PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data
Open accessAbstract
Single-cell RNA sequencing is an increasingly used method to measure gene expression at the single cell level and build cell-type atlases of tissues. Hundreds of single-cell sequencing datasets have already been published. However, studies are frequently deposited as raw data, a format difficult to access for biological researchers due to the need for data processing using complex computational pipelines. We have implemented an online database, PanglaoDB, accessible through a user-friendly interface that can be used to explore published mouse and human single cell RNA sequencing studies. PanglaoDB contains pre-processed and pre-computed analyses from more than 1054 single-cell experiments covering most major single cell platforms and protocols, based on more than 4 million cells from a wide range of tissues and organs. The online interface allows users to query and explore cell types, genetic pathways and regulatory networks. In addition, we have established a community-curated cell-type marker compendium, containing more than 6000 gene-cell-type associations, as a resource for automatic annotation of cell types.
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
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?
Cell type annotations were supported by expression patterns of curated marker genes.
“Annotations were supported by expression patterns of curated marker genes16,17 (Extended Data Fig. 1e; Supplementary Table S2).”
From APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes (2022) · cites this paper as [17]
Unannotated clusters in human skin tumor scRNA-seq were manually annotated via established marker gene expression and clonal copy number variation profiles.
“Unannotated clusters (presumed to reflect one of tumor, cancer-associated fibroblast or endothelial cells) were manually annotated via established marker gene expression98 and clonal copy number variation profiles, examined using the inferCNV114 package for R.”
From Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy (2023) · cites this paper as [98]
Four housekeeping genes (GUSB, PPIA, RPL15, RPL19) with minimal variation on GC treatment were selected for RT-PCR normalization on the basis of a literature review and differential expression analysis in ENCODE RNA-seq data.
“Four housekeeping genes (GUSB, PPIA, RPL15, RPL19) with minimal variation on GC treatment were selected on the basis of a literature review98 and differential expression analysis in ENCODE RNA-seq data (accession ENCSR897XFT), implemented in edgeR.”
From Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy (2023) · cites this paper as [98]
The Results and discussion section describes an index of genes involved in housekeeping tasks, but does not identify GUSB, PPIA, RPL15, or RPL19 as RT-PCR normalization genes. Searches for those genes, GC-treatment stability, a literature review supporting their selection, and the specified ENCODE analysis found no passage addressing the attributed selection; the paper’s Materials and methods instead describes single-cell RNA-seq analyses. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
HISAT: a fast spliced aligner with low memory requirements
Kim, D., Langmead, B., & Salzberg, S. L. (2015) · Nature Methods · doi:10.1038/nmeth.3317 · Cited by 24,112 · see citing works
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807 · see citing works
Gene Expression Omnibus: NCBI gene expression and hybridization array data repository
Edgar, R. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.207 · Cited by 13,288 · see citing works
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 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works
GENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585 · see citing works
FIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247 · see citing works
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137 · see citing works
The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
Liao, Y., Smyth, G. K., & Shi, W. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt214 · Cited by 3,064 · see citing works
Leinonen, R., Sugawara, H., & Shumway, M. F. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1019 · Cited by 2,760 · see citing works
Sambamba: fast processing of NGS alignment formats
Tarasov, A., Vilella, A. J., Cuppen, E. P. J. G., Nijman, I. J., & Prins, P. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv098 · Cited by 2,339 · see citing works
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012776 · Cited by 2,131 · see citing works
Smith, T. S., Heger, A., & Sudbery, I. M. (2017) · Genome Research · doi:10.1101/gr.209601.116 · Cited by 2,072 · see citing works
Determination and Inference of Eukaryotic Transcription Factor Sequence Specificity
Weirauch, M. T., Yang, A. W. H., Albu, M., Coté, A. G., Montenegro‐Montero, A., Drewe, P., Najafabadi, H. S., Lambert, S. A., Mann, I. K., Cook, K. B., et al. (2014) · Cell · doi:10.1016/j.cell.2014.08.009 · Cited by 1,705 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.2 · Cited by 1,586 · see citing works
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 · see citing works
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 · see citing works
Khan, A., Fornés, O., Stigliani, A., Gheorghe, M., Castro-Mondragon, J. A., van der Lee, R., Bessy, A., Chèneby, J., Kulkarni, S. R., Tan, G., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1126 · Cited by 1,436 · see citing works
Quantitative single-cell RNA-seq with unique molecular identifiers
Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183 · see citing works
iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections
Janky, R., Verfaillie, A., Imrichová, H., Van de Sande, B., Standaert, L., Christiaens, V., Hulselmans, G., Herten, K., Naval Sanchez, M., Potier, D., et al. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003731 · Cited by 900 · cited 2× · see citing works
“Regulons were identified by largely following what was described in (23) and (24).”
MethodA step-by-step workflow for low-level analysis of single-cell RNA-seq data
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.1 · Cited by 789 · see citing works
GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks
Moerman, T., Aibar Santos, S., Bravo González-Blas, C., Simm, J., Moreau, Y., Aerts, J., & Aerts, S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty916 · Cited by 621 · see citing works
Cooperative Binding of Transcription Factors Orchestrates Reprogramming
Chronis, C., Fiziev, P., Papp, B., Butz, S., Bonora, G., Sabri, S., Ernst, J., & Plath, K. (2017) · Cell · doi:10.1016/j.cell.2016.12.016 · Cited by 555 · see citing works
Design and computational analysis of single-cell RNA-sequencing experiments
Bacher, R. L., & Kendziorski, C. M. (2016) · Genome biology · doi:10.1186/s13059-016-0927-y · Cited by 437 · see citing works
UniPROBE: an online database of protein binding microarray data on protein-DNA interactions
Newburger, D. E., & Bulyk, M. L. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn660 · Cited by 367 · see citing works
FANTOM5 CAGE profiles of human and mouse samples
Noguchi, S., Arakawa, T., Fukuda, S., Furuno, M., Hasegawa, A., Hori, F., Ishikawa-Kato, S., Kaida, K., Kaiho, A., Kanamori-Katayama, M., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.112 · Cited by 310 · see citing works
Gotea, V., Visel, A., Westlund, J. M., Nobrega, M. A., Pennacchio, L. A., & Ovcharenko, I. (2010) · Genome Research · doi:10.1101/gr.104471.109 · Cited by 248 · see citing works
HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models
Kulakovskiy, I. V., Vorontsov, I. E., Yevshin, I., Soboleva, A. V., Kasianov, A. S., Ashoor, H., Ba-Alawi, W., Bajić, V. B., Medvedeva, Y. A., Kolpakov, F., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1249 · Cited by 210 · see citing works
TFCat: the curated catalog of mouse and human transcription factors
Fulton, D. L., Sundararajan, S., Badis, G., Hughes, T. R., Wasserman, W. W., Roach, J. C., & SLADEK, R. M. (2009) · Genome biology · doi:10.1186/gb-2009-10-3-r29 · Cited by 207 · see citing works
Down-weighting overlapping genes improves gene set analysis
Tarca, A. L., Drăghici, S., Bhatti, G., & Romero, R. J. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-136 · Cited by 154 · see citing works
SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates
Pachkov, M. V., Balwierz, P. J., Arnold, P., Ozonov, E. A., & van Nimwegen, E. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1145 · Cited by 153 · see citing works
Evaluating tools for transcription factor binding site prediction
Jayaram, N., Usvyat, D., & Martin, A. C. R. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1298-9 · Cited by 137 · see citing works
Scholer‐Dahirel, A., Schlabach, M. R., Loo, A. T., Bagdasarian, L., Meyer, R., Guo, R., Woolfenden, S., Yu, K., Markovits, J., Killary, K. A., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1104182108 · Cited by 118 · see citing works
scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells
Cao, Y., Zhu, J., Han, G., Jia, P., & Zhao, Z. (2017) · Genes · doi:10.3390/genes8120368 · Cited by 104 · see citing works
eDGAR: a database of Disease-Gene Associations with annotated Relationships among genes
Babbi, G., Martelli, P. L., Profiti, G., et al. (2017) · BMC Genomics · doi:10.1186/s12864-017-3911-3 · Cited by 67 · see citing works
SCPortalen: human and mouse single-cell centric database
Abugessaisa, I., Noguchi, S., Böttcher, M. E., Hasegawa, A., Kouno, T., Kato, S., Tada, Y., Ura, H., Abe, K., Shin, J. W., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx949 · Cited by 57 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Transfer learning enables predictions in network biology
Theodoris, C. V., Xiao, L., Chopra, A., Chaffin, M. D., Al Sayed, Z. R., Hill, M. C., Mantineo, H., Brydon, E. M., Zeng, Z., Liu, X. S., et al. (2023) · Nature · doi:10.1038/s41586-023-06139-9 · Cited by 1,193 · see citing works
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 · see citing works
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 · see citing works
Barkley, D., Moncada, R., Pour, M., Liberman, D. A., Dryg, I., Werba, G., Wang, W., Baron, M., Rao, A., Xia, B., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01141-9 · Cited by 532 · see citing works
APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes
Blanchard, J. W., Akay, L. A., Davila-Velderrain, J., von Maydell, D., Mathys, H., Davidson, S. M., Effenberger, A., Chen, C.-Y., Maner-Smith, K., Hajjar, I., et al. (2022) · Nature · doi:10.1038/s41586-022-05439-w · Cited by 493 · see citing works
“Annotations were supported by expression patterns of curated marker genes 16,17 (Extended Data Fig.”
Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations
Affo, S., Nair, A., Brundu, F., Ravichandra, A., Bhattacharjee, S., Matsuda, M., Chin, L., Filliol, A., Wen, W., Song, X., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.03.012 · Cited by 416 · see citing works
Automated 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 · see citing works
A curated database reveals trends in single-cell transcriptomics
Svensson, V., da Veiga Beltrame, E., & Pachter, L. (2020) · Database · doi:10.1093/database/baaa073 · Cited by 223 · see citing works
DISCO: A database of Deeply Integrated human Single-Cell Omics data
Li, M., Zhang, X., Ang, K. S., Ling, J., Sethi, R., Lee, N. Y. S., Ginhoux, F., & Chen, J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1020 · Cited by 205 · see citing works
Iron accumulation drives fibrosis, senescence and the senescence-associated secretory phenotype
Maus, M., López-Polo, V., Mateo, L., Lafarga, M., Aguilera, M., De Lama, E., Meyer, K., Sola, A., Lopez-Martinez, C., López-Alonso, I., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00928-2 · Cited by 215 · see citing works
Osorio, D., & Cai, J. J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa751 · Cited by 212 · see citing works
Characterization of cellular senescence in aging skeletal muscle
Zhang, X., Habiballa, L., Aversa, Z., Ng, Y. E., Sakamoto, A. E., Englund, D. A., Pearsall, V. M., White, T. A., Robinson, M. M., Rivas, D. A., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00250-8 · Cited by 206 · see citing works
Cobolt: integrative analysis of multimodal single-cell sequencing data
Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168 · see citing works
Humphrey, J., Venkatesh, S., Hasan, R., Herb, J. T., de Paiva Lopes, K., Küçükali, F., Byrska-Bishop, M., Evani, U. S., Narzisi, G., Fagegaltier, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01205-3 · Cited by 138 · see citing works
Oncogenic KRAS Drives Lipofibrogenesis to Promote Angiogenesis and Colon Cancer Progression
Hsu, W.-H., LaBella, K. A., Lin, Y., Xu, P., Lee, R., Hsieh, C.-E., Yang, L., Zhou, A., Blecher, J. M., Wu, C.-J., et al. (2023) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-1467 · Cited by 125 · see citing works
A comparison of marker gene selection methods for single-cell RNA sequencing data
Pullin, J. M., & McCarthy, D. J. (2022) · doi:10.1101/2022.05.09.490241 · Cited by 122 · see citing works
Neuronal activity increases translocator protein (TSPO) levels
Notter, T., Schalbetter, S. M., Clifton, N. E., Mattei, D., Richetto, J., Thomas, K., Meyer, U., & Hall, J. (2021) · Molecular Psychiatry · doi:10.1038/s41380-020-0745-1 · Cited by 118 · see citing works
Xu, P., Wang, M., Song, W.-M., Wang, Q., Yuan, G.-C., Sudmant, P. H., Zare, H., Tu, Z., Orr, M. E., & Zhang, B. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00507-7 · Cited by 114 · see citing works
Zhang, X., Pearsall, V. M., Carver, C. M., Atkinson, E. J., Clarkson, B. D. S., Grund, E. M., Baez-Faria, M., Pavelko, K. D., Kachergus, J. M., White, T. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33226-8 · Cited by 102 · see citing works
Biologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100 · see citing works
Evaluation of large language models for discovery of gene set function
Hu, M., Alkhairy, S., Lee, I., Pillich, R. T., Fong, D., Smith, K., Bachelder, R., Ideker, T., & Pratt, D. (2025) · Nature Methods · doi:10.1038/s41592-024-02525-x · Cited by 104 · see citing works
Sanborn, M. A., Wang, X., Gao, S., Dai, Y., & Rehman, J. (2025) · Nature Communications · doi:10.1038/s41467-025-57047-7 · Cited by 82 · see citing works
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 · see citing works
Wahane, S., Zhou, X., Zhou, X., Guo, L., Friedl, M.-S., Kluge, M., Ramakrishnan, A., Shen, L., Friedel, C. C., Zhang, B., et al. (2021) · Science Advances · doi:10.1126/sciadv.abd8811 · Cited by 67 · see citing works
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 · see citing works
Yang, Y., Li, G., Zhong, Y., Xu, Q., Chen, B.-J., Lin, Y.-T., Chapkin, R. S., & Cai, J. J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad450 · Cited by 48 · see citing works
A Deep Dive into Single-Cell RNA Sequencing Foundation Models
Boiarsky, R., Singh, N., Buendia, A., Getz, G., & Sontag, D. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.19.563100 · Cited by 43 · see citing works
Kleeman, S. O., Thakir, T. M., Demestichas, B., Mourikis, N., Loiero, D., Ferrer, M., Bankier, S., Riazat-Kesh, Y. J. R. A., Lee, H., Chantzichristos, D., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100347 · Cited by 37 · see citing works
Costa-Verdera, H., Meneghini, V., Fitzpatrick, Z., Abou Alezz, M., Fabyanic, E., Huang, X., Dzhashiashvili, Y., Ahiya, A., Mangiameli, E., Valeri, E., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58778-3 · Cited by 32 · see citing works
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 · see citing works
Thurman, A. L., Ratcliff, J. A., Chimenti, M. S., & Pezzulo, A. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab337 · Cited by 27 · see citing works
Deeper evaluation of a single-cell foundation model
Boiarsky, R., Singh, N. M., Buendia, A., Amini, A. P., Getz, G., & Sontag, D. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00949-w · Cited by 25 · see citing works
Wang, W., Cen, Y., Lu, Z., Xu, Y., Sun, T., Xiao, Y., Liu, W., Li, J. J., & Wang, C. (2024) · Genome Biology · doi:10.1186/s13059-024-03284-w · Cited by 19 · see citing works
Lim, B., Kamal, A., Gomez Ramos, B., Adrian Segarra, J. M., Ibarra, I. L., Dignas, L., Kindinger, T., Volz, K., Rahbari, M., Rahbari, N., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02081-w · Cited by 17 · see citing works
Multi-modal quantification of pathway activity with MAYA
Landais, Y., & Vallot, C. (2023) · Nature Communications · doi:10.1038/s41467-023-37410-2 · Cited by 9 · see citing works
“This approach is based on activation of the fi rst mode using PanglaoDB 17 as input gene lists by default (Methods).”
MethodInfluentialComparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing
Kersey, H. N., Acri, D. J., Dabin, L. C., Hartigan, K. A., Mustaklem, R., Park, J. H., & Kim, J. (2026) · Cell Reports Methods · doi:10.1016/j.crmeth.2026.101337 · Cited by 8 · see citing works
A probabilistic gene expression barcode for annotation of cell-types from single cell RNA-seq data
Grabski, I. N., & Irizarry, R. A. (2020) · doi:10.1101/2020.01.05.895441 · Cited by 7 · see citing works
He, F., Fei, R., Krull, J. E., Yu, Y., Zhang, X., Wang, X., Cheng, H., Gao, M., Su, L., Chen, Y., et al. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01170-z · Cited by 5 · see citing works
Computational challenges of cell cycle analysis using single cell transcriptomics
Chervov, A., & Zinovyev, A. (2022) · arXiv · doi:10.48550/arxiv.2208.05229 · Cited by 4 · see citing works
AAV vectors trigger DNA damage responses and STING-dependent inflammation in human CNS cells
Costa-Verdera, H., Meneghini, V., Fitzpatrick, Z., Alezz, M. A., Fabyanic, E., Huang, X., Dzhashiashvili, Y., Ahiya, A., Mangiameli, E., Valeri, E., et al. (2024) · doi:10.21203/rs.3.rs-4171795/v1 · Cited by 32 · see citing works
Han, X., Hu, R., Ping, X., Chen, J., Liu, J., Liu, Z., Jin, X., Gao, X., Lin, M., Zhao, Y., et al. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71908
Park, J. H., Kim, B., Al‐Amin, M. M., Tate, M. D., Sharify, A. D., John, S. K., Karahan, H., Lu, H., Dabin, L. C., & Kim, J. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71742
“2.0), 32 PanglaoDB, 33 and Semi-Automated Hand Annotation (SAHA, v0.”
InfluentialAcri, D. J., Mustaklem, R., Horan-Portelance, L., Dabin, L. C., Park, J. H., Hartigan, K. A., Kersey, H. N., Mesecar, M. E., Gibbs, J. R., Cookson, M. R., et al. (2026) · bioRxiv · doi:10.64898/2026.07.30.741795
“…the two clusters which were depleted in a mouse model of amyloidosis 35 with the deletion of an E3 ubiquitin ligase (IDOL) (clusters 10 and 22) were both annotated as Somatostatin positive chondrolectin expressing inhibitory neurons (056 Sst Chodl Gaba), a distinct subtype of neurons…”
- Authors
- Franzén, O., Gan, L.-M., & Björkegren, J. L. M.
- Affiliations
- Karolinska Institutet (SE)
- AstraZeneca (Sweden) (SE)
- Corresponding author
- Franzén, Oscar
- Venue
- Database
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-01-01
- Volume
- 2019
- ISSN
- 1758-0463 (ISSN-L)
- PMC
- PMC6450036
- OpenAlex
- W2931036699
- Cited by
- 1,551
- References
- 42
- Citations by year
- 2026: 231 · 2025: 336 · 2024: 314 · 2023: 258 · 2022: 191 · 2021: 144 · 2020: 71 · 2019: 6
- FWCI
- 34.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Database (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Karolinska Institutet
- AstraZeneca Integrated Cardio Metabolic Centre
- Fondation Leducq—Transantlantic PlaqOmics Network
- Hjärt-och Lungfonden (20170265)
- Vetenskapsrådet (2018-02529)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, web server, mice, human, cell type atlas, cell type markers, cell type annotation, gene regulatory networks, gene pathways, gene expression in tissues
- MeSH terms
- RNA-Seq, Animals, Humans, RNA, Databases, Nucleic Acid, Mice
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
