., Hartman, A., Zenk, F. L., Wahle, P., Brancati, G., Dalgarno, C., Treutlein, B., & Satija, R. (2025) · Nature Communications · doi:10.1038/s41467-025-56590-7 · Cited by 25 · see citing works
Single-cell dissection of tumor microenvironmental response and resistance to cancer therapy
Luo, Y., & Liang, H. (2023) · Trends in Genetics · doi:10.1016/j.tig.2023.07.005 · Cited by 25 · see citing works
CHOIR improves significance-based detection of cell types and states from single-cell data
Sant, C., Mucke, L., & Corces, M. R. (2025) · Nature Genetics · doi:10.1038/s41588-025-02148-8 · Cited by 24 · see citing works
Missarova, A., Dann, E., Dann, E., Satija, R., & Marioni, J. C. (2024) · Genome biology · doi:10.1186/s13059-024-03334-3 · Cited by 32 · see citing works
Revealing gene function with statistical inference at single-cell resolution
Trapnell, C. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00750-w · Cited by 19 · see citing works
Joint representation and visualization of derailed cell states with Decipher
Nazaret, A., Fan, J. L., Lavallée, V., Burdziak, C., Cornish, A. E., Kiseliovas, V., Bowman, R. L., Masilionis, I., Chun, J., Eisman, S. E., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03682-8 · Cited by 16 · see citing works
Self-citationSingle-cell and spatial multiomic inference of gene regulatory networks using SCRIPro
Chang, Z., Xu, Y., Dong, X., Gao, Y., & Wang, C. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae466 · Cited by 18 · see citing works
Autophagic degradation of SQSTM1 enables fibroblast activation to accelerate wound healing
Xu, Y., Gu, X., Li, W., Lin, B., Xu, Y., Wei, Q., Liu, Q., Zhao, Y., Long, R., Jiang, H., et al. (2025) · Autophagy · doi:10.1080/15548627.2025.2508546 · Cited by 18 · see citing works
Interpretable inflammation landscape of circulating immune cells
Jiménez-Gracia, L., Maspero, D., Aguilar-Fernández, S., Craighero, F., Boulougouri, M., Ruiz, M., Marchese, D., Caratù, G., Liñares-Blanco, J., Berasategi, M., et al. (2026) · Nature Medicine · doi:10.1038/s41591-025-04126-3 · Cited by 18 · see citing works
Dissecting gene regulatory networks governing human cortical cell fate
Ding, J. W., Kim, C. N., Ostrowski, M. S., Abeykoon, Y., Pavlovic, B. J., Wallace, J. L., Schaefer, N. K., Nowakowski, T. J., & Pollen, A. A. (2026) · Nature · doi:10.1038/s41586-025-09997-7 · Cited by 20 · see citing works
Building and analyzing metacells in single-cell genomics data
Bilous, M., Hérault, L., Gabriel, A. A., Teleman, M., & Gfeller, D. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00045-6 · Cited by 18 · see citing works
Distinctive DNA sequence features define epigenetic longevity of inflammatory memory
Cowley, C. J., Sajjath, S. M., Soto-Ugaldi, L. F., Steiger, M., Larsen, S. B., Carroll, T., Barrows, D., Mattei, A. L., Gonzales, K. A. U., Wang, W., et al. (2026) · Science · doi:10.1126/science.adz6830 · Cited by 17 · see citing works
Self-citationSingle-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
Sensitive cluster-free differential expression testing.Preprint
Missarova, A. M., Rosen, L. U., Dann, E., Satija, R., & Marioni, J. C. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.03.08.531744 · Cited by 15 · see citing works
A multimodal cross-species comparison of pancreas development
Yang, K., Spitzer, H., Sterr, M., Hrovatin, K., Sean de la, O., Zhang, X., Setyono, E. S. A., Ud‐Dean, M., Walzthoeni, T., Flisikowski, K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64774-4 · Cited by 15 · see citing works
Zebrahub-Multiome: Uncovering Gene Regulatory Network Dynamics During Zebrafish EmbryogenesisPreprint
Kim, Y. J., VijayKumar, S., Iovino, B. G., Granados, A. A., Ancheta, S., Frank, M., Zhao, X., Awayan, K., Seng, A., Borja, M., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.18.618987 · Cited by 15 · see citing works
MetaQ: fast, scalable and accurate metacell inference via single-cell quantization
Li, Y., Li, H., Lin, Y., Zhang, D., Peng, D., Liu, X., Xie, J., Hu, P., Chen, L., Luo, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56424-6 · Cited by 13 · see citing works
SCIPAC: quantitative estimation of cell-phenotype associations
Gan, D.-L., Zhu, Y.-N., Lu, X., & Li, J. (2024) · Genome Biology · doi:10.1186/s13059-024-03263-1 · Cited by 12 · see citing works
Wang, Y., Li, J., Malcolm, A. A., Mansfield, W., Clark, S. J., Argelaguet, R., Biggins, L., Acton, R., Andrews, S., Reik, W., et al. (2025) · Science Advances · doi:10.1126/sciadv.adu3308 · Cited by 11 · see citing works
Cranley, J. J., Kanemaru, K., Bayraktar, S., Knight-Schrijver, V. R., Hulbert, R., Lana-Elola, E., Aoidi, R., Pett, J. P., Wilbrey-Clark, A., Polański, K., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.29.591736 · Cited by 12 · see citing works
Sim, H., Park, H. J., Park, G., Kim, Y. J., Park, W., Lee, S., & Choi, M. (2024) · eLife · doi:10.7554/elife.96951 · Cited by 11 · see citing works
Reyes, J., Priore, I. S. D., Chaikovsky, A. C., Pasnuri, N., Elhossiny, A. M., Park, J., Weiler, P., Krause, T., Moorman, A. R., Snopkowski, C., et al. (2026) · Cell · doi:10.1016/j.cell.2026.03.032 · Cited by 11 · see citing works
Self-citationPatir, A., Raper, A., Fleming, R., Henderson, B. E. P., Murphy, L., Henderson, N. C., Clark, E. L., Freeman, T. C., & Barnett, M. W. (2023) · G3 Genes Genomes Genetics · doi:10.1093/g3journal/jkad178 · Cited by 11 · see citing works
O’Leary, K., & Zheng, D. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00364-2 · Cited by 11 · see citing works
Amancherla, K. V., Schlendorf, K. H., Chow, N., Sheng, Q., Freedman, J. E., & Rathmell, J. C. (2024) · The Journal of Heart and Lung Transplantation · doi:10.1016/j.healun.2024.11.017 · Cited by 11 · see citing works
Peng, D., Gui, Z., Wei, W., Li, F., Gui, J., Wu, H., & Gong, J. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01112-9 · Cited by 11 · see citing works
Single-cell multiomics data integration and generation with scPairing
Niu, J., Vasquez-Rios, C., & Ding, J. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101211 · Cited by 11 · see citing works
HALO: hierarchical causal modeling for single cell multi-omics data
Mao, H. H., Jia, M., Di, M. E., Valenzi, E., Cai, X. T., Singh, H., Lafyatis, R., Zhang, K., & Benos, P. V. (2025) · Nature Communications · doi:10.1038/s41467-025-63921-1 · Cited by 10 · see citing works
Decoding Human Biology and Disease Using Single-Cell Omics Technologies
Shi, Q., Chen, X., & Zhang, Z. (2023) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2023.06.003 · Cited by 10 · see citing works
A single-cell atlas of transcribed cis -regulatory elements in the human genomePreprint
Moody, J., Kouno, T., Kojima, M., Koya, I., Leon, J., Suzuki, A., Hasegawa, A., Akiyama, T., Akiyama, N., Amagai, M., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.13.566791 · Cited by 10 · see citing works
Identifying similar populations across independent single cell studies without data integration
González-Velasco, Ó., Simon, M., Yilmaz, R., Parlato, R., Weishaupt, J. H., Imbusch, C. D., & Brors, B. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf042 · Cited by 10 · see citing works
Alsulami, R., Lehmann, R., Khan, S. A., Lagani, V., Maillo, A., Gómez-Cabrero, D., Kiani, N. A., & Tegner, J. N. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01202-2 · Cited by 12 · see citing works
Predicting the impact of sequence motifs on gene regulation using single-cell data
Hepkema, J., Lee, N. K., Stewart, B. J., Ruangroengkulrith, S., Charoensawan, V., Clatworthy, M. R., & Hemberg, M. (2023) · Genome biology · doi:10.1186/s13059-023-03021-9 · Cited by 9 · see citing works
Leveraging gene correlations in single cell transcriptomic data
Silkwood, K., Dollinger, E., Gervin, J., Atwood, S. X., Nie, Q. H., & Lander, A. D. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05926-z · Cited by 9 · see citing works
MLL oncoprotein levels influence leukemia lineage identities
Janssens, D. H., Duran, M., Otto, D. J., Wu, W., Xu, Y., Kirkey, D. C., Mullighan, C. G., Yi, J. S., Meshinchi, S., Sarthy, J. F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53399-8 · Cited by 15 · see citing works
Chrysalis: decoding tissue compartments in spatial transcriptomics with archetypal analysis
Túrós, D., Vasiljević, J., Hahn, K., Rottenberg, S., & Valdeolivas, A. (2024) · Communications Biology · doi:10.1038/s42003-024-07165-7 · Cited by 8 · see citing works
A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data
Madrigal, A., Lu, T., Soto, L. M., & Najafabadi, H. S. (2024) · Nature Communications · doi:10.1038/s41467-024-50963-0 · Cited by 8 · see citing works
HIPSD&R-seq enables scalable genomic copy number and transcriptome profiling
Otoničar, J., Lazareva, O., Mallm, J., Simovic-Lorenz, M., Philippos, G., Sant, P., Parekh, U., Hammann, L., Li, A., Yildiz, U., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03450-0 · Cited by 7 · see citing works
Zhang, C., Hu, Y., Fang, J., Yin, Y., Huang, D., & Qin, N. (2025) · Energy · doi:10.1016/j.energy.2025.138045 · Cited by 8 · see citing works
Milite, S., Caravagna, G., & Sottoriva, A. (2025) · Genome biology · doi:10.1186/s13059-025-03530-9 · Cited by 8 · see citing works
Aliee, H., Kapl, F., Pham, D., Çakır, B., Jimba, T., Cranley, J. J., Teichmann, S. A., Meyer, K. B., Vento-Tormo, R., & Theis, F. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.06.627196 · Cited by 8 · see citing works
Guo, W., Li, X., Wang, D., Yan, N., Hu, Q., Yang, F., Zhang, X., Yao, J., & Gu, J. (2024) · Genome biology · doi:10.1186/s13059-024-03436-y · Cited by 7 · see citing works
Kwok, A. W. C., Shim, H., & McCarthy, D. J. (2025) · Genome biology · doi:10.1186/s13059-025-03735-y · Cited by 7 · see citing works
Liu, P., & Li, J. J. (2025) · Nature Communications · doi:10.1038/s41467-025-63626-5 · Cited by 7 · see citing works
Li, S., Li, Y., Sun, Y., Li, Y., Chen, X., Tang, S., & Chen, S. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae191 · Cited by 7 · see citing works
Yang, Y., & Pe’er, D. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae234 · Cited by 7 · see citing works
Self-citationAltered AP-1, RUNX and EGR chromatin dynamics drive fibrotic lung diseasePreprint
Valenzi, E., Jia, M., Gerges, P., Fan, J., Tabib, T., Behara, R., Zhou, Y., Sembrat, J., Das, J., Benos, P. V., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.23.619858 · Cited by 6 · see citing works
Regulatory logic of neuronal differentiation in the Drosophila visual system
Treese, M., Chen, Y., Tyree, A., Coyne, R., Lake, C. M., Tabi, O. B., Rajesh, R., Chen, Y.-C. D., Hassan, H., Li, H., et al. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2614314123 · Cited by 5 · see citing works
Liang, M., Chu, L. M., & Yue, Z. (2025) · Movement Disorders · doi:10.1002/mds.30097 · Cited by 6 · see citing works
Metabolic Objectives and Trade-Offs: Inference and Applications
Lin, D.-W., Khattar, S., & Chandrasekaran, S. (2025) · Metabolites · doi:10.3390/metabo15020101 · Cited by 5 · see citing works
On metrics for subpopulation detection in single-cell and spatial omics data
Luo, S., Germain, P., von Meyenn, F., & Robinson, M. D. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf921 · Cited by 6 · see citing works
SuperCellCyto: enabling efficient analysis of large scale cytometry datasets
Putri, G., Howitt, G., Marsh‐Wakefield, F., Ashhurst, T. M., & Phipson, B. (2024) · Genome biology · doi:10.1186/s13059-024-03229-3 · Cited by 5 · see citing works
Aging limits stemness and tumorigenesis in the lung by reprogramming iron homeostasisPreprint
Zhuang, X., Wang, Q., Joost, S., Ferrena, A., Humphreys, D. T., Li, Z., Blum, M., Bastl, K., Ding, S., Landais, Y., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.23.600305 · Cited by 5 · see citing works
Self-citationQiu, J., Ye, D., Chen, X. E., Dangle, N. J., Yoshor, B., Zhang, T., Shao, Y., Nallamala, V. C., Wang, S., Ren, D., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.13.607862 · Cited by 5 · see citing works
Nikhat, A., Mandal, T., Veerasubramanian, N., & Chakrabarti, S. (2026) · iScience · doi:10.1016/j.isci.2026.115394 · Cited by 4 · see citing works
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Qian, L., & Zeng, Z. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1382524 · Cited by 4 · see citing works
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- Authors
- Persad, S., Choo, Z.-N., Dien, C., Sohail, N., Masilionis, I., Chaligné, R., Nawy, T., Brown, C. C., Sharma, R., Pe’er, I., Setty, M., & Pe’er, D.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Fred Hutch Cancer Center (US)
- Columbia University (US)
- Corresponding author
- Pe’er, Itsik
- Setty, Manu
- Pe’er, Dana
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2023-03-27
- Volume
- 41
- Issue
- 12
- Pages
- 1746-1757
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC10713451
- OpenAlex
- W4360999291
- Cited by
- 222
- References
- 72
- Citations by year
- 2026: 72 · 2025: 54 · 2024: 72 · 2023: 20 · 2022: 3 · 2013: 1
- FWCI
- 19.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Biotechnology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R35 GM147125)
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U54 CA209975, U2C CA233284)
- Howard Hughes Medical Institute
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- metacells, transcriptional heterogeneity, chromatin accessibility, ATAC-seq, single-cell RNA sequencing, hematopoietic differentiation, CD4+ T cell activation, COVID-19 pandemic, data integration
- MeSH terms
- Algorithms, Chromatin, Humans, CD4-Positive T-Lymphocytes, Genomics, Epigenomics, Single-Cell Analysis
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