The landscape of cell–cell communication through single-cell transcriptomics

Inference and analysis of cell-cell communication using CellChat

Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C.-H., Myung, P., Plikus, M. V., & Nie, Q. (2021) · Nature Communications · doi:10.1038/s41467-021-21246-9 · Cited by 8,409 · see citing works

Visualization and analysis of gene expression in tissue sections by spatial transcriptomics

Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834 · see citing works

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

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

Simultaneous epitope and transcriptome measurement in single cells

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

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution

Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424 · see citing works

Single-cell reconstruction of the early maternal–fetal interface in humans

Vento-Tormo, R., Efremova, M., Botting, R. A., Turco, M. Y., Vento-Tormo, M., Meyer, K. B., Park, J.-E., Stephenson, E., Polański, K., Goncalves, A., et al. (2018) · Nature · doi:10.1038/s41586-018-0698-6 · Cited by 2,358 · see citing works

Single-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

NicheNet: Modeling intercellular communication by linking ligands to target genes

Browaeys, R., Saelens, W., & Saeys, Y. (2020) · Nature Methods · doi:10.1038/s41592-019-0667-5 · Cited by 2,192 · see citing works

Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH

Eng, C.-H. L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., et al. (2019) · Nature · doi:10.1038/s41586-019-1049-y · Cited by 1,754 · see citing works

A comparison of single-cell trajectory inference methods

Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670 · see citing works

Deciphering cell–cell interactions and communication from gene expression

Armingol, E., Officer, A., Harismendy, O., & Lewis, N. E. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-00292-x · Cited by 1,267 · see citing works

Exponential scaling of single-cell RNA-seq in the past decade

Svensson, V., Vento‐Tormo, R., & Teichmann, S. A. (2018) · Nature Protocols · doi:10.1038/nprot.2017.149 · Cited by 936 · see citing works

Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data

Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A., & Murali, T. M. (2020) · Nature Methods · doi:10.1038/s41592-019-0690-6 · Cited by 792 · see citing works

Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Pham, D., Tan, X., Balderson, B., Xu, J., Grice, L. F., Yoon, S., Willis, E. F., Tran, M., Lam, P. Y., Raghubar, A. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43120-6 · Cited by 716 · see citing works

Stellate Cells, Hepatocytes, and Endothelial Cells Imprint the Kupffer Cell Identity on Monocytes Colonizing the Liver Macrophage Niche

Bonnardel, J., T’Jonck, W., Gaublomme, D., Browaeys, R., Scott, C. L., Martens, L., Vanneste, B., De Prijck, S., Nedospasov, S. A., Kremer, A., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.08.017 · Cited by 631 · see citing works

Dysregulation of brain and choroid plexus cell types in severe COVID-19

Yang, A. C., Kern, F., Losada, P. M., Agam, M. R., Maat, C. A., Schmartz, G. P., Fehlmann, T., Stein, J. A., Schaum, N., Lee, D. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03710-0 · Cited by 620 · see citing works

Inference and analysis of cell-cell communication using CellChatPreprint

Jin, S., Guerrero‐Juarez, C. F., Zhang, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.21.214387 · Cited by 604 · see citing works

Coupled scRNA-Seq and Intracellular Protein Activity Reveal an Immunosuppressive Role of TREM2 in Cancer

Katzenelenbogen, Y., Sheban, F., Yalin, A., Yofe, I., Svetlichnyy, D., Jaitin, D. A., Bornstein, C., Moshe, A., Keren-Shaul, H., Cohen, M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.032 · Cited by 498 · see citing works

Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography

Andersson, A., Bergenstråhle, J., Asp, M., Bergenstråhle, L., Jurek, A., Navarro, J. F., & Lundeberg, J. (2020) · Communications Biology · doi:10.1038/s42003-020-01247-y · Cited by 463 · see citing works

Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function

Wang, L., Peng, Y., Zhou, B., Song, J., Li, Z., Zhang, M., Guo, G., Wang, Y., Chen, X., Han, L., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-019-0446-7 · Cited by 411 · see citing works

Inferring spatial and signaling relationships between cells from single cell transcriptomic data

Cang, Z., & Nie, Q. H. (2020) · Nature Communications · doi:10.1038/s41467-020-15968-5 · Cited by 393 · see citing works

SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics

Cabello‐Aguilar, S., Alame, M., Kon-Sun-Tack, F., Fau, C., Lacroix, M., & Colinge, J. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa183 · Cited by 390 · see citing works

stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissuesPreprint

Pham, D., Tan, X., Xu, J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.31.125658 · Cited by 377 · see citing works

iTALK: an R Package to Characterize and Illustrate Intercellular CommunicationPreprint

Wang, Y., Wang, R., Zhang, S., Song, S., Jiang, C., Han, G., Wang, M. L., Ajani, J. A., Futreal, A., & Wang, L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/507871 · Cited by 335 · see citing works

Predicting cell-to-cell communication networks using NATMI

Hou, R., Denisenko, E., Ong, H. T., Ramilowski, J. A., & Forrest, A. R. R. (2020) · Nature Communications · doi:10.1038/s41467-020-18873-z · Cited by 285 · see citing works

Gene expression cartography

Nitzan, M., Karaiskos, N., Friedman, N., & Rajewsky, N. (2019) · Nature · doi:10.1038/s41586-019-1773-3 · Cited by 272 · see citing works

Identification of spatially associated subpopulations by combining scRNAseq and sequential fluorescence in situ hybridization data

Zhu, Q., Shah, S., Dries, R., Cai, L., & Yuan, G. (2018) · Nature Biotechnology · doi:10.1038/nbt.4260 · Cited by 248 · see citing works

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis

Arnol, D., Schapiro, D., Bodenmiller, B., Sáez-Rodríguez, J., & Stegle, O. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.077 · Cited by 218 · see citing works

Dissection of intercellular communication using the transcriptome-based framework ICELLNET

Noël, F., Massenet-Regad, L., Carmi-Levy, I., Cappuccio, A., Grandclaudon, M., Trichot, C., Kieffer, Y., Mechta‐Grigoriou, F., & Soumelis, V. (2021) · Nature Communications · doi:10.1038/s41467-021-21244-x · Cited by 205 · see citing works

Distinct Regulatory Programs Control the Latent Regenerative Potential of Dermal Fibroblasts during Wound Healing

Abbasi, S., Sinha, S., Labit, E., Rosin, N. L., Yoon, G., Rahmani, W. M., Jaffer, A., Sharma, N. C., Hagner, A., Shah, P. T., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.07.008 · Cited by 199 · see citing works

A survey of community detection methods in multilayer networks

Huang, X., Chen, D., Ren, T., & Wang, D. (2020) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-020-00716-6 · Cited by 179 · see citing works

scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles

Jin, S., Zhang, L., & Nie, Q. H. (2020) · Genome biology · doi:10.1186/s13059-020-1932-8 · Cited by 174 · see citing works

Exponential scaling of single-cell RNA-seq in the last decadePreprint

Svensson, V., Tormo, R. V., & Teichmann, S. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01379 · Cited by 166 · see citing works

Cell lineage and communication network inference via optimization for single-cell transcriptomics

Wang, S., Karikomi, M. K., MacLean, A. L., & Nie, Q. H. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz204 · Cited by 153 · see citing works

Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly

Ren, X., Zhong, G., Zhang, Q., Zhang, L., Sun, Y., & Zhang, Z. (2020) · Cell Research · doi:10.1038/s41422-020-0353-2 · Cited by 148 · see citing works

Joint single-cell measurements of nuclear proteins and RNA in vivo

Chung, H., Parkhurst, C. N., Magee, E. M., Phillips, D., Habibi, E., Chen, F., Yeung, B. Z., Waldman, J., Artis, D., & Regev, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01278-1 · Cited by 144 · see citing works

Giotto, a toolbox for integrative analysis and visualization of spatial expression dataPreprint

Dries, R., Zhu, Q., Dong, R., Eng, C.-H. L., Li, H., Liu, K., Fu, Y., Zhao, T., Sarkar, A., Bao, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/701680 · Cited by 138 · see citing works

From whole-mount to single-cell spatial assessment of gene expression in 3D

Waylen, L. N., Nim, H. T., Martelotto, L. G., & Ramialison, M. (2020) · Communications Biology · doi:10.1038/s42003-020-01341-1 · Cited by 136 · see citing works

CytoTalk: De novo construction of signal transduction networks using single-cell transcriptomic data

Hu, Y., Peng, T., Gao, L., & Tan, K. (2021) · Science Advances · doi:10.1126/sciadv.abf1356 · Cited by 136 · see citing works

Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19

Cheng, J., Zhang, J., Wu, Z., & Sun, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa327 · Cited by 125 · see citing works

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

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

Identification of genomic enhancers through spatial integration of single‐cell transcriptomics and epigenomics

Bravo González‐Blas, C., Quan, X., Duran‐Romaña, R., Taskiran, I. I., Koldere, D., Davie, K., Christiaens, V., Makhzami, S., Hulselmans, G., de Waegeneer, M., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209438 · Cited by 88 · see citing works

Computational Methods for Single-Cell RNA Sequencing

Hie, B., Peters, J. M., Nyquist, S. K., Shalek, A. K., Berger, B., & Bryson, B. D. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-012220-100601 · Cited by 87 · see citing works

SWISS: multiplexed orthogonal genome editing in plants with a Cas9 nickase and engineered CRISPR RNA scaffolds

Li, C., Zong, Y., Jin, S., Zhu, H., Lin, D., Li, S., Qiu, J., Wang, Y., & Gao, C. (2020) · Genome biology · doi:10.1186/s13059-020-02051-x · Cited by 78 · see citing works

PyMINEr Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq

Tyler, S. R., Rotti, P. G., Sun, X., Yi, Y., Xie, W., Winter, M. C., Flamme‐Wiese, M. J., Tucker, B. A., Mullins, R. F., Norris, A. W., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.063 · Cited by 76 · see citing works

Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data

Sha, Y., Wang, S., Zhou, P., & Nie, Q. H. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa725 · Cited by 59 · see citing works

Uncovering hypergraphs of cell-cell interaction from single cell RNA-sequencing dataPreprint

Tsuyuzaki, K., Ishii, M., & Nikaido, I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/566182 · Cited by 54 · see citing works

Systematic Identification of Cell-Cell Communication Networks in the Developing Brain

Sheikh, B. N., Bondareva, O., Guhathakurta, S., Tsang, T. H., Sikora, K., Aizarani, N., Sagar, Holz, H., Grün, D., Hein, L., et al. (2019) · iScience · doi:10.1016/j.isci.2019.10.026 · Cited by 53 · see citing works

APERO: a genome-wide approach for identifying bacterial small RNAs from RNA-Seq data

Léonard, S., Meyer, S., Lacour, S., Nasser, W., Hommais, F., & Reverchon, S. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz485 · Cited by 48 · see citing works

Distinct Regulatory Programs Control the Latent Regenerative Potential of Dermal Fibroblasts during Wound Healing

Abbasi, S., Sinha, S., Labit, E., Rosin, N. L., Yoon, G., Rahmani, W. M., Jaffer, A., Sharma, N. C., Hagner, A., Shah, P. T., et al. (2021) · Cell stem cell · doi:10.1016/j.stem.2021.02.004 · Cited by 35 · see citing works

Broad transcriptional dysregulation of brain and choroid plexus cell types with COVID-19Preprint

Yang, A. C., Kern, F., Losada, P. M., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.22.349415 · Cited by 29 · see citing works

Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial dataPreprint

Tanevski, J., Flores, R. O. R., Gábor, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.08.084145 · Cited by 28 · see citing works

COMUNET: a tool to explore and visualize intercellular communication

Solovey, M., & Scialdone, A. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa482 · Cited by 25 · see citing works

Simultaneous single cell measurements of intranuclear proteins and gene expressionPreprint

Chung, H., Parkhurst, C. N., Magee, E. M., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.18.427139 · Cited by 24 · see citing works

Explainable multi-view framework for dissecting intercellular signaling from highly multiplexed spatial dataPreprint

Tanevski, J., Gábor, A., Flores, R. O. R., Schapiro, D., & Sáez-Rodríguez, J. (2021) · Research Square · doi:10.21203/rs.3.rs-735362/v1 · Cited by 12 · see citing works

CytoTalk: De novo construction of signal transduction networks using single-cell RNA-Seq dataPreprint

Hu, Y., Peng, T., Gao, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.29.014464 · Cited by 4 · see citing works

The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth

de Visser, K. E., & Joyce, J. A. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.02.016 · Cited by 4,059 · see citing works

CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics

Jin, S., Plikus, M. V., & Nie, Q. (2025) · Nature Protocols · doi:10.1038/s41596-024-01045-4 · Cited by 1,448 · see citing works

Comparison of methods and resources for cell-cell communication inference from single-cell RNA-Seq data

Dimitrov, D., Türei, D., Garrido-Rodriguez, M., Burmedi, P. L., Nagai, J. S., Boys, C., Ramirez Flores, R. O., Kim, H., Szalai, B., Costa, I. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30755-0 · Cited by 664 · see citing works

Molecularly defined and spatially resolved cell atlas of the whole mouse brain

Zhang, M., Pan, X., Jung, W., Halpern, A. R., Eichhorn, S. W., Lei, Z., Cohen, L., Smith, K. A., Tasic, B., Yao, Z., et al. (2023) · Nature · doi:10.1038/s41586-023-06808-9 · Cited by 506 · see citing works

Screening cell–cell communication in spatial transcriptomics via collective optimal transport

(2023) · doi:10.1038/s41592-022-01728-4 · Cited by 484 · see citing works

Spatial components of molecular tissue biology

Palla, G., Fischer, D. S., Regev, A., & Theis, F. J. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01182-1 · Cited by 315 · see citing works

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication

Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 237 · see citing works

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Shao, X., Li, C., Yang, H., Lu, X., Liao, J., Qian, J., Wang, K., Cheng, J., Yang, P., Chen, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32111-8 · Cited by 201 · see citing works

The diversification of methods for studying cell–cell interactions and communication

Armingol, E., Baghdassarian, H., & Lewis, N. E. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-023-00685-8 · Cited by 209 · see citing works

Statistical and machine learning methods for spatially resolved transcriptomics data analysis

Zeng, Z., Li, Y., Li, Y., & Luo, Y. (2022) · Genome Biology · doi:10.1186/s13059-022-02653-7 · Cited by 186 · see citing works

Single-cell atlases: shared and tissue-specific cell types across human organs

Elmentaite, R., Conde, C. D., Yang, L., & Teichmann, S. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00449-w · Cited by 171 · see citing works

Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology

Hu, T., Allam, M., Cai, S., Henderson, W. E., Yueh, B., Alici-Garipcan, A., Ievlev, A. V., Afkarian, M., Beyaz, S., & Coskun, A. F. (2023) · Nature Communications · doi:10.1038/s41467-023-43917-5 · Cited by 168 · see citing works

Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Liu, Z., Sun, D., & Wang, C. (2022) · Genome biology · doi:10.1186/s13059-022-02783-y · Cited by 162 · see citing works

Comparative analysis of cell–cell communication at single-cell resolution

Wilk, A. J., Shalek, A. K., Holmes, S., & Blish, C. A. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01782-z · Cited by 139 · see citing works

MultiNicheNet: a flexible framework for differential cell-cell communication analysis from multi-sample multi-condition single-cell transcriptomics dataPreprint

Browaeys, R., Gilis, J., Sang-aram, C., Bleser, P. D., Hoste, L., Tavernier, S. J., Lambrechts, D., Seurinck, R., & Saeys, Y. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.13.544751 · Cited by 126 · see citing works

Computational solutions for spatial transcriptomics

Kleino, I., Frolovaitė, P., Suomi, T., & Elo, L. L. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.08.043 · Cited by 118 · see citing works

SpatialDM for rapid identification of spatially co-expressed ligand–receptor and revealing cell–cell communication patterns

Li, Z., Wang, T., Liu, P., & Huang, Y. (2023) · Nature Communications · doi:10.1038/s41467-023-39608-w · Cited by 125 · see citing works

Context-aware deconvolution of cell–cell communication with Tensor-cell2cell

Armingol, E., Baghdassarian, H., Martino, C., Pérez-López, A., Aamodt, C., Knight, R. D., & Lewis, N. E. (2022) · Nature Communications · doi:10.1038/s41467-022-31369-2 · Cited by 109 · see citing works

Transcriptomics, proteomics, and metabolomics interventions prompt crop improvement against metal(loid) toxicity

Raza, A., Salehi, H., Bashir, S., Tabassum, J., Jamla, M., Charagh, S., Barmukh, R., Mir, R. A., Bhat, B. A., Javed, M. A., et al. (2024) · Plant Cell Reports · doi:10.1007/s00299-024-03153-7 · Cited by 88 · see citing works

Deciphering cell–cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network

Yang, W., Wang, P., Xu, S., Wang, T., Luo, M., Cai, Y., Xu, C., Xue, G., Que, J., Ding, Q., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51329-2 · Cited by 80 · see citing works

Computational challenges and opportunities in spatially resolved transcriptomic data analysis

Atta, L., & Fan, J. (2021) · Nature Communications · doi:10.1038/s41467-021-25557-9 · Cited by 77 · see citing works

ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methodsPreprint

Luo, J., Deng, M., Zhang, X., & Sun, X. (2023) · Genome Research · doi:10.1101/gr.278001.123 · Cited by 78 · see citing works

Spatial transcriptomics delineates molecular features and cellular plasticity in lung adenocarcinoma progression

Wang, Y., Liu, B., Min, Q., Yang, X., Yan, S., Ma, Y., Li, S., Fan, J., Wang, Y., Dong, B., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00591-7 · Cited by 76 · see citing works

Identify, quantify and characterize cellular communication from single-cell RNA sequencing data with scSeqComm

Baruzzo, G., Cesaro, G., & Camillo, B. D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac036 · Cited by 61 · see citing works

Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry

Zhang, Q., Jiang, S., Schroeder, A. R., Hu, J., Li, K., Zhang, B., Dai, D., Lee, E. B., Xiao, R., & Li, M. (2023) · Nature Communications · doi:10.1038/s41467-023-39895-3 · Cited by 56 · see citing works

Macrophages as key regulators of liver health and disease

Papachristoforou, E., & Ramachandran, P. (2022) · International review of cytology · doi:10.1016/bs.ircmb.2022.04.006 · Cited by 55 · see citing works

Alpha 7 nicotinic acetylcholine receptors signaling boosts cell-cell interactions in macrophages effecting anti-inflammatory and organ protection

Nakamura, Y., Matsumoto, H., Wu, C.-H., Fukaya, D., Uni, R., Hirakawa, Y., Katagiri, M., Yamada, S., Ko, T., Nomura, S., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05051-2 · Cited by 46 · see citing works

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

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

CLARIFY: cell–cell interaction and gene regulatory network refinement from spatially resolved transcriptomics

Bafna, M., Li, H., & Zhang, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad269 · Cited by 43 · see citing works

Multiscale proteomic modeling reveals protein networks driving Alzheimer’s disease pathogenesis

Wang, E., Yu, K., Cao, J., Wang, M., Katsel, P., Song, W., Wang, Z., Li, Y., Wang, X., Wang, Q., et al. (2025) · Cell · doi:10.1016/j.cell.2025.08.038 · Cited by 44 · see citing works

Crosstalk between glioblastoma and tumor microenvironment drives proneural–mesenchymal transition through ligand-receptor interactions

Lai, Y., Lu, X., Liao, Y., Ouyang, P., Wang, H., Zhang, X., Huang, G., Qi, S., & Li, Y. (2023) · Genes & Diseases · doi:10.1016/j.gendis.2023.05.025 · Cited by 40 · see citing works

Pathogenesis, therapeutic strategies and biomarker development based on “omics” analysis related to microglia in Alzheimer’s disease

Gao, C., Shen, X., Tan, Y., & Chen, S. (2022) · Journal of Neuroinflammation · doi:10.1186/s12974-022-02580-1 · Cited by 43 · see citing works

Astronaut omics and the impact of space on the human body at scale

Rutter, L. A., Cope, H., MacKay, M. J., Herranz, R., Das, S., Ponomarev, S. A., Costes, S. V., Paul, A. M., Barker, R. J., Taylor, D. M., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47237-0 · Cited by 38 · see citing works

Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics

Almet, A. A., Tsai, Y., Watanabe, M., & Nie, Q. H. (2024) · Nature Methods · doi:10.1038/s41592-024-02380-w · Cited by 37 · see citing works

A Roadmap for a Consensus Human Skin Cell Atlas and Single-Cell Data Standardization

Almet, A. A., Yuan, H., Annusver, K., Ramos, R., Liu, Y., Wiedemann, J., Sorkin, D. H., Xu, N., Sonkoly, E., Haniffa, M. A., et al. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.03.1679 · Cited by 32 · see citing works

Altered glia-neuron communication in Alzheimer’s Disease affects WNT, p53, and NFkB Signaling determined by snRNA-seq

Soelter, T. M., Howton, T. C., Clark, A. D., Oza, V. H., & Lasseigne, B. N. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01686-8 · Cited by 34 · see citing works

LIANA+: an all-in-one cell-cell communication frameworkPreprint

Dimitrov, D., Schäfer, P. S. L., Farr, E., Rodríguez-Mier, P., Lobentanzer, S., Dugourd, A. J. F., Tanevski, J., Flores, R. O. R., & Sáez-Rodríguez, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.19.553863 · Cited by 34 · see citing works

Learning cell communication from spatial graphs of cellsPreprint

Fischer, D. S., Schaar, A. C., & Theis, F. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.11.451750 · Cited by 32 · see citing works

LRLoop: a method to predict feedback loops in cell–cell communication

Xin, Y., Lyu, P., Jiang, J., Zhou, F., Wang, J., Blackshaw, S., & Qian, J. X. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac447 · Cited by 35 · see citing works

Single‐cell RNA sequencing in exploring the pathogenesis of diabetic retinopathy

Zhang, X. Y., Zhang, F., & Xu, X. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.1751 · Cited by 29 · see citing works

Reversible Chemistry for Cancer Therapy and Diagnosis

Xu, H., Hong, H., Kim, C., Lee, Y., Li, Y., Zhang, Y. S., Makvandi, P., Song, G., Zhang, H., Kang, H., et al. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00549 · Cited by 31 · see citing works

A systematic evaluation of the computational tools for ligand-receptor-based cell–cell interaction inference

Wang, S., Zheng, H., Choi, J. S., Lee, J. K., Li, X., & Hu, H. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac019 · Cited by 30 · see citing works

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Chu, L.-X., Wang, W.-J., Gu, X.-P., Wu, P., Gao, C., Zhang, Q., Wu, J., Jiang, D.-W., Huang, J.-Q., Ying, X.-W., et al. (2024) · Military Medical Research · doi:10.1186/s40779-024-00537-4 · Cited by 29 · see citing works

Application of Single-Cell and Spatial Omics in Musculoskeletal Disorder Research

Feng, S., Li, J., Tian, J., Lu, S., & Zhao, Y. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032271 · Cited by 29 · see citing works

Deep Learning in Single-cell Analysis

Molho, D., Ding, J., Tang, W., Li, Z., Wen, H., Wang, Y., Venegas, J., Jin, W., Liu, R., Su, R., et al. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3641284 · Cited by 32 · see citing works

Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data

Jin, S., & Ramos, R. (2022) · Biochemical Society Transactions · doi:10.1042/bst20210863 · Cited by 28 · see citing works

Immunotherapy of cancer in single-cell RNA sequencing era: A precision medicine perspective

Erfanian, N., Derakhshani, A., Nasseri, S., Fereidouni, M., Baradaran, B., Tabrizi, N. J., Brunetti, O., Bernardini, R., Silvestris, N., & Safarpour, H. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.112558 · Cited by 27 · see citing works

Crosstalk of T cells within the ovarian cancer microenvironment

Chap, B. S., Rayroux, N., Grimm, A. J., Ghisoni, E., & Laniti, D. D. (2024) · Trends in cancer · doi:10.1016/j.trecan.2024.09.001 · Cited by 27 · see citing works

The Applications of Single-Cell RNA Sequencing in Atherosclerotic Disease

Slenders, L., Tessels, D. E., van der Laan, S. W., Pasterkamp, G., & Mokrý, M. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.826103 · Cited by 26 · see citing works

CellCommuNet: an atlas of cell–cell communication networks from single-cell RNA sequencing of human and mouse tissues in normal and disease states

Ma, Q., Li, Q., Xiao, Z., & Pan, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad906 · Cited by 26 · see citing works

Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples

Baghdassarian, H., Dimitrov, D., Armingol, E., Sáez-Rodríguez, J., & Lewis, N. E. (2024) · Cell Reports Methods · doi:10.1016/j.crmeth.2024.100758 · Cited by 22 · see citing works

Spatiotemporal Omics-Refining the landscape of precision medicine

Zhang, J., Yin, J., Heng, Y., Xie, K., Chen, A., Amit, I., Bian, X.-W., & Xu, X. (2022) · Life Medicine · doi:10.1093/lifemedi/lnac053 · Cited by 27 · see citing works

Single-cell transcriptomics in tissue engineering and regenerative medicine

Ruta, A., Krishnan, K., & Elisseeff, J. H. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00132-7 · Cited by 25 · see citing works

scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

Yang, Y., Li, G., Zhong, Y., Xu, Q., Lin, Y.-T., Roman-Vicharra, C., Chapkin, R. S., & Cai, J. J. (2023) · Cell Systems · doi:10.1016/j.cels.2023.01.004 · Cited by 24 · see citing works

Deciphering intercellular signaling complexes by interaction-guided chemical proteomics

Zheng, J., Zheng, Z., Fu, C., Weng, Y., He, A., Ye, X., Gao, W., & Tian, R. (2023) · Nature Communications · doi:10.1038/s41467-023-39881-9 · Cited by 25 · see citing works

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology

Arulraj, T., Wang, H., Ippolito, A., Zhang, S., Fertig, E. J., & Popel, A. S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae131 · Cited by 20 · see citing works

Implementation and validation of single-cell genomics experiments in neuroscience

Colonna, M., Konopka, G., Liddelow, S. A., Nowakowski, T. J., Awatramani, R. B., Bateup, H. S., Cadwell, C. R., Caglayan, E., Chen, J. L., Gillis, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01814-0 · Cited by 22 · see citing works

Single-Cell RNA-Seq Analysis Reveals the Acquisition of Cancer Stem Cell Traits and Increase of Cell–Cell Signaling during EMT Progression

Bocci, F., Zhou, P., & Nie, Q. H. (2021) · Cancers · doi:10.3390/cancers13225726 · Cited by 20 · see citing works

Profiling immune cell tissue niches in the spatial -omics era

Lee, C. Y. C., McCaffrey, J., McGovern, D., & Clatworthy, M. R. (2024) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2024.11.001 · Cited by 21 · see citing works

Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets

Hao, M., Zou, X., & Jin, S. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.751158 · Cited by 18 · see citing works

Deciphering Cell-Cell Communication in Abdominal Aortic Aneurysm From Single-Cell RNA Transcriptomic Data

Yang, H. M., DeRoo, E., Zhou, T., & Liu, B. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.831789 · Cited by 17 · see citing works

Dysregulated ligand–receptor interactions from single-cell transcriptomics

Liu, Q., Hsu, C., Li, J., & Shyr, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac294 · Cited by 18 · see citing works

A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data

Xie, Z., Li, X., & Mora, A. (2023) · Biomolecules · doi:10.3390/biom13081211 · Cited by 18 · see citing works

CCPlotR: an R package for the visualization of cell–cell interactions

Ennis, S., Broin, P. Ó., & Szegezdi, É. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad130 · Cited by 17 · see citing works

New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics

Heller, G., Fuereder, T., Grandits, A. M., & Wieser, R. (2023) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.32604/or.2023.044774 · Cited by 18 · see citing works

exFINDER: identify external communication signals using single-cell transcriptomics data

He, C., Zhou, P., & Nie, Q. H. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad262 · Cited by 17 · see citing works

Resource allocation in mammalian systems

Baghdassarian, H. M., & Lewis, N. E. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108305 · Cited by 17 · see citing works

CytoSignal detects locations and dynamics of ligand–receptor signaling at cellular resolution from spatial transcriptomic data

Liu, J., Manabe, H., Qian, W., Orikasa, S., Ghaemmaghami, J., Wang, Y., Gu, Y., Chu, A. K. Y., Gadhvi, G. T., Song, Y., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02624-9 · Cited by 18 · see citing works

Deep Learning Applications in Single-Cell Omics Data AnalysisPreprint

Erfanian, N., Heydari, A. A., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Nasseri, S., Safarpour, H., & Sahebkar, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.26.470166 · Cited by 17 · see citing works

Intercellular communication in the tumour microecosystem: Mediators and therapeutic approaches for hepatocellular carcinoma

Wang, W., Jin, W., & Li, X. (2022) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2022.166528 · Cited by 16 · see citing works

Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Li, R., Chen, X., & Yang, X. (2024) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1839 · Cited by 15 · see citing works

Predicting cell–cell communication by combining heterogeneous ensemble deep learning and weighted geometric mean

Peng, L., Liu, L., Huang, L., Bai, Z., Chen, M., & Chen, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112839 · Cited by 16 · see citing works

Nanotechnology Meets the Tumor Microenvironment: Unlocking New Horizons in Cancer Therapy

Yadav, P., Yadav, A. K., & Bhatia, D. D. (2025) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.5c01120 · Cited by 16 · see citing works

CITEdb: a manually curated database of cell–cell interactions in human

Shan, N., Lü, Y., Guo, H., Li, D., Jiang, J., Yan, L., Gao, J., Ren, Y., Zhao, X., & Hou, L. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac654 · Cited by 15 · see citing works

Single-cell pair-wise relationships untangled by composite embedding model

Subedi, S., & Park, Y. (2023) · iScience · doi:10.1016/j.isci.2023.106025 · Cited by 14 · see citing works

scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies

Baran, Y., & Doğan, B. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106634 · Cited by 14 · see citing works

A CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics

Pandey, A. C., Bezney, J., DeAscanis, D., Kirsch, E., Ahmed, F., Crinklaw, A., Choudhary, K. S., Mandala, T., Deason, J., Hamidi, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59880-2 · Cited by 13 · see citing works

EasyCellType: marker-based cell-type annotation by automatically querying multiple databases

Li, R., Zhang, J. J., & Li, Z. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad029 · Cited by 12 · see citing works

Computational cell–cell interaction technologies drive mechanistic and biomarker discovery in the tumor microenvironment

Pong, A., Mah, C. K., Yeo, G. W.-M., & Lewis, N. E. (2023) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2023.103048 · Cited by 13 · see citing works

Dissecting intercellular and intracellular signaling networks with barcoded genetic tools

Herholt, A., Sahoo, V. K., Popović, L., Wehr, M. C., & Rossner, M. J. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.09.002 · Cited by 13 · see citing works

PEG-fibrinogen hydrogel microspheres as a scaffold for therapeutic delivery of immune cells

Cohen, N., Vagima, Y., Mouhadeb, O., Toister, E., Gutman, H., Lazar, S., Jayson, A., Artzy‐Schnirman, A., Sznitman, J., Ordentlich, A., et al. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.905557 · Cited by 13 · see citing works

Technical Advances and Applications of Spatial Transcriptomics

Liang, G., Yin, H. Z., & Ding, F. (2023) · GEN Biotechnology · doi:10.1089/genbio.2023.0032 · Cited by 12 · see citing works

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

Nie, W., Yu, Y., Wang, X., Wang, R., & Li, S. C. (2024) · Advanced Science · doi:10.1002/advs.202403572 · Cited by 12 · see citing works

Deciphering cell-fate trajectories using spatiotemporal single-cell transcriptomic data

Zhang, Z., Wang, Z., Sun, Y., Shen, J., Peng, Q., Li, T., & Zhou, P. (2025) · npj Systems Biology and Applications · doi:10.1038/s41540-025-00624-9 · Cited by 12 · see citing works

Deciphering colorectal cancer progression features and prognostic signature by single-cell RNA sequencing pseudotime trajectory analysis

Bazyari, M. J., Saadat, Z., Firouzjaei, A. A., & Aghaee‐Bakhtiari, S. H. (2023) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2023.101491 · Cited by 12 · see citing works

scHyper: reconstructing cell–cell communication through hypergraph neural networks

Li, W., Wang, H., Zhao, J., Xia, J., & Sun, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae436 · Cited by 11 · see citing works

Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data

Hou, J., Zhao, W., & Nie, Q. H. (2025) · Nature Communications · doi:10.1038/s41467-025-61149-7 · Cited by 12 · see citing works

Charting spatial ligand-target activity using Renoir

Rao, N., Kumar, T., Kazemi, D., Hou, S., Khakpoor, A., Eapen, M. M., Pai, R., Qiao, L., Mishra, A., Ginhoux, F., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-72388-7 · Cited by 11 · see citing works

Modeling and inference of spatial intercellular communications and multilayer signaling regulations using stMLnetPreprint

Cheng, J., Yan, L., Nie, Q. H., & Sun, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.27.497696 · Cited by 11 · see citing works

SpatialDM: Rapid identification of spatially co-expressed ligand-receptor reveals cell-cell communication patternsPreprint

Zhuoxuan, L. I., Wang, T., Liu, P., & Huang, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.19.504616 · Cited by 11 · see citing works

Intercellular Tunneling Nanotubes as Natural Biophotonic Conveyors

Gong, Z., Wu, T., Zhao, Y., Guo, J., Zhang, Y., Li, B., & Li, Y. (2024) · ACS Nano · doi:10.1021/acsnano.4c12681 · Cited by 10 · see citing works

SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics

Bai, Y., Guo, X., Liu, K., Zheng, B., Wei, Y., Wang, Y., Zhang, W. H., Luo, Q., Yin, J., Wu, L. L., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03697-1 · Cited by 9 · see citing works

Single-Cell RNA Sequencing Analysis of Gene Regulatory Network Changes in the Development of Lung Adenocarcinoma

Yu, D., Zhang, S., Liu, Z., Xu, L., Chen, L., & Xie, L. (2023) · Biomolecules · doi:10.3390/biom13040671 · Cited by 9 · see citing works

The genomic physics of tumor–microenvironment crosstalk

Sang, M., Li, F., Dong, A., Gragnoli, C., Griffin, C. H., & Wu, R. (2023) · Physics Reports · doi:10.1016/j.physrep.2023.07.006 · Cited by 9 · see citing works

Identification of a novel disulfidptosis-related gene signature in osteoarthritis using bioinformatics analysis and experimental validation

Wei, M., Shi, X., Tang, W., Lv, Q., Wu, Y., & Xu, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-85569-z · Cited by 9 · see citing works

Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis

Zhang, Z., Sun, Y., Peng, Q., Li, T., & Zhou, P. (2025) · Entropy · doi:10.3390/e27050453 · Cited by 12 · see citing works

Mining cell–cell signaling in single-cell transcriptomics atlases

Deng, M., Wang, Y., & Yan, Y. (2022) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2022.102101 · Cited by 10 · see citing works

An individualized causal framework for learning intercellular communication networks that define microenvironments of individual tumors

Chen, X., Chen, L., Kürten, C. H. L., Jabbari, F., Vujanovic, L. N., Ding, Y., Lu, B., Lu, K., Kulkarni, A., Tabib, T., et al. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010761 · Cited by 9 · see citing works

Detecting global and local hierarchical structures in cell-cell communication using CrossChat

Wang, X., Almet, A. A., & Nie, Q. H. (2024) · Nature Communications · doi:10.1038/s41467-024-54821-x · Cited by 8 · see citing works

SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell–cell communication

Liu, J., Ma, L., Ju, F., Zhao, C., & Yu, L. (2025) · BMC Biology · doi:10.1186/s12915-025-02141-x · Cited by 8 · see citing works

New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data

Shao, X., Lu, X., Liao, J., Chen, H., & Fan, X. (2020) · Protein & Cell · doi:10.1007/s13238-020-00727-5 · Cited by 162 · see citing works

CellChat for systematic analysis of cell–cell communication from single-cell transcriptomics

Jin, S., Plikus, M. V., & Nie, Q. (2025) · Nature Protocols · doi:10.1038/s41596-024-01045-4 · Cited by 1,448 · see citing works

Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data

Jin, S., & Ramos, R. (2022) · Biochemical Society Transactions · doi:10.1042/bst20210863 · Cited by 28 · see citing works

Cell-Cell Communication Inference and Analysis: Biological Mechanisms, Computational Approaches, and Future Opportunities

Cheng, X., Huang, H., Su, Y., Nie, Q. H., Zou, X., & Jin, S. (2026) · CSIAM transactions on life sciences. · doi:10.4208/csiam-ls.so-2025-0022 · Cited by 1 · see citing works

Inference of Ligand–Receptor Pairs from Single-Cell Transcriptomics Data

Efremova, M., & Vento‐Tormo, R. (2021) · Methods in molecular biology · doi:10.1007/7651_2020_343 · Cited by 2 · see citing works