Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data
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Abstract
Background: Many functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way. Results: To address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community. Conclusions: Our analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.
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Enrichment methods developed for bulk transcriptomics can be applied to scRNA-seq.
“Enrichment methods developed for bulk transcriptomics can be applied to scRNA-seq102”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [102]
Gene set enrichment analysis is more sensitive to the choice of gene sets than to statistical methods.
“Gene set enrichment analysis was found to be more sensitive to the choice of gene sets rather than statistical methods102”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [102]
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R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
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 · see citing works
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019) · Journal of Open Source Software · doi:10.21105/joss.01686 · Cited by 22,138 · see citing works
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392 · 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
NF-κB signaling in inflammation
Liu, T., Zhang, L., Joo, D., & Sun, S.-C. (2017) · Signal Transduction and Targeted Therapy · doi:10.1038/sigtrans.2017.23 · Cited by 8,791 · 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
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
Next-generation characterization of the Cancer Cell Line Encyclopedia
Ghandi, M., Huang, F. W., Jané-Valbuena, J., Kryukov, G. V., Lo, C. C., McDonald, E. R., Barretina, J., Gelfand, E. T., Bielski, C. M., Li, H., et al. (2019) · Nature · doi:10.1038/s41586-019-1186-3 · Cited by 3,394 · see citing works
Margolin, A. A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-s1-s7 · Cited by 2,420 · see citing works
Margolin, A. A., Nemenman, I. M., Basso, K., Wiggins, C. H., Stolovitzky, G. A., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.7916/d8g15zbp · Cited by 2,260 · see citing works
Dixit, A., Parnas, O., Li, B., Chen, J., Fulco, C. P., Jerby-Arnon, L., Marjanovic, N. D., Dionne, D., Burks, T., Raychowdhury, R., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.038 · Cited by 1,863 · 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
Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges
Khatri, P., Sirota, M., & Butte, A. J. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002375 · Cited by 1,569 · see citing works
A 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 1,511 · see citing works
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 · see citing works
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 · see citing works
Computational and analytical challenges in single-cell transcriptomics
Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Self-citationChEA3: transcription factor enrichment analysis by orthogonal omics integration
Keenan, A. B., Torre, D., Lachmann, A., Leong, A. K., Wojciechowicz, M. L., Utti, V., Jagodnik, K. M., Kropiwnicki, E., Wang, Z., & Ma’ayan, A. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz446 · Cited by 1,136 · see citing works
Benchmark and integration of resources for the estimation of human transcription factor activities
(2019) · doi:10.1101/gr.240663.118 · Cited by 1,122 · see citing works
Self-citationAlvarez, M. J., Shen, Y., Giorgi, F. M., Lachmann, A., Ding, B. B., Ye, B. H., & Califano, A. (2016) · Nature Genetics · doi:10.1038/ng.3593 · Cited by 966 · see citing works
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905 · see citing works
A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project
Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., et al. (2015) · Biopreservation and Biobanking · doi:10.1089/bio.2015.0032 · Cited by 833 · see citing works
Massive mining of publicly available RNA-seq data from human and mouse
Lachmann, A., Torre, D., Keenan, A. B., Jagodnik, K. M., Lee, H. J., Wang, L., Silverstein, M. C., & Ma’ayan, A. (2018) · Nature Communications · doi:10.1038/s41467-018-03751-6 · Cited by 701 · see citing works
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 · see citing works
Self-citationMcFarland, J. M., Ho, Z. V., Kugener, G., Dempster, J. M., Montgomery, P. G., Bryan, J. G., Krill-Burger, J., Green, T. M., Vazquez, F., Boehm, J., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06916-5 · Cited by 348 · see citing works
Gene set enrichment analysis: performance evaluation and usage guidelines
Hung, J., Yang, T.-H., Hu, Z., Weng, Z., & DeLisi, C. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr049 · Cited by 265 · see citing works
Pathway analysis using random forests classification and regression
Pang, H., Lin, A., Holford, M. E., Enerson, B. E., Lu, B., Lawton, M. P., Floyd, E., & Zhao, H. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl344 · Cited by 239 · see citing works
Biological plasticity rescues target activity in CRISPR knock outs
Smits, A. H., Ziebell, F., Joberty, G., Zinn, N., Mueller, W. F., Clauder-Münster, S., Eberhard, D., Fälth Savitski, M., Grandi, P., Jakob, P., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0614-5 · Cited by 219 · see citing works
Identifying significantly impacted pathways: a comprehensive review and assessment
Nguyen, T., Shafi, A., Nguyen, T., & Drăghici, S. (2019) · Genome biology · doi:10.1186/s13059-019-1790-4 · Cited by 209 · see citing works
Macrophage-specific gene functions in Spi1-directed innate immunity
Zakrzewska, A., Cui, C., Stockhammer, O. W., Benard, E. L., Spaink, H. P., & Meijer, A. H. (2010) · Blood · doi:10.1182/blood-2010-01-262873 · Cited by 199 · see citing works
Essaghir, A., Toffalini, F., Knoops, L., Kallin, A., van Helden, J., & Demoulin, J. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq149 · Cited by 197 · see citing works
Feng, X., Wang, H., Takata, H., Day, T. J., Willen, J., & Hu, H. (2011) · Nature Immunology · doi:10.1038/ni.2034 · Cited by 191 · see citing works
Transfer of regulatory knowledge from human to mouse for functional genomics analysis
Holland, C. H., Szalai, B., & Sáez-Rodríguez, J. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194431 · Cited by 137 · see citing works
Self-citationDing, H., Douglass, E. F., Sonabend, A. M., Mela, A., Bose, S., Gonzalez, C., Canoll, P., Sims, P. A., Alvarez, M. J., & Califano, A. (2018) · Nature Communications · doi:10.1038/s41467-018-03843-3 · Cited by 118 · see citing works
SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data
Peng, T., Zhu, Q., Yin, P.-H., & Tan, K. (2019) · Genome Biology · doi:10.1186/s13059-019-1681-8 · Cited by 92 · see citing works
Genga, R. M. J., Kernfeld, E. M., Parsi, K. M., Parsons, T. J., Ziller, M. J., & Maehr, R. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.076 · Cited by 91 · see citing works
Discovering causal signaling pathways through gene-expression patterns
Parikh, J. R., Klinger, B., Xia, Y., Marto, J. A., & Blüthgen, N. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq424 · Cited by 69 · see citing works
Hegde, M., Strand, C., Hanna, R. E., & Doench, J. G. (2018) · PLOS ONE · doi:10.1371/journal.pone.0197547 · Cited by 66 · see citing works
Classification of gene signatures for their information value and functional redundancy
Cantini, L., Calzone, L., Martignetti, L., Rydenfelt, M., Blüthgen, N., Barillot, E., & Zinovyev, A. Y. (2017) · npj Systems Biology and Applications · doi:10.1038/s41540-017-0038-8 · Cited by 54 · see citing works
Footprint-based functional analysis of multiomic data
Dugourd, A., & Saez-Rodriguez, J. (2019) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2019.04.002 · Cited by 53 · see citing works
TRAIL-R1 and TRAIL-R2 Mediate TRAIL-Dependent Apoptosis in Activated Primary Human B Lymphocytes
Staniek, J., Lorenzetti, R., Heller, B., Janowska, I., Schneider, P., Unger, S., Warnatz, K., Seidl, M., Venhoff, N., Thiel, J., et al. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00951 · Cited by 22 · see citing works
Burd, A. L., Ingraham, R. H., Goldrick, S. E., Kroe‐Barrett, R., Crute, J. J., & Grygon, C. A. (2004) · Biochemistry · doi:10.1021/bi030262o · Cited by 17 · see citing works
Trescher, S., Münchmeyer, J., & Leser, U. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0419-z · Cited by 13 · see citing works
Transfer of regulatory knowledge from human to mouse for functional genomic analysisPreprint
Holland, C. H., Szalai, B., & Sáez-Rodríguez, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/532739 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [40]Sergushichev A. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv. 2016:060012 [cited 2018 Jul 17]. Available from: https://www.biorxiv.org/content/early/2016/06/20/060012.abstract.
- [51]Holland CH, Tanevski J, Perales-Patón J, Gleixner J, Kumar MP, Mereu E, et al. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. GitHub. 2020; Available from: https://github.com/saezlab/FootprintMethods_on_scRNAseq. PMC7017576 32051003
- [52]Holland CH Saez-Rodriguez J Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data 2019 PMC7017576 32051003
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“3).”
Self-citationdecoupleR: ensemble of computational methods to infer biological activities from omics data
(2022) · doi:10.1093/bioadv/vbac016 · Cited by 1,098 · see citing works
Self-citationDecoding myofibroblast origins in human kidney fibrosis
Kuppe, C., Ibrahim, M. M., Kranz, J., Zhang, X., Ziegler, S., Perales-Patón, J., Jansen, J., Reimer, K. C., Smith, J. R., Dobie, R., et al. (2021) · Nature · doi:10.1038/s41586-020-2941-1 · Cited by 769 · see citing works
Self-citationBenchmarking single-cell RNA-sequencing protocols for cell atlas projects
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Self-citationSalcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., Panizzolo, E., Martowicz, A., Trebo, M., Pall, G., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.008 · Cited by 479 · see citing works
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Badia-i-Mompel, P., Wessels, L., Müller‐Dott, S., Trimbour, R., Flores, R. O. R., Argelaguet, R., & Sáez-Rodríguez, J. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00618-5 · Cited by 475 · see citing works
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García‐Alonso, L., Lorenzi, V., Icoresi-Mazzeo, C., Alves-Lopes, J. P., Roberts, K., Sancho‐Serra, C., Engelbert, J., Marečková, M., Gruhn, W. H., Botting, R. A., et al. (2022) · Nature · doi:10.1038/s41586-022-04918-4 · Cited by 452 · see citing works
Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function
Eraslan, G., Drokhlyansky, E., Anand, S. K., Fiškin, E., Subramanian, A., Slyper, M., Wang, J., Wittenberghe, N. V., Rouhana, J. M., Waldman, J., et al. (2022) · Science · doi:10.1126/science.abl4290 · Cited by 396 · see citing works
Müller‐Dott, S., Tsirvouli, E., Vázquez, M., Flores, R. O. R., Badia-i-Mompel, P., Fallegger, R., Türei, D., Lægreid, A., & Sáez-Rodríguez, J. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad841 · Cited by 404 · see citing works
Hoeft, K., Schaefer, G. J. L., Kim, H., Schumacher, D., Bleckwehl, T., Long, Q., Klinkhammer, B. M., Peisker, F., Koch, L., Nagai, J. S., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112131 · Cited by 323 · see citing works
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Suo, C., Dann, E., Goh, I., Jardine, L., Kleshchevnikov, V., Park, J.-E., Botting, R. A., Stephenson, E., Engelbert, J., Tuong, Z. K., et al. (2022) · Science · doi:10.1126/science.abo0510 · Cited by 315 · see citing works
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Self-citationExplainable multiview framework for dissecting spatial relationships from highly multiplexed data
(2022) · doi:10.1186/s13059-022-02663-5 · Cited by 297 · see citing works
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Korsunsky, I., Wei, K., Pohin, M., Kim, E. Y., Barone, F., Major, T., Taylor, E., Ravindran, R. P., Kemble, S., Watts, G. F. M., et al. (2022) · Med · doi:10.1016/j.medj.2022.05.002 · Cited by 245 · see citing works
Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma
Bischoff, P., Trinks, A., Obermayer, B., Pett, J. P., Wiederspahn, J., Uhlitz, F., Liang, X., Lehmann, A., Jurmeister, P. S., Elsner, A., et al. (2021) · Oncogene · doi:10.1038/s41388-021-02054-3 · Cited by 242 · see citing works
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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
Tumor-infiltrating dendritic cell states are conserved across solid human cancers
Gerhard, G. M., Bill, R., Messemaker, M., Klein, A. M., & Pittet, M. J. (2020) · The Journal of Experimental Medicine · doi:10.1084/jem.20200264 · Cited by 240 · see citing works
Gong, Z., Li, Q., Shi, J., Wei, J. Q., Li, P., Chang, C., Shultz, L. D., & Ren, G. (2022) · Immunity · doi:10.1016/j.immuni.2022.07.001 · Cited by 241 · see citing works
Li, H., Dixon, E. E., Wu, H., & Humphreys, B. D. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.09.026 · Cited by 211 · see citing works
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Gurtner, A., Borrelli, C., Gonzalez-Perez, I., Bach, K., Acar, İ. E., Núñez, N. G., Crepaz, D., Handler, K., Vu, V. P., Lafzi, A., et al. (2022) · Nature · doi:10.1038/s41586-022-05628-7 · Cited by 180 · see citing works
Pei, W., Shang, F., Wang, X., Fanti, A.-K., Greco, A., Busch, K., Klapproth, K., Zhang, Q., Quedenau, C., Sauer, S., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.07.018 · Cited by 174 · see citing works
Jin, H., Zhang, C., Zwahlen, M., von Feilitzen, K., Karlsson, M., Shi, M., Yuan, M., Song, X., Li, X., Yang, H., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-41132-w · Cited by 176 · see citing works
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Saichi, M., Ladjemi, M. Z., Korniotis, S., Rousseau, C., Hamou, Z. A., Massenet-Regad, L., Amblard, E., Noël, F., Marie, Y., Bouteiller, D., et al. (2021) · Nature Cell Biology · doi:10.1038/s41556-021-00681-2 · Cited by 151 · see citing works
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Hatton, C. F., Botting, R. A., Dueñas, M. E., Haq, I., Verdon, B., Thompson, B. J., Spegarova, J. S., Gothe, F., Stephenson, E., Gardner, A. I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27318-0 · Cited by 122 · see citing works
Statistical mechanics meets single-cell biology
Teschendorff, A. E., & Feinberg, A. P. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00341-z · Cited by 117 · see citing works
Desta, G. M., & Birhanu, A. G. (2025) · Acta Biochimica Polonica · doi:10.3389/abp.2025.13922 · Cited by 121 · see citing works
Ma, Y., Deng, C., Zhou, Y., Zhang, Y., Qiu, F., Jiang, D., Zheng, G., Li, J., Shuai, J., Zhang, Y., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100383 · Cited by 107 · see citing works
Fei, L., Chen, H., Ma, L., Weigao, E., Wang, R., Fang, X., Zhou, Z., Sun, H., Wang, J., Jiang, M., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01118-8 · Cited by 107 · see citing works
Urea cycle activation triggered by host-microbiota maladaptation driving colorectal tumorigenesis
Chen, H., Tong, T., Lu, S., Ji, L., Xuan, B., Zhao, G., Yan, Y., Song, L., Zhao, L., Xie, Y., et al. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.03.003 · Cited by 106 · see citing works
Harmonized single-cell landscape, intercellular crosstalk and tumor architecture of glioblastomaPreprint
Ruiz-Moreno, C., Salas, S. M., Samuelsson, E. R., Brandner, S., Kranendonk, M. E. G., Nilsson, M. F., & Stunnenberg, H. G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.27.505439 · Cited by 100 · see citing works
Mapping the cardiac vascular niche in heart failure
Peisker, F., Halder, M., Nagai, J. S., Ziegler, S. M., Kaesler, N., Hoeft, K., Li, R., Bindels, E. M., Kuppe, C., Moellmann, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30682-0 · Cited by 99 · see citing works
Fan, G., Xie, T., Li, L., Tang, L., Han, X., & Shi, Y. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00564-3 · Cited by 106 · see citing works
Li, H., Li, D., Ledru, N., Xuanyuan, Q., Wu, H., Asthana, A., Byers, L. N., Tullius, S. G., Orlando, G., Waikar, S. S., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.02.015 · Cited by 98 · see citing works
Xu, Q., Γεωργίου, Γ., Frölich, S., van der Sande, M., Veenstra, G. J. C., Zhou, H., & van Heeringen, S. J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab598 · Cited by 88 · see citing works
Mo, C., Guo, J., Qin, J., Zhang, X., Sun, Y., Wei, H., Cao, D., Zhang, Y., Zhao, C., Xiong, Y., et al. (2021) · The EMBO Journal · doi:10.15252/embj.2021108415 · Cited by 93 · see citing works
Zhao, H., Yin, X., Xu, H., Liu, K., Liu, W., Wang, L., Zhang, C., Bo, L., Lan, X., Lin, S., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac984 · Cited by 80 · see citing works
Benchmarking algorithms for pathway activity transformation of single-cell RNA-seq data
Zhang, Y., Ma, Y., Huang, Y., Zhang, Y., Jiang, Q., Zhou, M., & Su, J. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.10.007 · Cited by 79 · see citing works
Becker, L. M., Chen, S.-H., Rodor, J., de Rooij, L. P. M. H., Baker, A. H., & Carmeliet, P. (2022) · Cardiovascular Research · doi:10.1093/cvr/cvac018 · Cited by 76 · see citing works
Chen, Z., Li, Y., Yuan, Y., Lai, K., Ye, K., Lin, Y., Lan, R., Chen, H., & Xu, Y. (2023) · Cell Death and Disease · doi:10.1038/s41419-023-05830-z · Cited by 78 · see citing works
Hautz, T., Salcher, S., Fodor, M., Sturm, G., Ebner, S., Mair, A., Trebo, M., Untergasser, G., Sopper, S., Cardini, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37674-8 · Cited by 73 · see citing works
Selective inhibition of stromal mechanosensing suppresses cardiac fibrosis
Cho, S., Rhee, S., Madl, C. M., Caudal, A., Thomas, D., Kim, H., Kojic, A., Shin, H. S., Mahajan, A., Jahng, J. W. S., et al. (2025) · Nature · doi:10.1038/s41586-025-08945-9 · Cited by 72 · see citing works
Xu, Y., Kuppe, C., Perales-Patón, J., Hayat, S., Kranz, J., Abdallah, A. T., Nagai, J. S., Li, Z., Peisker, F., Saritas, T., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01202-z · Cited by 67 · see citing works
Self-citationXue, Y., Luo, M., Hu, X., Li, X., Shen, J., Zhu, W., Huang, L., Hu, Y., Guo, Y., Liu, L., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-04255-2 · Cited by 66 · see citing works
Fanti, A.-K., Busch, K., Greco, A., Wang, X., Cirovic, B., Shang, F., Nizharadze, T., Frank, L., Barile, M., Feyerabend, T. B., et al. (2023) · Cell stem cell · doi:10.1016/j.stem.2022.12.014 · Cited by 60 · see citing works
Transcriptional census of epithelial-mesenchymal plasticity in cancer
Cook, D. P., & Vanderhyden, B. C. (2022) · Science Advances · doi:10.1126/sciadv.abi7640 · Cited by 62 · see citing works
GRaNIE and GRaNPA: inference and evaluation of enhancer‐mediated gene regulatory networks
Kamal, A., Arnold, C., Claringbould, A., Moussa, R., Servaas, N. H., Kholmatov, M. M., Daga, N., Nogina, D., Müller‐Dott, S., Reyes‐Palomares, A., et al. (2023) · Molecular Systems Biology · doi:10.15252/msb.202311627 · Cited by 63 · see citing works
Han, P.-Z., Ye, W.-D., Yu, P., Tan, L.-C., Xiao Hua, S. H. I., Chen, X.-F., He, C., Hu, J., Wei, W., Lu, Z., et al. (2024) · JCI Insight · doi:10.1172/jci.insight.173712 · Cited by 63 · see citing works
Mead, B. E., Hattori, K., Levy, L. E., Imada, S., Goto, N., Vukovic, M., Sze, D., Kummerlowe, C., Matute, J. D., Duan, J., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00863-9 · Cited by 59 · see citing works
A multi-omics atlas of the human retina at single-cell resolution
Liang, Q., Cheng, X., Wang, J., Owen, L. A., Shakoor, A., Lillvis, J. L., Zhang, C., Farkas, M. H., Kim, I. K., Li, Y., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100298 · Cited by 59 · see citing works
Noureen, N., Ye, Z., Chen, Y., Wang, X., & Zheng, S. (2022) · eLife · doi:10.7554/elife.71994 · Cited by 58 · see citing works
Schäfer, P. S. L., Dimitrov, D., Villablanca, E. J., & Sáez-Rodríguez, J. (2024) · Nature Immunology · doi:10.1038/s41590-024-01768-2 · Cited by 66 · see citing works
Gong, T., Jiang, Y., Shao, C., Peng, W., Liu, M., Li, D., Zhang, B.-Y., Du, P., Huang, Y., Li, F., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110460 · Cited by 60 · see citing works
Jansen, J., van den Berge, B. T., van den Broek, M., Maas, R. J. H., Daviran, D., Willemsen, B. K., Roverts, R., van der Kruit, M., Kuppe, C., Reimer, K. C., et al. (2022) · Development · doi:10.1242/dev.200198 · Cited by 54 · see citing works
Dai, L., Fan, G., Xie, T., Li, L., Tang, L. M., Chen, H., Shi, Y., & Xiao-hong, H. (2024) · Biomarker Research · doi:10.1186/s40364-024-00605-w · Cited by 50 · see citing works
Vaccine-associated enhanced respiratory pathology in COVID-19 hamsters after TH2-biased immunization
Ebenig, A., Muraleedharan, S., Kazmierski, J., Tödt, D., Auste, A., Anzaghe, M., Gömer, A., Postmus, D., Gogesch, P., Niles, M. A., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111214 · Cited by 48 · see citing works
Robust temporal map of human in vitro myelopoiesis using single-cell genomics
Alsinet, C., Primo, M. N., Lorenzi, V., Bello, E., Kelava, I., Jones, C. P., Vilarrasa‐Blasi, R., Sancho‐Serra, C., Knights, A. J., Park, J.-E., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30557-4 · Cited by 44 · see citing works
Chen, Y., Sun, Y., Luo, Z., Lin, J., Qi, B., Kang, X., Ying, C., Guo, C., Yao, M., Chen, X., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.860364 · Cited by 46 · see citing works
Mukund, K., Nayak, P., Ashokkumar, C., Rao, S., Almeda, J. L., Betancourt-Garcia, M. M., Sindhi, R. K., & Subramaniam, S. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.738073 · Cited by 45 · see citing works
Gene regulatory networks in disease and ageing
Avila, P. U., Padvitski, T., Leote, A. C., Chen, H., Sáez-Rodríguez, J., Kann, M., & Beyer, A. (2024) · Nature Reviews Nephrology · doi:10.1038/s41581-024-00849-7 · Cited by 44 · see citing works
Li, G., Zhao, H., Cheng, Z., Liu, J., Li, G., & Guo, Y. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.02.006 · Cited by 45 · see citing works
van Kuijk, K., Demandt, J. A. F., Perales-Patón, J., Theelen, T. L., Kuppe, C., Marsch, E., de Bruijn, J., Jin, H., Gijbels, M. J., Matic, L. P., et al. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab152 · Cited by 40 · see citing works
Self-citationGuo, J., Qiu, J., Jia, M., Li, Q., Wei, X., Li, L., Pan, Q., Jin, J., Ge, F., Ma, S., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad120 · Cited by 39 · see citing works
Flynn, E., Almonte-Loya, A., & Fragiadakis, G. K. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020422-050645 · Cited by 40 · see citing works
Cross-regional homeostatic and reactive glial signatures in multiple sclerosis
Trobisch, T., Zulji, A., Stevens, N. A., Schwarz, S., Wischnewski, S., Öztürk, M., Perales-Patón, J., Haeussler, M., Saez-Rodriguez, J., Velmeshev, D., et al. (2022) · Acta Neuropathologica · doi:10.1007/s00401-022-02497-2 · Cited by 40 · see citing works
Self-citationscTPA: a web tool for single-cell transcriptome analysis of pathway activation signatures
Zhang, Y., Zhang, Y., Hu, J. S., Zhang, J., Guo, F., Zhou, M., Zhang, G., Yu, F., & Su, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa532 · Cited by 39 · see citing works
Fan, G., Tao, C., Li, L., Xie, T., Tang, L. M., Han, X., & Shi, Y. (2024) · Hepatology · doi:10.1097/hep.0000000000001138 · Cited by 40 · see citing works
Werder, R. B., Liu, T., Abo, K. M., Lindstrom-Vautrin, J., Villacorta-Martín, C., Huang, J., Hinds, A., Boyer, N., Bullitt, E., Liesa, M., et al. (2022) · Science Advances · doi:10.1126/sciadv.abo6566 · Cited by 38 · see citing works
Tumor heterogeneity and tumor-microglia interactions in primary and recurrent IDH1-mutant gliomas
Blanco-Carmona, E., Narayanan, A., Hernandez, I., Nieto, J. C., Elosua-Bayés, M., Sun, X., Schmidt, C. S., Pamir, M. N., Özduman, K., Herold‐Mende, C. C., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101249 · Cited by 37 · see citing works
Self-citationMarteau, V., Nemati, N., Handler, K., Raju, D., Kirchmair, A., Rieder, D., Soto, E. K., Fotakis, G., de Lange, G., Carollo, S., et al. (2026) · Cancer Cell · doi:10.1016/j.ccell.2025.12.003 · Cited by 37 · see citing works
Impaired BCAA catabolism in adipose tissues promotes age-associated metabolic derangement
Han, H.-S., Ahn, E., Park, E. S., Huh, T., Choi, S., Kwon, Y., Choi, B. H., Lee, J., Choi, Y. H., Jeong, Y. L., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00460-8 · Cited by 38 · see citing works
Improved detection of tumor suppressor events in single-cell RNA-Seq data
Teschendorff, A. E., & Wang, N. (2020) · npj Genomic Medicine · doi:10.1038/s41525-020-00151-y · Cited by 33 · see citing works
Why do pathway methods work better than they should?
Szalai, B., & Saez‐Rodriguez, J. (2020) · FEBS Letters · doi:10.1002/1873-3468.14011 · Cited by 34 · see citing works
Self-citationJiang, Y., Yu, W., Hu, T., Peng, H., Hu, F., Yuan, Y., Liu, X., Lai, S., Zhou, J., & Dong, X. S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1335333 · Cited by 31 · see citing works
JAK/STAT signaling promotes the emergence of unique cell states in ulcerative colitis
Maciag, G. J., Hansen, S. L., Krizic, K., Kellermann, L., Zøylner, M. J. I., Ulyanchenko, S., Maimets, M., Baattrup, A. M., Riis, L. B., Khodosevich, K., et al. (2024) · Stem Cell Reports · doi:10.1016/j.stemcr.2024.06.006 · Cited by 32 · see citing works
The single‐cell transcriptional landscape of lung carcinoid tumors
Bischoff, P., Trinks, A., Wiederspahn, J., Obermayer, B., Pett, J. P., Jurmeister, P. S., Elsner, A., Dziodzio, T., Rückert, J., Neudecker, J., et al. (2022) · International Journal of Cancer · doi:10.1002/ijc.33995 · Cited by 31 · see citing works
Monocytes differentiate along two alternative pathways during sterile inflammation
Villar, J., Ouaknin, L., Cros, A., & Segura, É. (2023) · EMBO Reports · doi:10.15252/embr.202256308 · Cited by 32 · see citing works
Comparative analysis of syngeneic mouse models of high-grade serous ovarian cancer
Cook, D. P., Galpin, K. J. C., Rodriguez, G. M., Shakfa, N. E., Wilson‐Sanchez, J., Echaibi, M., Pereira, M., Matuszewska, K., Haagsma, J., Murshed, H., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05529-z · Cited by 31 · see citing works
Prospects and challenges of cancer systems medicine: from genes to disease networks
Karimi, M. R., Karimi, A. H., Abolmaali, S., Sadeghi, M., & Schmitz, U. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab343 · Cited by 33 · see citing works
Microfluidics Facilitates the Development of Single-Cell RNA Sequencing
Pan, Y., Cao, W., Mu, Y., & Zhu, Q. (2022) · Biosensors · doi:10.3390/bios12070450 · Cited by 31 · see citing works
Mathur, L., Szalai, B., Du, N. H., Utharala, R., Ballinger, M., Landry, J. J. M., Ryckelynck, M., Beneš, V., Sáez-Rodríguez, J., & Merten, C. A. (2022) · Nature Communications · doi:10.1038/s41467-022-32197-0 · Cited by 27 · see citing works
Self-citationInference of age-associated transcription factor regulatory activity changes in single cells
Maity, A. K., Hu, X., Zhu, T., & Teschendorff, A. E. (2022) · Nature Aging · doi:10.1038/s43587-022-00233-9 · Cited by 28 · see citing works
Transcriptome profiling for precision cancer medicine using shallow nanopore cDNA sequencing
Möck, A., Braun, M., Scholl, C., Fröhling, S., & Erkut, C. (2023) · Scientific Reports · doi:10.1038/s41598-023-29550-8 · Cited by 28 · see citing works
Liang, J., Wei, J., Cao, J., Qian, J., Gao, R., Li, X., Wang, D., Gu, Y., Dong, L., Yu, J., et al. (2023) · Genome Biology · doi:10.1186/s13059-023-03084-8 · Cited by 27 · see citing works
Plattner, C., Lamberti, G., Blattmann, P., Kirchmair, A., Rieder, D., Loncova, Z., Sturm, G., Scheidl, S., Ijsselsteijn, M., Fotakis, G., et al. (2023) · iScience · doi:10.1016/j.isci.2023.108399 · Cited by 27 · see citing works
Pott, F., Postmus, D., Brown, R. J. P., Wyler, E., Neumann, E., Landthaler, M., & Goffinet, C. (2021) · Emerging Microbes & Infections · doi:10.1080/22221751.2021.2000891 · Cited by 28 · see citing works
Gao, S., Dai, Y. D., & Rehman, J. (2021) · Genome Research · doi:10.1101/gr.265595.120 · Cited by 23 · see citing works
Horny, K., Sproll, C. K., Peiffer, L., Furtmann, F., Gerhardt, P., Gravemeyer, J., Stoecklein, N. H., Spassova, I., & Becker, J. C. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04102-w · Cited by 25 · see citing works
Decoding sequence determinants of gene expression in diverse cellular and disease states
Lal, A., Karollus, A., Gunsalus, L. M., Garfield, D., Nair, S., Tseng, A. M., Gordon, M. G., Blischak, J., Geijn, B. V. D., Bhangale, T., et al. (2026) · Nature Methods · doi:10.1038/s41592-026-03102-0 · Cited by 24 · see citing works
Mabrouk, M. H. E., Goetzke, R., Abagnale, G., Yesilyurt, B., Salz, L., Cypris, O., Glück, P., Liesenfelder, S., Zeevaert, K., Ma, Z., et al. (2022) · Biomaterials · doi:10.1016/j.biomaterials.2022.121389 · Cited by 24 · see citing works
A novel strategy to dissect multifaceted macrophage function in human diseases
Karlinsey, K., Qu, L., Matz, A. J., & Zhou, B. (2022) · Journal of Leukocyte Biology · doi:10.1002/jlb.6mr0522-685r · Cited by 23 · see citing works
Hepatic adaptation to chronic metabolic stress primes tumorigenesis
Tzouanas, C. N., Shay, J. E. S., Sherman, M. S., Rubin, A. J., Mead, B. E., Dao, T. T., Tao, J., Lehrich, B. M., Eng, G. M., Patterson-Fortin, J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.11.031 · Cited by 24 · see citing works
Liu, T., Zhao, X., Lin, Y., Luo, Q., Zhang, S., Xi, Y., Chen, Y., Lin, L., Fan, W., Yang, J., et al. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-0668 · Cited by 23 · see citing works
Eymael, J., van den Broek, M., Miesen, L., Monge, V. V., van den Berge, B. T., Mooren, F., Velez, V. L., Dijkstra, J. J., Hermsen, M., Bándi, P., et al. (2022) · The Journal of Pathology · doi:10.1002/path.6029 · Cited by 23 · see citing works
Big Data Approaches in Heart Failure Research
Lanzer, J. D., Leuschner, F., Kramann, R., Levinson, R. T., & Sáez-Rodríguez, J. (2020) · Current Heart Failure Reports · doi:10.1007/s11897-020-00469-9 · Cited by 23 · see citing works
- Authors
- 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., Szalai, B., & Saez-Rodriguez, J.
- Affiliations
- Heidelberg University (DE)
- German Cancer Research Center (DE)
- Barcelona Institute of Science and Technology (ES)
- Koch Institute for Integrative Cancer Research At MIT (US)
- European Bioinformatics Institute (GB)
- Massachusetts Institute of Technology (US)
- Universitat Pompeu Fabra (ES)
- Semmelweis University (HU)
- ORCID
- Venue
- Genome Biology
- Published
- 2020-02-12
- Volume
- 21
- Issue
- 1
- Pages
- 36-36
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC7017576
- OpenAlex
- W3007199577
- Cited by
- 323
- References
- 51
- Citations by year
- 2026: 37 · 2025: 39 · 2024: 67 · 2023: 60 · 2022: 61 · 2021: 38 · 2020: 18 · 2013: 1
- FWCI
- 14.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Semmelweis University Repository (Semmelweis University) (submittedVersion)
- Funders
- Bundesministerium für Bildung und Forschung (FKZ: 031L0049)
- National Institutes of Health (U54-CA217377)
- Ministry of Science, Innovation and Universities (SAF2017-89109-P)
- 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, transcription factor activity, pathway activity, benchmarking, offspring, SCENIC, AUCell, Dorothea, gene ontology enrichment, simulated data
- MeSH terms
- RNA-Seq, Animals, Humans, Software, Transcription Factors, Benchmarking, Gene Regulatory Networks, Single-Cell Analysis, Transcriptome
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