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Automated methods for cell type annotation on scRNA-seq data
Open accessAbstract
The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.
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“Annotation results obtained with pre-trained classifiers are strongly affected by the classifier type and the quality of the training data used to create the classifier54,55.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [55]
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Cites
(2008) · Journal of Machine Learning Research · Cited by 33,051
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358
Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0276-y · Cited by 4,984
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
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 2,842 · cited 2×
“In tests using the Tabula Muris Atlas as a training dataset [47], ACTINN was highly accurate in classifying strictly related cellular subtypes, and was robust against batch effects arising from the use of different sequencing techniques.”
MethodMolecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
Conserved cell types with divergent features in human versus mouse cortex
Hodge, R. D., Bakken, T. E., Miller, J. A., Smith, K. A., Barkan, E. R., Graybuck, L. T., Close, J. L., Long, B., Johansen, N., Penn, O., et al. (2019) · Nature · doi:10.1038/s41586-019-1506-7 · Cited by 1,775 · cited 1×
“One task likely to be significantly more challenging is the identification of cell subtypes, for example within the neuronal classes, as only few genes may be crucial for their discrimination [57].”
BackgroundMapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597
Machine learning: a review of classification and combining techniques
Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9052-3 · Cited by 1,582
PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data
Franzén, O., Gan, L.-M., & Björkegren, J. L. M. (2019) · Database · doi:10.1093/database/baz046 · Cited by 1,551
CellMarker: a manually curated resource of cell markers in human and mouse
Zhang, X., Lan, Y., Xu, J., Quan, F., Zhao, E., Deng, C., Luo, T., Xu, L., Liao, G., Yan, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky900 · Cited by 1,524
The Technology and Biology of Single-Cell RNA Sequencing
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., & al, E. (2017) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162597 · Cited by 1,457
Challenges in unsupervised clustering of single-cell RNA-seq data
Kiselev, V. Y., Andrews, T. S., & Hemberg, M. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0088-9 · Cited by 1,205
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Murthy, S. K. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009744630224 · Cited by 1,085
BioNumbers—the database of key numbers in molecular and cell biology
Milo, R., Jorgensen, P., Moran, U., Weber, G., & Springer, M. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkp889 · Cited by 1,055
CancerSEA: a cancer single-cell state atlas
Yuan, H., Yan, M., Zhang, G., Liu, W., Deng, C., Liao, G., Xu, L., Luo, T., Yan, H., Long, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky939 · Cited by 1,048
Harmancı, A. S., Youngblood, M. W., Clark, V. E., Coşkun, S., Henegariu, O., Durán, D., Erson-Omay, Z., Kaulen, L. D., Lee, T. I., Abraham, B. J., et al. (2018) · Nature Communications · doi:10.1038/ncomms16215 · Cited by 907
Multiplexed quantification of proteins and transcripts in single cells
Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832
High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
Chen, S., Lake, B. B., & Zhang, K. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0290-0 · Cited by 810
G&T-seq: parallel sequencing of single-cell genomes and transcriptomes
Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750
scmap: projection of single-cell RNA-seq data across data sets
(2018) · doi:10.1038/nmeth.4644 · Cited by 735
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209620 · Cited by 691 · cited 3×
“scANVI [43] Capybara U U [44] scID U U [45] scNym U U U U [46] G.”
BackgroundMethodClark, S. J., Argelaguet, R., Kapourani, C.-A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03149-4 · Cited by 657
Cell Types of the Human Retina and Its Organoids at Single-Cell Resolution
Cowan, C. S., Renner, M., De Gennaro, M., Gross-Scherf, B., Goldblum, D., Hou, Y., Munz, M., Rodrigues, T. M., Krol, J., Szikra, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.013 · Cited by 654
A comparison of automatic cell identification methods for single-cell RNA sequencing data
Abdelaal, T., Michielsen, L., Cats, D., Hoogduin, D., Mei, H., Reinders, M. J. T., & Mahfouz, A. (2019) · Genome Biology · doi:10.1186/s13059-019-1795-z · Cited by 628
Wainberg, M., Merico, D., Delong, A., & Frey, B. J. (2018) · Nature Biotechnology · doi:10.1038/nbt.4233 · Cited by 596
CellNet: Network Biology Applied to Stem Cell Engineering
Cahan, P., Li, H., Morris, S. A., da Rocha, E. L., Daley, G. Q., & Collins, J. J. (2014) · Cell · doi:10.1016/j.cell.2014.07.020 · Cited by 535
Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
Mereu, E., Lafzi, A., Moutinho, C., Ziegenhain, C., McCarthy, D. J., Álvarez-Varela, A., Batlle, E., Sagar, Grün, D., Lau, J. K., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0469-4 · Cited by 499
Supervised classification enables rapid annotation of cell atlases
Pliner, H. A., Shendure, J., & Trapnell, C. (2019) · doi:10.1101/538652 · Cited by 498
An expression atlas of human primary cells: inference of gene function from coexpression networks
Mabbott, N. A., Baillie, J. K., Brown, H. K., Freeman, T. C., & Hume, D. A. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-632 · Cited by 494
The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery
Stunnenberg, H. G., Abrignani, S., Adams, D. J., de Almeida, M., Altucci, L., Amin, V. R., Amit, I., Antonarakis, S. E., Aparicio, S. A. J. R., Arima, T., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.007 · Cited by 485
scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data
Alquicira-Hernández, J., Sathe, A., Ji, H. P., Nguyen, Q. H., & Powell, J. E. (2019) · Genome biology · doi:10.1186/s13059-019-1862-5 · Cited by 474
Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq
Treutlein, B., Lee, Q. Y., Camp, J. G., Mall, M., Koh, W. L. C., Shariati, S. A., Sim, S., Neff, N. F., Skotheim, J. M., Wernig, M., et al. (2016) · Nature · doi:10.1038/nature18323 · Cited by 442
Single-cell mapping of lineage and identity in direct reprogramming
Biddy, B. A., Kong, W., Kamimoto, K., Guo, C., Waye, S. E., Sun, T., & Morris, S. A. (2018) · Nature · doi:10.1038/s41586-018-0744-4 · Cited by 390
Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
Zhang, A. W., O’Flanagan, C. H., Chavez, E. A., Lim, J. L., Ceglia, N., McPherson, A., Wiens, M., Walters, P., Chan, T., Hewitson, B., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0529-1 · Cited by 385
Tan, Y., & Cahan, P. (2019) · Cell Systems · doi:10.1016/j.cels.2019.06.004 · Cited by 368
An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
Zhu, C., Yu, M., Huang, H., Juric, I., Abnousi, A., Hu, R., Lucero, J., Behrens, M. M., Hu, M., & Ren, B. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0323-x · Cited by 344
Tissue Stem Cells: Architects of Their Niches
Fuchs, E., & Blau, H. M. (2020) · Cell Stem Cell · doi:10.1016/j.stem.2020.09.011 · Cited by 332
Holland, C. H., Tanevski, J., Perales-Patón, J., Gleixner, J., Kumar, M. P., Mereu, E., Joughin, B. A., Stegle, O., Lauffenburger, D. A., Heyn, H., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1949-z · Cited by 323
The promise of single-cell sequencing
Eberwine, J. H., Sul, J., Bartfai, T., & Kim, J. (2013) · Nature Methods · doi:10.1038/nmeth.2769 · Cited by 305
SBML Level 3: an extensible format for the exchange and reuse of biological models
Keating, S., Waltemath, D., König, M., Zhang, F., Dräger, A., Chaouiya, C., Bergmann, F., Finney, A., Gillespie, C. S., Helikar, T., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199110 · Cited by 301
M3Drop: dropout-based feature selection for scRNASeq
Andrews, T. S., & Hemberg, M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1044 · Cited by 298
scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data
Shao, X., Liao, J., Lu, X., Xue, R., Ai, N., & Fan, X. (2020) · iScience · doi:10.1016/j.isci.2020.100882 · Cited by 286
SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples
Zhang, Z., Luo, D., Zhong, X. Y., Choi, J. H., Ma, Y., Wang, S., Mahrt, E., Guo, W., Stawiski, E. W., Modrusan, Z. D., et al. (2019) · Genes · doi:10.3390/genes10070531 · Cited by 285
CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing
de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T., & Holstege, F. C. P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz543 · Cited by 283
ACTINN: automated identification of cell types in single cell RNA sequencing
Ma, F., & Pellegrini, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz592 · Cited by 252
How transcription factors drive choice of the T cell fate
Hosokawa, H., & Rothenberg, E. V. (2020) · Nature reviews. Immunology · doi:10.1038/s41577-020-00426-6 · Cited by 250
Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity
Liu, L., Liu, C., Quintero, A., Wu, L., Yuan, Y., Wang, M., Cheng, M., Leng, L., Xu, L., Dong, G., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08205-7 · Cited by 243
A comprehensive library of human transcription factors for cell fate engineering
Ng, A. H. M., Khoshakhlagh, P., Arias, J. E. R., Pasquini, G., Wang, K., Swiersy, A., Shipman, S. L., Appleton, E., Kiaee, K., Kohman, R. E., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0742-6 · Cited by 243
Self-citationDeep Learning in the Biomedical Applications: Recent and Future Status
Zemouri, R. M., Noureddine Zerhouni, A., & Racoceanu, D. (2019) · Applied Sciences · doi:10.3390/app9081526 · Cited by 200
Lin, Y., Cao, Y., Kim, H. J., Salim, A., Speed, T. P., Lin, D., Yang, P., & Yang, J. Y. H. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199389 · Cited by 168
SCSA: A Cell Type Annotation Tool for Single-Cell RNA-seq Data
Cao, Y., Wang, X., & Peng, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00490 · Cited by 166
clustifyr: an R package for automated single-cell RNA sequencing cluster classification
Fu, R., Gillen, A. E., Sheridan, R. M., Tian, C., Daya, M., Hao, Y., Hesselberth, J. R., & Riemondy, K. A. (2020) · F1000Research · doi:10.12688/f1000research.22969.2 · Cited by 135
scMatch: a single-cell gene expression profile annotation tool using reference datasets
Hou, R., Denisenko, E., & Forrest, A. R. R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz292 · Cited by 130
Update of the FANTOM web resource: high resolution transcriptome of diverse cell types in mammals
Lizio, M., Harshbarger, J., Abugessaisa, I., Noguchi, S., Kondo, A., Severin, J., Mungall, C., Arenillas, D. J., Mathelier, A., Medvedeva, Y. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw995 · Cited by 115
Lieberman, Y., Rokach, L., & Shay, T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205499 · Cited by 113
Evaluation of single-cell classifiers for single-cell RNA sequencing data sets
Zhao, X. S., Wu, S., Fang, N., Sun, X. S., & Fan, J. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz096 · Cited by 102
Histone Acetylation as a Regenerative Target in the Dentine-Pulp Complex
Yamauchi, Y., Cooper, P. R., Shimizu, E., Kobayashi, Y., Smith, A. J., & Duncan, H. F. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00001 · Cited by 93
Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysisPreprint
Hsiao, C. J., Tung, P., Blischak, J., Burnett, J. E., Barr, K., Dey, K. K., Stephens, M., & Gilad, Y. (2020) · Genome Research · doi:10.1101/gr.247759.118 · Cited by 85
Ekiz, H. A., Conley, C. J., Stephens, W. Z., & O’Connell, R. M. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3538-2 · Cited by 81
Xu, C. A., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/532895 · Cited by 80
Boufea, K., Seth, S., & Batada, N. N. (2020) · iScience · doi:10.1016/j.isci.2020.100914 · Cited by 77
Xie, P., Gao, M., Wang, C., Zhang, J., Noel, P., Yang, C., Von Hoff, D., Han, H., Zhang, M. Q., & Lin, W. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz116 · Cited by 74
Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq dataPreprint
Wagner, F., & Yanai, I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/456129 · Cited by 73
Global impact of somatic structural variation on the DNA methylome of human cancers
Zhang, Y., Yang, L., Kucherlapati, M. H., Hadjipanayis, A. G., Pantazi, A., Bristow, C. A., Lee, E. A., Mahadeshwar, H. S., Tang, J., Zhang, J., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1818-9 · Cited by 70
Amid, C., Pakseresht, N., Silvester, N. C., Jayathilaka, S., Lund, O., Dynovski, L. D., Pataki, B., Visontai, D., Xavier, B. B., Alako, B., et al. (2019) · Database · doi:10.1093/database/baz136 · Cited by 69
Capybara: A computational tool to measure cell identity and fate transitions
Kong, W., Fu, Y. C., Holloway, E. M., Garipler, G., Yang, X., Mazzoni, E. O., & Morris, S. A. (2022) · Cell stem cell · doi:10.1016/j.stem.2022.03.001 · Cited by 65
Investigating Tumor Heterogeneity in Mouse Models
Tammela, T., & Sage, J. (2019) · Annual Review of Cancer Biology · doi:10.1146/annurev-cancerbio-030419-033413 · Cited by 61
Identifying cell types to interpret scRNA-seq data: how, why and more possibilities
Wang, Z., Ding, H., & Zou, Q. (2020) · Briefings in Functional Genomics · doi:10.1093/bfgp/elaa003 · Cited by 52
Random forest based similarity learning for single cell RNA sequencing data
Pouyan, M. B., & Kostka, D. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty260 · Cited by 50
Díaz-Mejía, J. J., Meng, E. C., Pico, A. R., MacParland, S. A., Ketela, T., Pugh, T. J., Bader, G. D., & Morris, J. H. (2019) · F1000Research · doi:10.12688/f1000research.18490.1 · Cited by 50
Díaz-Mejía, J. J., Meng, E. C., Pico, A. R., MacParland, S. A., Ketela, T., Pugh, T. J., Bader, G. D., & Morris, J. H. (2019) · F1000Research · doi:10.12688/f1000research.18490.3 · Cited by 49
Johnson, T. S., Wang, T., Huang, Z., Yu, C. Y., Wu, Y., Han, Y., Zhang, Y., Huang, K., & Zhang, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz295 · Cited by 44
CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencingPreprint
de Kanter, J. K., Lijnzaad, P., Candelli, T., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/558908 · Cited by 36
Comparative transcriptomics of primary cells in vertebratesPreprint
Alam, T., Agrawal, S., Severin, J., Young, R. S., Andersson, R., Arner, E., Hasegawa, A., Lizio, M., Ramilowski, J. A., Abugessaisa, I., et al. (2020) · Genome Research · doi:10.1101/gr.255679.119 · Cited by 36
The Cell Theory, Past and Present1
Turner, W. (1890) · Nature · doi:10.1038/043031a0 · Cited by 24
Unifying single-cell annotations based on the Cell OntologyPreprint
Wang, S., Pisco, A. O., McGeever, A., Brbić, M., Žitnik, M., Darmanis, S., Leskovec, J., Karkanias, J., & Altman, R. B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/810234 · Cited by 21
scNym: Semi-supervised adversarial neural networks for single cell classificationPreprint
Kimmel, J. C., & Kelley, D. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.04.132324 · Cited by 19
Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome
Capriotti, E., Montanucci, L., Profiti, G., Rossi, I., Giannuzzi, D., Aresu, L., & Fariselli, P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz420 · Cited by 15
Pasquini, G., Cora, V., Swiersy, A., Achberger, K., Antkowiak, L., Müller, B., Wimmer, T., Fraschka, S. A. K., Casadei, N., Ueffing, M., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21041380 · Cited by 15
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- The T.W. and Thoery C.: Past and present. J Anat Physiol 1890; 24: 253.
- 10.1038/s41592-019-0535-3 · DOI · Paper page
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Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051
“Generally, pseudobulk methods with sum aggregation and mixed models such as MAST with random effect setting were found to be superior to naive methods, such as the popular Wilcoxon rank-sum test, which does not account for within-sample correlation 88 .”
MethodYang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., Lu, H., & Yao, J. (2021) · doi:10.1101/2021.12.05.471261 · Cited by 617
Advances in spatial transcriptomic data analysisPreprint
Dries, R., Chen, J. G., Rossi, N. D., Khan, M. M., Sistig, A., & Yuan, G. (2021) · Genome Research · doi:10.1101/gr.275224.121 · Cited by 246
Shao, X., Yang, H., Zhuang, X., Liao, J., Yang, P., Cheng, J., Lu, X., Chen, H., & Fan, X. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab775 · Cited by 158
Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications
Wu, Y., Cheng, M., Huang, S., Pei, Z., Zuo, Y., Liu, J., Yang, K., Zhu, Q., Zhang, J., Hong, H., et al. (2022) · Cancers · doi:10.3390/cancers14051199 · Cited by 148
Metadata
- Authors
- Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V.
- Affiliations
- University of Bonn (DE)
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
- Center for Regenerative Therapies Dresden (DE)
- Corresponding author
- Busskamp, Volker
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2021-01
- Volume
- 19
- Pages
- 961-969
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC7873570
- OpenAlex
- W3124814274
- Cited by
- 249
- References
- 85
- Citations by year
- 2026: 45 · 2025: 62 · 2024: 45 · 2023: 48 · 2022: 35 · 2021: 14
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo
- Funders
- H2020 European Research Council (ERC-StG-678071 – ProNeurons)
- European Research Council
- Deutsche Forschungsgemeinschaft (EXC-2068-390729961, SPP2127)
- Volkswagen Foundation
- Technische Universität Dresden (EXC-2151-390873048)
- Rheinische Friedrich-Wilhelms-Universität Bonn
- 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 › Genomics and Phylogenetic Studies
- Keywords
- cell type annotation, single-cell RNA sequencing, supervised classification, cellular heterogeneity
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 ›
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