The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Spatial reconstruction of single-cell gene expression data
Open accessAbstract
Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
Overview
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
Cites
Statistics
Verification
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?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Among other cell type-specific local eQTLs are CD52, a gene ubiquitously expressed in leukocytes that only has eQTLs in monocyte populations, and DIP2A, a gene with an eQTL only in NK cells that is associated with immune response to vaccination in peripheral blood35.”
From Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2018) · cites this paper as [35]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Batch effects were corrected with the FindIntegrationAnchor() and the IntegrateData() function in the reciprocal PCA approach of the Seurat workflow.”
From TBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration (2023) · cites this paper as [76]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Critically, such methods can only detect variations that are captured by differences between discrete groups.”
From SpatialDE: identification of spatially variable genes (2018) · cites this paper as [3]
Experimental — accuracy not yet measuredIn the citing paper
“These factors may not only be the discrete categories that are often assumed when classifying cells into major types4,30,94 but may also represent a continuous spectrum1, or a combination of discrete and continuous categories36,95–97,227.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [95]
In the citing paper
“even when a cell population partitions into discrete types, hybrid cells—single cells that are a mix of two or more types—may be observed95,97”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [95]
In the citing paper
“One approach used existing in situ hybridisation maps of spatially restricted genes as a “barcode” to which the complete expression profiles of individual cells can be matched.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Satija et al, 2015
In the citing paper
“This was applied by two groups to reconstruct expression patterns in zebrafish embryos (Satija et al, 2015), and to the brain of the marine annelid Platynereis dumerilii (Achim et al, 2015).”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Satija et al, 2015
In the citing paper
“Notably, the down-regulation of ribosomal genes and bulk protein synthesis has been long considered as a hallmark of aging 54,55 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [54]
In the citing paper
“Thus, while there may be hallmarks of aging that occur in most cell types, such as mitochondrial dysfunction and loss of proteostasis 1,4,54 , our data argue against the hypothesis that aging induces a single universal molecular program in all cells and tissues 16 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [54]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · cited 1×
“Specifically, Seurat uses the expression levels of all highly variable genes in the RNA-seq data set and an L1-constrained, LASSO (least absolute shrinkage and selection operator 23 ) technique to construct separate models of gene expression for each of the landmark genes.”
MethodStages of embryonic development of the zebrafish
Kimmel, C. B., Ballard, W. W., Kimmel, S. R., Ullmann, B., & Schilling, T. F. (1995) · Developmental Dynamics · doi:10.1002/aja.1002030302 · Cited by 12,037 · cited 1×
“Finally, we observed a population of 94 cells expressing high levels of canonical markers of the enveloping layer (e.g., krt18 , krt4 , cldne ), a single layer of differentiated squamous cells that cover the outside of the embryo 30,31 .”
BackgroundTrapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609 · cited 1×
“” Nature biotechnology 33 (5): 495-502.”
The developmental transcriptome of Drosophila melanogaster
Graveley, B. R., Brooks, A. N., Carlson, J. W., Duff, M. O., Landolin, J. M., Yang, L. C., Artieri, C. G., van Baren, M. J., Boley, N. P., Booth, B. W., et al. (2010) · Nature · doi:10.1038/nature09715 · Cited by 1,504 · cited 8×
“Seurat consists of the following steps: (1) It uses co-expression patterns across cells in the single-cell RNA-seq profiles to impute the expression of each landmark gene in each cell.”
BackgroundMethodInfluentialmixtools : An R Package for Analyzing Finite Mixture Models
Benaglia, T., Chauveau, D., Hunter, D. R., & Young, D. S. (2009) · Journal of Statistical Software · doi:10.18637/jss.v032.i06 · Cited by 1,391
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · cited 3×
“Single-cell measurements for a single gene, however, are inherently noisy, with extensive variability stemming from both biological and technical sources 21,22 .”
BackgroundSingle-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345 · cited 1×
“Single-cell RNA-seq is quickly becoming an established experimental method, with ongoing cost and throughput improvements enabling applications from cell type discovery 6,7 to regulatory network inference 8,9 to the reconstruction of developmental processes 10–12 .”
Method
Flagged first, then by citations
Cited by
Sorted by citations
Items 1,001–1,061 of 1,061
Perspectives on defining cell types in the brain
Mukamel, E. A., & Ngai, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.11.007 · Cited by 56
“Methods that can provide spatial context for transcriptomic data via computational analysis [70] and in situ sequencing [71] can help connect molecular and anatomical information.”
BackgroundTrellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses
Ramos Zapatero, M., Tong, A., Opzoomer, J. W., O’Sullivan, R., Cardoso Rodriguez, F., Sufi, J., Vlckova, P., Nattress, C., Qin, X., Claus, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.005 · Cited by 72
Vincenti, I., Page, N., Steinbach, K., Yermanos, A., Lemeille, S., Nunez, N., Kreutzfeldt, M., Klimek, B., Di Liberto, G., Egervari, K., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abl6058 · Cited by 70
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63
FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data
DeTomaso, D., & Yosef, N. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1176-5 · Cited by 58
Deconstructing Stepwise Fate Conversion of Human Fibroblasts to Neurons by MicroRNAs
Cates, K., McCoy, M. J., Kwon, J.-S., Liu, Y., Abernathy, D. G., Zhang, B., Liu, S., Gontarz, P., Kim, W. K., Chen, S., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2020.08.015 · Cited by 67
Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases
Yasumizu, Y., Takeuchi, D., Morimoto, R., Takeshima, Y., Okuno, T., Kinoshita, M., Morita, T., Kato, Y., Wang, M., Motooka, D., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2023.100473 · Cited by 60
Zhang, H., Ma, Y., Cheng, X., Wu, D., Huang, X., Chen, B., Ren, Y., Jiang, W., Tang, X., Bai, T., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abd1206 · Cited by 65
Capano, L. S., Sato, C., Ficulle, E., Yu, A., Horie, K., Kwon, J.-S., Burbach, K. F., Barthélemy, N. R., Fox, S. G., Karch, C. M., et al. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.04.018 · Cited by 65
Mitochondrial origins of the pressure to sleep
Sarnataro, R., Velasco, C. D., Monaco, N., Kempf, A., & Miesenböck, G. (2025) · Nature · doi:10.1038/s41586-025-09261-y · Cited by 62
“Raw transcriptomic data were pre-processed with a custom command line script 30,99,100 , which extracted cell barcodes and aligned associated reads to a combination of the Drosophila melanogaster genome release BDGP6.”
Evaluating the Utilities of Foundation Models in Single-cell Data Analysis
Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2023) · doi:10.1101/2023.09.08.555192 · Cited by 62
Li, J., Wang, J., Zhang, P., Wang, R., Mei, Y., Sun, Z., Fei, L., Jiang, M., Ma, L., E, W., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01197-7 · Cited by 57
Karahan, H., Smith, D. C., Kim, B., Dabin, L. C., Al-Amin, M. M., Wijeratne, H. R. S., Pennington, T., Viana di Prisco, G., McCord, B., Lin, P. B.-C., et al. (2021) · Science Advances · doi:10.1126/sciadv.abe3954 · Cited by 53
Chen, Y., Wang, T., Liang, F., Han, J., Lou, Z., Yu, Y., Li, J., Zhan, T., Gu, Y., Dong, L., et al. (2024) · Theranostics · doi:10.7150/thno.94482 · Cited by 60
“The analysis was performed using the R package Seurat (version 4.3.0) [29-32].”
MethodInfluentialLopez, 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
Atherosclerosis in the single-cell era
Winkels, H., Ehinger, E., Ghosheh, Y., Wolf, D., & Ley, K. (2018) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000537 · Cited by 54
Functional identity of hypothalamic melanocortin neurons depends on Tbx3
Quarta, C., Fisette, A., Xu, Y., Colldén, G., Legutko, B., Tseng, Y.-T., Reim, A., Wierer, M., De Rosa, M. C., Klaus, V., et al. (2019) · Nature Metabolism · doi:10.1038/s42255-018-0028-1 · Cited by 54
Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2015) · doi:10.1101/025528 · Cited by 625
A human brain vascular atlas reveals diverse cell mediators of Alzheimer’s disease risk
Yang, A. C., Vest, R. T., Kern, F., Lee, D. P., Maat, C. A., Losada, P. M., Chen, M. B., Agam, M., Schaum, N., Khoury, N., et al. (2021) · doi:10.1101/2021.04.26.441262 · Cited by 48
Deep learning in spatial transcriptomics: Learning from the next next-generation sequencing
Heydari, A. A., & Sindi, S. S. (2023) · Biophysics Reviews · doi:10.1063/5.0091135 · Cited by 44
Peng, G., Cui, G., Ke, J., & Jing, N. (2020) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-120219-083220 · Cited by 42
Spatiotemporal transcriptomics elucidates the pathogenesis of fulminant viral myocarditis
Li, H., Chen, X., Wang, J. J., Shen, J., Abuduwufuer, K., Zhang, Z., Dong, Z., Wen, Z., He, J., Chen, S., et al. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02143-9 · Cited by 40
Heimberg, G., Kuo, T., DePianto, D., Heigl, T., Diamant, N., Salem, O., Scalia, G., Biancalani, T., Turley, S., Rock, J., et al. (2023) · doi:10.1101/2023.07.18.549537 · Cited by 40
Self-citationGroh, J., Abdelwahab, T., Kattimani, Y., Hörner, M., Loserth, S., Gudi, V., Adalbert, R., Imdahl, F., Saliba, A.-E., Coleman, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42570-2 · Cited by 39
Littman, R., Cheng, M., Wang, N., Peng, C., & Yang, X. (2023) · iScience · doi:10.1016/j.isci.2023.107124 · Cited by 35
“To reduce computational cost and enable network building for all approaches, we first took the top 3,000 highly variable genes using the variance stabilizing transform method, 83 including the differentially perturbed genes.”
MethodZhou, Y., Luo, K., Liang, L., Chen, M., & He, X. (2023) · Nature Methods · doi:10.1038/s41592-023-02017-4 · Cited by 34
Nuclear envelope disruption triggers hallmarks of aging in lung alveolar macrophages
De Silva, N. S., Siewiera, J., Alkhoury, C., Nader, G. P. F., Nadalin, F., de Azevedo, K., Couty, M., Izquierdo, H. M., Bhargava, A., Conrad, C., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00488-w · Cited by 34
Encoding Method of Single-cell Spatial Transcriptomics Sequencing
Zhou, Y., Jia, E., Pan, M., Zhao, X., & Ge, Q. (2020) · International Journal of Biological Sciences · doi:10.7150/ijbs.43887 · Cited by 32
“This method can also be combined with the scRNA-seq of cells isolated from adjacent tissue sections [17, 18].”
MethodMitra, A., Andrews, M. C., Roh, W., De Macedo, M. P., Hudgens, C. W., Carapeto, F., Singh, S., Reuben, A., Wang, F., Mao, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15538-9 · Cited by 27
Single-Cell RNA Sequencing for Studying Human Cancers
Aran, D. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-091857 · Cited by 31
XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples
Cheloni, S., Hillje, R., Luzi, L., Pelicci, P. G., & Gatti, E. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-00872-8 · Cited by 28
Thurman, A. L., Ratcliff, J. A., Chimenti, M. S., & Pezzulo, A. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab337 · Cited by 27
Single-cell multi-omics map of human fetal blood in Down syndrome
Marderstein, A. R., De Zuani, M., Moeller, R., Bezney, J., Padhi, E. M., Wong, S., Coorens, T. H. H., Xie, Y., Xue, H., Montgomery, S. B., et al. (2024) · Nature · doi:10.1038/s41586-024-07946-4 · Cited by 26
“0.3) implementation 56 .”
MethodInfluentialBalakumar, A., Das, D., Datta, A., Mishra, A., Bryak, G., Ganesh, S. M., Netea, M. G., Kumar, V., Lionakis, M. S., Arora, D., et al. (2024) · PLOS Pathogens · doi:10.1371/journal.ppat.1012699 · Cited by 25
Hachey, S. J., Hatch, C. J., Gaebler, D., Mocherla, A., Nee, K., Kessenbrock, K., & Hughes, C. C. W. (2024) · Breast Cancer Research · doi:10.1186/s13058-023-01760-y · Cited by 24
“Count matrices were loaded into RStudio (1.2.5042 version, R version 4.0.4 [61, 62]) and converted into a Seurat object using the Seurat R Package (version 4.0.0.9015) [63, 64].”
MethodInfluentialPeng, L., Renauer, P. A., Ökten, A., Fang, Z., Park, J. J., Zhou, X., Lin, Q., Dong, M. B., Filler, R., Xiong, Q., et al. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2022.100634 · Cited by 21
“47 Specifically, each dataset was filtered (cells with 200-2500 RNA features and 5% mitochondrial RNA), log-normalized, scaled then integrated using the method by Stuart et al., using PBS vaccination samples as the reference group (reciprocal-PCA method, 2000 anchors, k = 20).”
Chakraborty, S., Wenzlitschke, N., Anderson, M. J., Eraso, A., Baudic, M., Thompson, J. J., Evans, A. A., Shatford-Adams, L. M., Chari, R., Awasthi, P., et al. (2025) · Developmental Cell · doi:10.1016/j.devcel.2025.02.002 · Cited by 22
Stern, E., Ruf-Zamojski, F., Zalepa-King, L., Pincas, H., Choi, S. G., Peskin, C. S., Hayot, F., Turgeon, J. L., & Sealfon, S. C. (2017) · Journal of Biological Chemistry · doi:10.1074/jbc.m117.783886 · Cited by 21
Sinusoidal communication in chronic liver disease
Gibert-Ramos, A., Andrés-Rozas, M., Pastó, R., Alfaro-Retamero, P., Guixé-Muntet, S., & Gracia-Sancho, J. (2025) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2024.0734 · Cited by 20
“Briefly, the scSeq RNA data was processed as described in the original paper using the Seurat package for R(167).”
MethodNadeem, O., Aranha, M. P., Redd, R., Timonian, M., Magidson, S., Lightbody, E. D., Alberge, J.-B., Bertamini, L., Dutta, A. K., El-Khoury, H., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55308-5 · Cited by 20
Rare genetic variants confer a high risk of ADHD and implicate neuronal biology
Demontis, D., Duan, J., Hsu, Y.-H. H., Pintacuda, G., Grove, J., Nielsen, T. T., Thirstrup, J., Martorana, M., Botts, T., Satterstrom, F. K., et al. (2026) · Nature · doi:10.1038/s41586-025-09702-8 · Cited by 17
Helios represses megakaryocyte priming in hematopoietic stem and progenitor cells
Cova, G., Taroni, C., Deau, M.-C., Cai, Q., Mittelheisser, V., Philipps, M., Jung, M., Cerciat, M., Le Gras, S., Thibault-Carpentier, C., et al. (2021) · Journal of Experimental Medicine · doi:10.1084/jem.20202317 · Cited by 17
“Pathway analysis of PC1 and PC2 genes (30 top positive and 30 top negative genes) was performed at the Metascape website (http://metascape.org; Satija et al., 2015).”
MethodIntercellular signaling dynamics from a single cell atlas of the biomaterials response
Cherry, C., Maestas, D. R., Han, J., Andorko, J. I., Cahan, P., Fertig, E. J., Garmire, L. X., & Elisseeff, J. H. (2020) · doi:10.1101/2020.07.24.218537
SEQUIN is an R/Shiny framework for rapid and reproducible analysis of RNA-seq data.
Weber, C., Hirst, M. B., Ernest, B., Schaub, N. J., Wilson, K. M., Wang, K., Baskir, H. M., Chu, P.-H., Tristan, C. A., & Singeç, I. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100420 · Cited by 11
Hawkins, D. Y., Zuch, D. T., Huth, J., Rodriguez-Sastre, N., McCutcheon, K. R., Glick, A., Lion, A. T., Thomas, C. F., Descoteaux, A. E., Johnson, W. E., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad278 · Cited by 10
“We compared ICAT against two state-of-the-art integration methods: Seurat (Stuart and Satija, 2019; Satija et al. , 2015) and Scanorama (Hie et al. , 2019).”
MethodTian, C., Ao, Z., Cerneckis, J., Cai, H., Chen, L., Niu, H., Takayama, K., Kim, J., Shi, Y., Gu, M., et al. (2025) · Science Advances · doi:10.1126/sciadv.adu2708 · Cited by 9
Comparison of SureSelect and Nextera Exome Capture Performance in Single-Cell Sequencing
Huss, W. J., Hu, Q., Glenn, S. T., Gangavarapu, K. J., Wang, J., Luce, J. D., Quinn, P. K., Brese, E. A., Zhan, F., Conroy, J. M., et al. (2018) · Human Heredity · doi:10.1159/000490506 · Cited by 8
Endothelial Pim3 kinase protects the vascular barrier during lung metastasis
Santio, N. M., Ganesh, K., Kaipainen, P. P., Halme, A., Seyednasrollah, F., Arbash, E., Hänninen, S., Kivelä, R., Carpen, O., & Saharinen, P. (2024) · Nature Communications · doi:10.1038/s41467-024-54445-1 · Cited by 9
Improved ClickTags enable live-cell barcoding for highly multiplexed single cell sequencing
Zhao, X., Sun, S., Yu, W., Zhu, W., Zhao, Z., Zhou, Y., Ding, X., Fang, N., Yang, R., & Li, J. P. (2022) · RSC Chemical Biology · doi:10.1039/d2cb00046f · Cited by 8
Ham, M., Cho, Y., Kang, T., Oh, T., Kim, H., & Kim, K. (2024) · Aging Cell · doi:10.1111/acel.14049 · Cited by 8
“…was divided by the average TPM value of the entire tissue A previously published snRNA-Seq dataset (ArrayExpress: E- MAB-8564) was analyzed using Seurat package (Satija et al., 2015) in R. Cells that had more than 0.5% expression on mitochondrial genes and fewer than 150 features were removed.”
MethodInfluentialGrubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2019) · doi:10.1101/639054 · Cited by 7
Araya, P., Niemeyer, B., Schade, K., Dunn, L. N., Waugh, K., Busquet, N., Brindley, C., Brown, C., Winkler, C., Lyford, H. R., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113130 · Cited by 7
Huang, X., Liu, Y., Wang, Q., Rehman, H. M., Horváth, D., Zhou, S., Fu, R., Zhang, L., Szöllősi, A. G., & Li, Z. (2023) · BMC Oral Health · doi:10.1186/s12903-023-03181-x · Cited by 6
“For single-cell analysis, we utilized the R package "Seu-rat" (version 3.0.2) to generate the object and exclude samples with poor quality [10–13].”
InfluentialRetractedTBX3 is dynamically expressed in pancreatic organogenesis and fine-tunes regeneration
Melzer, M. K., Schirge, S., Gout, J., Arnold, F., Srinivasan, D., Burtscher, I., Allgöwer, C., Mulaw, M., Zengerling, F., Günes, C., et al. (2023) · BMC Biology · doi:10.1186/s12915-023-01553-x · Cited by 5
MCGAE: unraveling tumor invasion through integrated multimodal spatial transcriptomics
Yang, Y., Zhang, C., Liu, Z., Aihara, K., Zhang, C., Chen, L., & Wei, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae608 · Cited by 5
“Traditional non-spatial algorithms like Seurat [13] use linear dimensionality reduction for clustering based on expression data alone.”
Zinati, Y., Takiddeen, A., & Emad, A. (2023) · doi:10.1101/2023.07.25.550225 · Cited by 36
Type 1 interferon perturbates clonal competition by reshaping human blood development
Lama, C., Isakov, D., Rosenberg, S., Quijada-Álamo, M., Saurty-Seerunghen, M. S., Moein, S., Nozais, M., Abera, T.-A., Sakaguchi, O., Totwani, M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02751-3 · Cited by 3
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
Order Matters: Ambient RNA correction confounds doublet detection in single-cell RNA sequencing
Meier, M., Howitt, G., Grima, C. V., Dixit, G., King, H., Gubbels, L., Levi, E., Davies, K., Shanthikumar, S., Neeland, M. R., et al. (2026) · doi:10.64898/2026.10.13.758369
“We employ both SingleCellExperiment (version 1.30.1) [53] and Seurat (version 5.3.1) [54] throughout this study.”
InfluentialNordbeck, A. J., Martá‐Ariza, M., Ek Olofsson, H., Kanshin, E., Ueberheide, B., Jones, K., Sanford, B., Hamlett, E. D., Head, E., Mufson, E. J., et al. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71776
(2016) · PubMed · Cited by 91
Metadata
- Authors
- Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Corresponding author
- Schier, Alexander F
- Regev, Aviv
- Venue
- Nature Biotechnology
- Published
- 2015-04-13
- Volume
- 33
- Issue
- 5
- Pages
- 495-502
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4430369
- OpenAlex
- W2069089843
- Cited by
- 7,055
- References
- 50
- Citations by year
- 2026: 931 · 2025: 1220 · 2024: 1142 · 2023: 987 · 2022: 784 · 2021: 640 · 2020: 510 · 2019: 341 · 2018: 278 · 2017: 126 · 2016: 64 · 2015: 27
- FWCI
- 71.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- edoc (University of Basel) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- Jane Coffin Childs Memorial Fund for Medical Research
- National Institutes of Health
- Klarman Cell Observatory, Broad Institute
- National Institute of Mental Health (U01MH105960)
- National Human Genome Research Institute (P50HG006193)
- National Institute of General Medical Sciences (R01GM056211)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (F32HD075541)
- 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 expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- spatial reconstruction, single-cell RNA sequencing, subcellular localization, spatial transcriptomics, zebrafish embryos, spatial patterning, spatial markers, Seurat
- MeSH terms
- Animals, Image Processing, Computer-Assisted, Zebrafish, In Situ Hybridization, Fluorescence, Gene Expression Regulation, Developmental, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, 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
