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Computational approaches for interpreting sc RNA‐seq data
Open accessAbstract
The recent developments in high‐throughput single‐cell RNA sequencing technology (sc RNA‐seq) have enabled the generation of vast amounts of transcriptomic data at cellular resolution. With these advances come new modes of data analysis, building on high‐dimensional data mining techniques. Here, we consider biological questions for which sc RNA‐seq data is used, both at a cell and gene level, and describe tools available for these types of analyses. This is an exciting and rapidly evolving field, where clustering, pseudotime inference, branching inference and gene‐level analyses are particularly informative areas of computational analysis.
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“Recently developed single-cell RNA-sequencing technology enabled the generation of vast amounts of the transcriptomic data at cellular resolution19.”
From High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa (2020) · cites this paper as [19]
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
(2008) · Journal of Machine Learning Research · Cited by 33,051
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
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 21,341
LIII. On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 12,864
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314
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
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231
Research works printed but not linked: 3
- 10.1126/sciimmunol.aal2192 · DOI
- Campbell K, 2016, Ouija: Incorporating prior knowledge in single‐cell trajectory learning using Bayesian nonlinear factor analysis, bioRxiv
- Andrews TS, 2016, Modelling dropouts allows for unbiased identification of marker genes in scRNASeq experiments, bioRxiv
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Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071
Self-citationSingle-cell transcriptomics to explore the immune system in health and disease
Stubbington, M. J. T., Rozenblatt–Rosen, O., Regev, A., & Teichmann, S. A. (2017) · Science · doi:10.1126/science.aan6828 · Cited by 586
Self-citationExploring the single-cell RNA-seq analysis landscape with the scRNA-tools database
Zappia, L., Phipson, B., & Oshlack, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006245 · Cited by 319
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241
Chen, K., Wang, Q., Li, M., Guo, H., Liu, W., Wang, F., Tian, X., & Yang, Y. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2021.103315 · Cited by 236
Understanding tumor ecosystems by single-cell sequencing: promises and limitations
Ren, X., Kang, B., & Zhang, Z. (2018) · Genome biology · doi:10.1186/s13059-018-1593-z · Cited by 220
Interpretable factor models of single-cell RNA-seq via variational autoencoders
Svensson, V., Gayoso, A., Yosef, N., & Pachter, L. S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa169 · Cited by 191
Self-citationGorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186
Evaluation of Cell Type Annotation R Packages on Single-Cell RNA-Seq Data
Huang, Q., Liu, Y., Du, Y., & Garmire, L. X. (2021) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.07.004 · Cited by 149
Cell composition analysis of bulk genomics using single-cell data
Frishberg, A., Peshes‐Yaloz, N., Cohn, O., Rosentul, D. C., Steuerman, Y., Valadarsky, L., Yankovitz, G., Mandelboim, M., Iraqi, F. A., Amit, I., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0355-5 · Cited by 111
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Chazarra-Gil, R., van Dongen, S., Kiselev, V. Y., & Hemberg, M. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab004 · Cited by 93
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Molin, A. D., & Camillo, B. D. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby007 · Cited by 97
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Palit, S., Heuser-Loy, C., de Almeida, G. P., Theis, F. J., & Zielinski, C. E. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.01515 · Cited by 82
Ke, M. S., Elshenawy, B., Sheldon, H., Arora, A., & Buffa, F. M. (2022) · BioEssays · doi:10.1002/bies.202200084 · Cited by 83
Understanding initiation and progression of hepatocellular carcinoma through single cell sequencing
Li, X., Shen, Y., Zhang, L., Guo, X., & Wu, J. H. (2022) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2022.188720 · Cited by 73
PROSSTT: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes
Παπαδόπουλος, Ν., Parra, R. G., & Söding, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz078 · Cited by 60
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Cristinelli, S., & Ciuffi, A. (2018) · Current Opinion in Virology · doi:10.1016/j.coviro.2018.03.001 · Cited by 57
Federico, A., Serra, A., Ha, M. K., Kohonen, P. J., Choi, J., Liampa, I., Nymark, P. E. H., Sanabria, N. M., Cattelani, L., Fratello, M., et al. (2020) · Nanomaterials · doi:10.3390/nano10050903 · Cited by 58
Schiebinger, G. R., Shu, J., Tabaka, M., Cleary, B., Subramanian, V., Solomon, A., Liu, S., Lin, S. L., Berube, P., Lee, L., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/191056 · Cited by 54
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Zhang, J. M., Fan, J., Fan, H. C., Rosenfeld, D., & Tse, D. N. C. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2092-7 · Cited by 51
Guo, Y., Li, Y., Cai, B., He, Q., Chen, G., Wang, M., Wang, K., Wan, X., & Yan, Q. (2021) · Aging · doi:10.18632/aging.202288 · Cited by 51
Gorin, G., Vastola, J. J., Fang, M., & Pachter, L. S. (2022) · Nature Communications · doi:10.1038/s41467-022-34857-7 · Cited by 51
Generative modeling of single-cell gene expression for dose-dependent chemical perturbations
Kana, O., Nault, R., Filipovic, D., Marri, D., Zacharewski, T., & Bhattacharya, S. (2023) · Patterns · doi:10.1016/j.patter.2023.100817 · Cited by 44
Zeng, L., Yang, K., Zhang, T., Zhu, X., Hao, W., Chen, H., & Ge, J. (2022) · Journal of Autoimmunity · doi:10.1016/j.jaut.2022.102919 · Cited by 47
The Unmixing Problem: A Guide to Applying Single-Cell RNA Sequencing to Bone
Greenblatt, M. B., Ono, N., Ayturk, U. M., Debnath, S., & Lalani, S. (2019) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3802 · Cited by 43
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Wang, K., Zhao, C., Xiang, S., Duan, K., Chen, X., Guo, X. L., & Sahu, S. K. (2022) · Plant Science · doi:10.1016/j.plantsci.2022.111535 · Cited by 41
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Choi, K., Raghupathy, N., & Churchill, G. A. (2019) · Nature Communications · doi:10.1038/s41467-019-13099-0 · Cited by 39
Siddique, K., Ager-Wick, E., Fontaine, R., Weltzien, F., & Henkel, C. V. (2021) · Scientific Data · doi:10.1038/s41597-021-01058-8 · Cited by 34
Embracing systems toxicology at single-cell resolution
Zhang, Q., Caudle, W. M., Pi, J., Bhattacharya, S., Andersen, M. E., Kaminski, N. E., & Conolly, R. B. (2019) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2019.04.003 · Cited by 37
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Mieth, B., Hockley, J. R. F., Görnitz, N., Vidovic, M. M.-C., Müller, K., Gutteridge, A., & Ziemek, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-56911-z · Cited by 31
Elucidating the cellular dynamics of the brain with single-cell RNA sequencing
Cardona‐Alberich, A., Tourbez, M., Pearce, S. F., & Sibley, C. R. (2021) · RNA Biology · doi:10.1080/15476286.2020.1870362 · Cited by 33
Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysisPreprint
Wagner, F., Barkley, D., & Yanai, I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/655365 · Cited by 32
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Wiedmeier, J. E., Noel, P., Lin, W., Hoff, D. D. V., & Han, H. (2019) · Cancer treatment and research · doi:10.1007/978-3-030-16391-4_9 · Cited by 31
Wrenzycki, C. (2018) · Reproduction in Domestic Animals · doi:10.1111/rda.13260 · Cited by 28
Tracking the origin, development, and differentiation of hematopoietic stem cells
Dharampuriya, P. R., Scapin, G., Wong, C., Wagner, K., Cillis, J. L., & Shah, D. I. (2017) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2018.01.002 · Cited by 26
David, F. P. A., Litovchenko, M., Deplancke, B., & Gardeux, V. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa412 · Cited by 28
Reconstructing complex lineage trees from scRNA-seq data using MERLoT
Parra, R. G., Παπαδόπουλος, Ν., Ahumada-Arranz, L., Kholtei, J. E., Mottelson, N., Horokhovsky, Y., Treutlein, B., & Söding, J. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz706 · Cited by 26
Klimm, F., Toledo, E. M., Monfeuga, T., Zhang, F., Deane, C. M., & Reinert, G. (2020) · BMC Genomics · doi:10.1186/s12864-020-07144-2 · Cited by 26
Single-Cell Analysis of the Transcriptome and Epigenome
Mazan-Mamczarz, K., Ha, J., De, S., & Sen, P. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1831-8_3 · 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
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Huang, J., Liu, L., Qin, L., Huang, H., & Li, X. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.686739 · Cited by 23
Delineating biological and technical variance in single cell expression data
Arzalluz-Luque, Á., Devailly, G., Mantsoki, A., & Joshi, A. (2017) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2017.07.006 · Cited by 21
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Merchant, J. P., Zhu, K., Henrion, M., Zaidi, S. S. A., Lau, B., Moein, S., Alamprese, M. L., Pearse, R. V., Bennett, D. A., Ertekin‐Taner, N., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-04791-5 · Cited by 23
K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis
Cottrell, S., Hozumi, Y., & Wei, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108497 · Cited by 24
Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications
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Wang, Y., Hoinka, J., & Przytycka, T. M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.05.007 · Cited by 17
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Erfanian, N., Heydari, A. A., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Nasseri, S., Safarpour, H., & Sahebkar, A. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.26.470166 · Cited by 17
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Single‐Cell Sequencing in Normal and Malignant Hematopoiesis
Wilson, N. K., & Göttgens, B. (2018) · HemaSphere · doi:10.1097/hs9.0000000000000034 · Cited by 13
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Vrahatis, A. G., Tasoulis, S. K., Maglogiannis, I. G., & Plagianakos, V. P. (2020) · Studies in computational intelligence · doi:10.1007/978-3-662-61114-2_5 · Cited by 11
Arteaga-Arteaga, H. B., Candamil-Cortés, M. S., Breaux, B., Guillen-Rondon, P., Orozco-Arias, S., & Tabares-Soto, R. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad002 · Cited by 12
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Kuppe, C., Perales-Patón, J., Sáez-Rodríguez, J., & Kramann, R. (2020) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfaa233 · Cited by 12
Zhao, Z., Wang, X., Schatten, H., & Sun, Q. (2021) · Reproductive Toxicology · doi:10.1016/j.reprotox.2021.12.003 · Cited by 12
Immune cell type ‘fingerprints’ at the basis of outcome diversity of human infection
Hen‐Avivi, S., & Avraham, R. (2017) · Current Opinion in Microbiology · doi:10.1016/j.mib.2017.09.012 · Cited by 10
Gorin, G., Vastola, J. J., Fang, M., & Pachter, L. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.09.06.459173 · Cited by 11
Malec, M., Kurban, H., & Dalkilic, M. M. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04814-8 · Cited by 10
Characterization of cell-fate decision landscapes by estimating transcription factor dynamics
Jiménez, S., Schreiber, V., Mercier, R., Gradwohl, G., & Molina, N. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100512 · Cited by 12
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Zhou, T., & Matsunami, H. (2017) · Cell and Tissue Research · doi:10.1007/s00441-017-2682-0 · Cited by 8
Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges
Talukdar, S., Chang, Z., Winterhoff, B. J. N., & Starr, T. K. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-73359-9_7 · Cited by 9
Zheng, X. K., Huang, Y., & Zou, X. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007471 · Cited by 9
Identification of non-cancer cells from cancer transcriptomic data
Bortolomeazzi, M., Keddar, M. R., Ciccarelli, F. D., & Benedetti, L. G. (2019) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2019.194445 · Cited by 10
Vidal, M., Fraga, M., Llerena, F., Vera, A., Hernández, M., Koch, E. S., Reyes‐López, F. E., Vallejos‐Vidal, E., Cabrera-Vives, G., & Nova‐Lamperti, E. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231911065 · Cited by 8
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Vrahatis, A. G., Tasoulis, S. K., Dimitrakopoulos, G. Ν., & Plagianakos, V. P. (2019) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2019.8791482 · Cited by 9
UICPC: Centrality-based clustering for scRNA-seq data analysis without user input
Chowdhury, H. A., Bhattacharyya, D. K., & Kalita, J. K. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104820 · Cited by 8
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Informing disease modelling with brain-relevant functional genomic annotations
Reynolds, R. H., Hardy, J., Ryten, M., & Taliun, S. A. G. (2019) · Brain · doi:10.1093/brain/awz295 · Cited by 8
Computational modelling in single-cell cancer genomics: methods and future directions
Zhang, A. W., & Campbell, K. R. (2020) · Physical Biology · doi:10.1088/1478-3975/abacfe · Cited by 7
Sánchez, P. A. G., & Elena, S. F. (2022) · Communications Biology · doi:10.1038/s42003-022-04253-4 · Cited by 8
Padrón-Manrique, C., Vázquez-Jiménez, A., Esquivel-Hernandez, D. A., Martínez-López, Y. E., Neri-Rosario, D., Paul, S.-C. J., Giron-Villalobos, D., & Reséndis-Antonio, O. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.09.495525 · Cited by 6
Single-cell insights into transcriptomic diversity in immunity
Miragaia, R. J., Teichmann, S. A., & Hagai, T. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.08.003 · Cited by 5
Self-citationscASK: A Novel Ensemble Framework for Classifying Cell Types Based on Single-cell RNA-seq Data
Liu, B., Wu, F., & Zou, X. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3050963 · Cited by 5
Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision
Xie, J., Yin, Y., Yang, F., Sun, J., & Wang, J. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00415-2 · Cited by 5
Fantastic beasts and how to sequence them: genomic approaches for obscure model organismsPreprint
Matz, M. V. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/165928 · Cited by 4
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Jeong, H., Jeong, H., Lee, S., & Jeong, W. (2023) · IEEE Pacific Visualization Symposium · doi:10.1109/pacificvis56936.2023.00013 · Cited by 3
Tan, Y. K., Zhang, X., & Chen, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/222372 · Cited by 3
Manifold Learning via Manifold DeflationPreprint
Ting, D., & Jordan, M. I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.03315 · Cited by 3
Zhou, J., Wang, Y., Bu, T., Zhang, S., Qiu, X., Chu, H., Wang, W., Wu, Y., Wang, W., Zhang, Y., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3619777 · Cited by 3
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Beken, B., Arcolacı, A., & Jiménez‐Saiz, R. (2020) · Allergy · doi:10.1111/all.14487 · Cited by 2
JEBIN: analyzing gene co-expressions across multiple datasets by joint network embedding
Wu, G., Li, X., Guo, W., Zheng, W., Hu, T., Shan, Y., & Gu, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab603 · Cited by 1
Single-Cell Transcriptomics: Technology and Applications
Fuchs, O. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-814919-5.00012-9 · Cited by 2
Sun, L., Wang, G., & Zhang, Z. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac590 · Cited by 2
Single-cell transcriptomic analysis of pancreatic islets in health and type 2 diabetes
Kumar, S., & Vinod, P. K. U. (2018) · International Journal of Advances in Engineering Sciences and Applied Mathematics · doi:10.1007/s12572-018-0239-4 · Cited by 1
A mathematical framework for raw counts of single-cell RNA-seq data analysisPreprint
Galfrè, S. G., & Morandin, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.02933 · Cited by 2
Approaches to Marker Gene Identification from Single-Cell RNA-Sequencing Data
Li, Y., Ma, W., & Qin, Z. S. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_4 · Cited by 1
Ai, B., Liang, Y., Yan, T., & Lei, Y. (2024) · Environmental Toxicology · doi:10.1002/tox.24200 · Cited by 1
Silva, J. M. F., Oteo, J. Á., Peña-Garay, C., & Elena, S. F. (2024) · PLOS complex systems. · doi:10.1371/journal.pcsy.0000016 · Cited by 1
Optimal Dimensionality Selection Using Hull Heatmaps for Single‐Cell Analysis
Jeong, H., Jeong, H., Lee, S., & Jeong, W. (2025) · Computer Graphics Forum · doi:10.1111/cgf.70151 · Cited by 2
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Echeagaray, O., & Sussman, M. A. (2019) · Regenerative Medicine · doi:10.2217/rme-2019-0063 · Cited by 1
Wang, H., Lian, Y., Li, C., Ma, Y., Yan, Z., & Dong, C. (2019) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720019500355
Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces
Vrahatis, A. G., Dimitrakopoulos, G. Ν., Tasoulis, S. K., & Plagianakos, V. P. (2019) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2019.00157
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Zhu, T., Zhou, J., Zhang, L., & Cao, Y. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983083
scBASE: A Bayesian mixture model for the analysis of allelic expression in single cellsPreprint
Choi, K., Raghupathy, N., & Churchill, G. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/383224
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Wang, C., Gao, Y.-L., Jiao, C.-N., Liu, J., Zheng, C.-H., & Kong, X.-Z. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313380
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Barmpas, P. T., Tasoulis, S. K., Vrahatis, A. G., Anagnostou, P., Georgakopoulos, S. V., Prina, M. A., Ayuso‐Mateos, J. L., Bickenbach, J. E., Bayes‐Marin, I., Bobák, M., et al. (2021) · medRxiv · doi:10.1101/2021.04.01.21254751
Metadata
- Authors
- Rostom, R., Svensson, V., Teichmann, S. A., & Kar, G.
- Affiliations
- Wellcome Sanger Institute (GB)
- European Bioinformatics Institute (GB)
- Corresponding author
- Kar, Gozde
- Venue
- FEBS Letters
- Publisher
- Wiley
- Published
- 2017-06-12
- Volume
- 591
- Issue
- 15
- Pages
- 2213-2225
- ISSN
- 0014-5793 (ISSN-L)
- 1873-3468
- PMC
- PMC5575496
- OpenAlex
- W2615816755
- Cited by
- 107
- References
- 89
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 18 · 2021: 14 · 2020: 19 · 2019: 20 · 2018: 9 · 2017: 11
- FWCI
- 1.51
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- FEBS Letters (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, scRNA-seq data analysis, cell clustering, gene expression analysis, high-dimensional data mining
- MeSH terms
- Animals, Humans, Gene Expression, Cluster Analysis, Sequence Analysis, RNA, Computational Biology, Single-Cell Analysis
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