The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
ACTINN: automated identification of cell types in single cell RNA sequencing
Open accessPublished version of a preprint
Abstract
Abstract Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with 3 hidden layers, trains on datasets with predefined cell types, and predicts cell types for other datasets based on the trained parameters. We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Author Summary Single cell RNA sequencing (scRNA-seq) provides high resolution profiling of the transcriptomes of individual cells, which inevitably results in high volumes of data that require complex data processing pipelines. Usually, one of the first steps in the analysis of scRNA-seq is to assign individual cells to known cell types. To accomplish this, traditional methods first group the cells into different clusters, then find marker genes, and finally use these to manually assign cell types for each cluster. Thus these methods require prior knowledge of cell type canonical markers, and some level of subjectivity to make the cell type assignments. As a result, the process is often laborious and requires domain specific expertise, which is a barrier for inexperienced users. By contrast, our neural network ACTINN automatically learns the features for each predefined cell type and uses these features to predict cell types for individual cells. This approach is computationally efficient and requires no domain expertise of the tissues being studied. We believe ACTINN allows users to rapidly identify cell types in their datasets, thus rendering the analysis of their scRNA-seq datasets more efficient.
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?
In the citing paper
“Furthermore, the parameter settings of HVG selection and PCA in these methods are far to reach a consensus and inevitably introduce artificial bias for performance evaluation 15- 19 .”
From scBERT as a Large-scale Pretrained Deep Language Model for Cell Type Annotation of Single-cell RNA-seq Data (2021) · cites this paper as [15]
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
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
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Deep generative modeling for single-cell transcriptomics
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I., & Yosef, N. (2018) · Nature Methods · doi:10.1038/s41592-018-0229-2 · Cited by 2,530
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Haghverdi, L., Lun, A. T. L., Morgan, M. D., & Marioni, J. C. (2018) · Nature Biotechnology · doi:10.1038/nbt.4091 · Cited by 2,416
Single-cell RNA sequencing technologies and bioinformatics pipelines
Hwang, B., Lee, J. H., & Bang, D. (2018) · Experimental & Molecular Medicine · doi:10.1038/s12276-018-0071-8 · Cited by 1,866
Winkels, H., Ehinger, E., Vassallo, M., Buscher, K., Dinh, H. Q., Kobiyama, K., Hamers, A. A. J., Cochain, C., Vafadarnejad, E., Saliba, A.-E., et al. (2018) · Circulation Research · doi:10.1161/circresaha.117.312513 · Cited by 547
Tan, Y., & Cahan, P. (2019) · Cell Systems · doi:10.1016/j.cels.2019.06.004 · Cited by 368
Using neural networks for reducing the dimensions of single-cell RNA-Seq data
Lin, C., Jain, S., Kim, H., & Bar-Joseph, Z. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx681 · Cited by 197
Removal of batch effects using distribution-matching residual networks
Shaham, U., Stanton, K., Zhao, J., Li, H., Raddassi, K., Montgomery, R. R., & Kluger, Y. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx196 · Cited by 170
Lieberman, Y., Rokach, L., & Shay, T. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205499 · Cited by 113
Flagged first, then by citations
Research works printed but not linked: 1
- 2010, Proceedings of the thirteenth international conference on artificial intelligence and statistics, PMLR, 9, 249
Cited by
Sorted by citations
Ianevski, A., Giri, A. K., & Aittokallio, T. A. (2022) · Nature Communications · doi:10.1038/s41467-022-28803-w · Cited by 787
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
Large-scale foundation model on single-cell transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628
Yang, 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
Mapping microglia states in the human brain through the integration of high-dimensional techniques
Sankowski, R., Böttcher, C., Masuda, T., Geirsdottir, L., Sagar, Sindram, E., Seredenina, T., Muhs, A., Scheiwe, C., Shah, M. J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0532-y · Cited by 449
Mapping human haematopoietic stem cells from haemogenic endothelium to birth
Calvanese, V., Capellera-Garcia, S., Ma, F., Fares, I., Liebscher, S., Ng, E. S., Ekstrand, S., Aguadé-Gorgorió, J., Vavilina, A., Lefaudeux, D., et al. (2022) · Nature · doi:10.1038/s41586-022-04571-x · Cited by 279
Self-citationAutomated methods for cell type annotation on scRNA-seq data
Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.015 · Cited by 249
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189
Histone H3.3G34-Mutant Interneuron Progenitors Co-opt PDGFRA for Gliomagenesis
Chen, C. C. L., Deshmukh, S., Jessa, S., Hadjadj, D., Lisi, V., Andrade, A. F., Faury, D., Jawhar, W., Dali, R., Suzuki, H., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.012 · Cited by 199
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
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
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
Fortelny, N., & Bock, C. (2020) · Genome Biology · doi:10.1186/s13059-020-02100-5 · Cited by 135
scSorter: assigning cells to known cell types according to marker genes
Guo, H., & Li, J. (2021) · Genome biology · doi:10.1186/s13059-021-02281-7 · Cited by 132
A brain cell atlas integrating single-cell transcriptomes across human brain regions
Chen, X., Huang, Y., Huang, L., Huang, Z., Hao, Z.-Z., Xu, L., Xu, N., Li, Z., Mou, Y., Ye, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03150-z · Cited by 118
Large Scale Foundation Model on Single-cell Transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L., & Zhang, X. (2023) · doi:10.1101/2023.05.29.542705 · Cited by 639
Ng, E., Sarila, G., Li, J. Y., Edirisinghe, H. S., Saxena, R., Sun, S., Bruveris, F., Labonne, T., Sleebs, N., Maytum, A., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02360-7 · Cited by 106
Self-citationData analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
Su, M., Pan, T., Chen, Q.-Z., Zhou, W., Gong, Y., Xu, G., Yan, H.-Y., Li, S., Shi, Q.-Z., Zhang, Y., et al. (2022) · Military Medical Research · doi:10.1186/s40779-022-00434-8 · Cited by 114
Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding
Chen, X., Chen, S., Song, S., Gao, Z., Hou, L., Zhang, X., Lv, H., & Jiang, R. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-021-00432-w · Cited by 92
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
Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges
Liu, J., Fan, Z., Zhao, W., & Zhou, X. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.655536 · Cited by 86
Single-cell omics: experimental workflow, data analyses and applications
Sun, F., Li, H., Sun, D., Fu, S., Gu, L., Shao, X., Wang, Q., Dong, X., Duan, B., Xing, F., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2561-0 · Cited by 90
Computational methods for the integrative analysis of single-cell data
Forcato, M., Romano, O., & Bicciato, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa042 · Cited by 71
CellPLM: Pre-training of Cell Language Model Beyond Single CellsPreprint
Wen, H., Tang, W., Dai, X., Ding, J., Jin, W., Xie, Y., & Tang, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.03.560734 · Cited by 104
Automatic cell type identification methods for single-cell RNA sequencing
Xie, B., Jiang, Q., Mora, A., & Li, X. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.10.027 · Cited by 66
Leveraging the Cell Ontology to classify unseen cell types
Wang, S., Pisco, A. O., McGeever, A., Brbić, M., Žitnik, M., Darmanis, S., Leskovec, J., Karkanias, J., & Altman, R. B. (2021) · Nature Communications · doi:10.1038/s41467-021-25725-x · Cited by 87
Identifying tumor cells at the single-cell level using machine learning
Dohmen, J., Baranovskii, A., Ronen, J., Uyar, B., Franke, V., & Akalin, A. (2022) · Genome biology · doi:10.1186/s13059-022-02683-1 · Cited by 63
SMILE: mutual information learning for integration of single-cell omics data
Xu, Y., Das, P., & McCord, R. P. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab706 · Cited by 59
Yuan, L., Sun, S., Jiang, Y., Zhang, Q., Ye, L., Zheng, C.-H., & Huang, D.-S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae662 · Cited by 56
Siu, D. M. D., Lee, K. C. M., Lo, M. C. K., Stassen, S. V., Wang, M., Zhang, I. Z. Q., So, H. K., Chan, G., Cheah, K. S. E., Wong, K. K. Y., et al. (2020) · Lab on a Chip · doi:10.1039/d0lc00542h · Cited by 52
RA3 is a reference-guided approach for epigenetic characterization of single cells
Chen, S., Yan, G., Zhang, W., Li, J., Jiang, R., & Lin, Z. (2021) · Nature Communications · doi:10.1038/s41467-021-22495-4 · Cited by 52
Single-cell classification using graph convolutional networks
Wang, T., Bai, J., & Nabavi, S. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04278-2 · Cited by 55
Cellcano: supervised cell type identification for single cell ATAC-seq data
Ma, W., Lu, J., & Wu, H. (2023) · Nature Communications · doi:10.1038/s41467-023-37439-3 · Cited by 51
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
Michaels, Y. S., Edgar, J. M., Major, M. C., Castle, E. L., Zimmerman, C., Yin, T., Hagner, A., Lau, C., Hsu, H. H., Ibanez-Rios, M. I., et al. (2022) · Science Advances · doi:10.1126/sciadv.abn5522 · Cited by 53
CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology
Bernstein, M. N., Ma, Z., Gleicher, M., & Dewey, C. N. (2021) · iScience · doi:10.1016/j.isci.2020.101913 · Cited by 45
Neural G0: a quiescent‐like state found in neuroepithelial‐derived cells and glioma
O’Connor, S. A., Feldman, H. M., Arora, S., Hoellerbauer, P., Toledo, C. M., Corrin, P. D., Carter, L. M., Kufeld, M., Bolouri, H., Basom, R., et al. (2021) · Molecular Systems Biology · doi:10.15252/msb.20209522 · 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
A robust and scalable graph neural network for accurate single-cell classification
Zeng, Y., Wei, Z., Pan, Z., Lu, Y., & Yang, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab570 · Cited by 42
Deep learning-based advances and applications for single-cell RNA-sequencing data analysis
Bao, S., Li, K., Yan, C., Zhang, Z., Qu, J., & Zhou, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab473 · Cited by 40
Andrade, A. F., Annett, A., Karimi, E., Topouza, D. G., Rezanejad, M., Liu, Y., McNicholas, M., Santiago, E. G. G., Llivichuzhca-Loja, D., Gehlhaar, A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52096-w · Cited by 40
Heydari, A. A., Davalos, O. A., Zhao, L., Hoyer, K. K., & Sindi, S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac095 · Cited by 36
Canozo, F. J. G., Zuo, Z. C., Martin, J. F., & Samee, M. A. H. (2021) · Cell Systems · doi:10.1016/j.cels.2021.09.004 · Cited by 34
scGraph: a graph neural network-based approach to automatically identify cell types
Yin, Q., Liu, Q., Fu, Z., Zeng, W., Zhang, B., Zhang, X., Jiang, R., & Lv, H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac199 · Cited by 37
DEEPsc: A Deep Learning-Based Map Connecting Single-Cell Transcriptomics and Spatial Imaging Data
Maseda, F., Cang, Z., & Nie, Q. H. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.636743 · Cited by 33
xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data
Gong, J., Hao, M., Zeng, X., Liu, C., Ma, J., Cheng, X., Wang, T., Zhang, X., & Song, L. (2023) · doi:10.1101/2023.03.24.534055 · Cited by 17
Abu-Doleh, A., & Fahoum, A. S. A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109066 · Cited by 29
Xu, Y., Begoli, E., & McCord, R. P. (2022) · npj Systems Biology and Applications · doi:10.1038/s41540-022-00245-6 · Cited by 33
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 38
scTyper: a comprehensive pipeline for the cell typing analysis of single-cell RNA-seq data
Choi, J. H., Kim, H. I., & Woo, H. G. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03700-5 · Cited by 28
Huang, Y., & Zhang, P. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab035 · Cited by 28
Tang, H., Yu, X., Liu, R., & Zeng, T. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab584 · Cited by 27
An overview of computational methods in single-cell transcriptomic cell type annotation
Li, T., Wang, Z., Liu, Y., He, S., Zou, Q., & Zhang, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf207 · Cited by 28
Analysis of single-cell RNA sequencing data based on autoencoders
Tangherloni, A., Ricciuti, F., Besozzi, D., Lió, P., & Cvejic, A. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04150-3 · Cited by 30
scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data
Nguyen, V., & Griss, J. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04574-5 · Cited by 28
Cutting-Edge AI Technologies Meet Precision Medicine to Improve Cancer Care
Lin, P., Tsai, Y.-S., Yeh, Y., & Shen, M.-R. (2022) · Biomolecules · doi:10.3390/biom12081133 · Cited by 27
Comparison of high-throughput single-cell RNA sequencing data processing pipelines
Gao, M., Ling, M., Tang, X., Wang, S., Xu, X., Qiao, Y., Yang, W., & Yu, R. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa116 · Cited by 24
Integrating multiple references for single-cell assignment
Duan, B., Chen, S., Chen, X., Zhu, C., Tang, C., Wang, S., Gao, Y., Fu, S., & Liu, Q. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab380 · Cited by 23
Overview of single‐cell RNA sequencing analysis and its application to spermatogenesis research
Suzuki, T. (2023) · Reproductive Medicine and Biology · doi:10.1002/rmb2.12502 · Cited by 24
scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings
Jiao, L., Wang, G., Dai, H., Li, X., Wang, S., & Song, T. (2023) · Biomolecules · doi:10.3390/biom13040611 · Cited by 26
Zhao, M., Li, J., Liu, X., Ma, K., Tang, J., & Guo, F. (2024) · Genome Research · doi:10.1101/gr.278439.123 · Cited by 26
Yin, Q., Wang, Y., Guan, J., & Ji, G. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab508 · Cited by 23
MarkerCount: A stable, count-based cell type identifier for single-cell RNA-seq experiments
Kim, H., Lee, J., Kang, K., & Yoon, S. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.010 · Cited by 24
DEMOC: a deep embedded multi-omics learning approach for clustering single-cell CITE-seq data
Zou, G., Lin, Y., Han, T., & Ou-Yang, L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac347 · Cited by 24
Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data
Islam, M. T., & Xing, L. (2023) · Nature Communications · doi:10.1038/s41467-023-36383-6 · Cited by 26
Li, K., Yan, C., Li, C., Chen, L., Zhao, J., Zhang, Z., Bao, S., Sun, J., & Zhou, M. (2021) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2021.12.009 · Cited by 22
Cheng, Y., Fan, X., Zhang, J., & Li, Y. (2023) · Communications Biology · doi:10.1038/s42003-023-04928-6 · Cited by 22
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
scPretrain: multi-task self-supervised learning for cell-type classification
Zhang, R., Luo, Y., Ma, J., Zhang, M., & Wang, S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac007 · Cited by 21
Song, T., Dai, H., Wang, S., Gan, W., Zhang, X., Zhang, Y., & Jiao, L. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1038919 · Cited by 20
Topological and geometric analysis of cell states in single-cell transcriptomic data
Huynh, T., & Cang, Z. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae176 · Cited by 19
Liu, Y., Guo, W., Li, C., Shen, L.-C., Gasser, R. B., Song, J., Chen, D., & Yu, D. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad132 · Cited by 16
Fan, X., Liu, J., Yang, Y., Gu, C., Han, Y., Wu, B., Jiang, Y., Chen, G., & Heng, P. (2024) · Communications Biology · doi:10.1038/s42003-024-07154-w · Cited by 17
CellMeSH: probabilistic cell-type identification using indexed literature
Mao, S., Zhang, Y., Seelig, G., & Kannan, S. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab834 · Cited by 18
HiCat: a semi-supervised approach for cell type annotation
Bi, C., Bai, K., & Zhang, X. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf428 · Cited by 15
CellSTAR: a comprehensive resource for single-cell transcriptomic annotation
Zhang, Y., Sun, H., Zhang, W., Fu, T., Huang, S., Mou, M., Zhang, J., Gao, J., Ge, Y., Yang, Q., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad874 · Cited by 15
DANCE: a deep learning library and benchmark platform for single-cell analysis
Ding, J., Liu, R., Wen, H., Tang, W., Li, Z., Venegas, J., Su, R., Molho, D., Jin, W., Wang, Y., et al. (2024) · Genome biology · doi:10.1186/s13059-024-03211-z · Cited by 18
Evaluation of single-cell RNAseq labelling algorithms using cancer datasets
Christensen, E., Luo, P., Turinsky, A. L., Husić, M., Mahalanabis, A., Naidas, A., Díaz-Mejía, J. J., Brudno, M., Pugh, T. J., Ramani, A. K., et al. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac561 · Cited by 15
Putative cell type discovery from single-cell gene expression data
Miao, Z., Moreno, P., Huang, N., Papatheodorou, I., Brazma, A., & Teichmann, S. A. (2020) · Nature Methods · doi:10.26226/morressier.5ebd45acffea6f735881af34 · Cited by 133
Chen, S., Duan, B., Zhu, C., Tang, C., Wang, S., Gao, Y., Fu, S., Fan, L., Yang, Q., & Liu, Q. (2022) · Science China Life Sciences · doi:10.1007/s11427-022-2224-4 · Cited by 16
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
Yin, Q., & Chen, L. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad449 · Cited by 13
scFed: federated learning for cell type classification with scRNA-seq
Wang, S., Shen, B., Guo, L., Shang, M., Liu, J., Sun, Q., & Shen, B. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad507 · Cited by 13
Application of Deep Learning for Single Cell Multi-Omics: A State-of-the-Art Review
Wani, S. A., Khan, S. A., & Quadri, S. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10230-x · Cited by 13
He, H., Tang, Z., Chen, G., Xu, F., Hu, Y. F., Feng, Y., Wu, J., Huang, Y., Huang, Z.-A., & Tan, K. C. (2025) · Genome biology · doi:10.1186/s13059-025-03779-0 · Cited by 12
Adversarial dense graph convolutional networks for single-cell classification
Wang, K., Li, Z., You, Z., Han, P., & Nie, R. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad043 · Cited by 16
Ren, P., Shi, X., Yu, Z., Dong, X., Ding, X., Wang, J., Sun, L., Yan, Y., Hu, J., Zhang, P., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100577 · Cited by 13
SCIPAC: quantitative estimation of cell-phenotype associations
Gan, D., Zhu, Y., Lü, X., & Li, J. (2024) · Genome biology · doi:10.1186/s13059-024-03263-1 · Cited by 13
Single-Cell RNA-Seq Technologies and Computational Analysis Tools: Application in Cancer Research
Song, Q., & Liu, L. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1896-7_23 · Cited by 12
Lee, J., Kim, M., Kang, K., Yang, C., & Yoon, S. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad006 · Cited by 12
Deep generative models in single-cell omics
Rivero-García, I., Torres, M., & Sánchez‐Cabo, F. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108561 · Cited by 13
Classifying cell cycle states and a quiescent-like G0 state using single-cell transcriptomicsPreprint
O’Connor, S. A., Garcia, L., Hoover, R., Patel, A. P., Bartelle, B. B., Hugnot, J., Paddison, P. J., & Plaisier, C. L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.16.589816 · Cited by 14
Cell-type annotation with accurate unseen cell-type identification using multiple references
Xiong, Y.-X., Wang, M.-G., Chen, L., & Zhang, X.-F. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011261 · Cited by 11
Michaels, Y. S., Major, M. C., Bonham-Carter, B., Zhang, J.-Q., Heydari, T., Edgar, J. M., Siu, M. M., Greenstreet, L., Vilarrasa‐Blasi, R., Kim, S., et al. (2024) · Cell Systems · doi:10.1016/j.cels.2024.11.001 · Cited by 11
Choi, J. M., Rhee, J., & Chae, H. (2021) · IEEE Access · doi:10.1109/access.2021.3052923 · Cited by 11
Ianevski, A., Giri, A. K., & Aittokallio, T. A. (2022) · RePEc: Research Papers in Economics · Cited by 11
JIND: joint integration and discrimination for automated single-cell annotation
Goyal, M., Serrano, G., Argemí, J., Shomorony, I., Hernáez, M., & Ochoa, I. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac140 · Cited by 11
A reference-free approach for cell type classification with scRNA-seq
Sun, Q., Peng, Y., & Liu, J. (2021) · iScience · doi:10.1016/j.isci.2021.102855 · Cited by 10
MarkerCapsule: Explainable Single Cell Typing using Capsule NetworksPreprint
Ray, S., & Schönhuth, A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.22.307512 · Cited by 8
CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server
Lyu, P., Zhai, Y., Li, T., & Qian, J. X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad521 · Cited by 8
Metadata
- Authors
- Ma, F., & Pellegrini, M.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Pellegrini, Matteo
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-07-29
- Volume
- 36
- Issue
- 2
- Pages
- 533-538
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2964643116
- Cited by
- 252
- References
- 12
- Citations by year
- 2026: 25 · 2025: 46 · 2024: 45 · 2023: 40 · 2022: 36 · 2021: 35 · 2020: 20 · 2019: 5
- FWCI
- 7.94
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy Office of Science
- Office of Biological and Environmental Research (DE-FC02-02ER63421)
- 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 › Biophysics › Cell Image Analysis Techniques
- Keywords
- cell type annotation, single-cell RNA sequencing, neural network classification, human peripheral blood mononuclear cells, T cell subsets, immune cell classification, cell type atlas
- MeSH terms
- Algorithms, Animals, Humans, Cluster Analysis, Sequence Analysis, RNA, Gene Expression Profiling, Mice, Single-Cell Analysis
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
