Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation
Open accessAbstract
Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution.
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?
Low numbers of reads in peak versus non-peak regions indicate low signal-to-noise ratios, similar to TSS scores.
“Moreover, low numbers of reads in peak versus non-peak regions are indicators for low signal-to-noise ratios similar to TSS scores9.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [9]
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
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548 · 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
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · 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
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
Model-based Analysis of ChIP-Seq (MACS)
Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-9-r137 · Cited by 18,386 · see citing works
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453 · see citing works
Robinson, J. T., Thorvaldsdóttir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G., & Mesirov, J. P. (2011) · Nature Biotechnology · doi:10.1038/nbt.1754 · Cited by 15,390 · see citing works
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382 · see citing works
Fast, sensitive and accurate integration of single-cell data with Harmony
Korsunsky, I., Millard, N., Fan, J., Slowikowski, K., Zhang, F., Wei, K., Baglaenko, Y., Brenner, M., Loh, P.-R., & Raychaudhuri, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0619-0 · Cited by 10,488 · see citing works
The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400 · see citing works
deepTools2: a next generation web server for deep-sequencing data analysis
Ramírez, F., Ryan, D. P., Grüning, B., Bhardwaj, V., Kilpert, F., Richter, A. S., Heyne, S., Dündar, F., & Manke, T. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw257 · Cited by 8,096 · see citing works
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 · 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
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422 · see citing works
Dimensionality reduction for visualizing single-cell data using UMAP
Becht, E., McInnes, L., Healy, J., Dutertre, C.-A., Kwok, I. W. H., Ng, L. G., Ginhoux, F., & Newell, E. W. (2019) · Nature Biotechnology · doi:10.1038/nbt.4314 · Cited by 5,358 · see citing works
GREAT improves functional interpretation of cis-regulatory regions
McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., & Bejerano, G. (2010) · Nature Biotechnology · doi:10.1038/nbt.1630 · Cited by 4,502 · 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
An Expanded View of Complex Traits: From Polygenic to Omnigenic
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017) · Cell · doi:10.1016/j.cell.2017.05.038 · Cited by 2,851 · see citing works
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 · see citing works
Single-cell chromatin accessibility reveals principles of regulatory variation
Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works
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 · see citing works
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 1,793 · 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
chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data
Schep, A. N., Wu, B., Buenrostro, J. D., & Greenleaf, W. J. (2017) · Nature Methods · doi:10.1038/nmeth.4401 · Cited by 1,680 · see citing works
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis
Granja, J. M., Corces, M. R., Pierce, S. E., Bagdatli, S. T., Choudhry, H., Chang, H. Y., & Greenleaf, W. J. (2021) · Nature Genetics · doi:10.1038/s41588-021-00790-6 · Cited by 1,392 · see citing works
Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · see citing works
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works
Corces, M. R., Buenrostro, J. D., Wu, B., Greenside, P. G., Chan, S. M., Koenig, J. L., Snyder, M. P., Pritchard, J. K., Kundaje, A., Greenleaf, W. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3646 · Cited by 1,151 · see citing works
A rapid and robust method for single cell chromatin accessibility profiling
Chen, X., Miragaia, R. J., Natarajan, K. N., & Teichmann, S. A. (2018) · Nature Communications · doi:10.1038/s41467-018-07771-0 · Cited by 1,147 · see citing works
The art of using t-SNE for single-cell transcriptomics
Kobak, D., & Berens, P. (2019) · Nature Communications · doi:10.1038/s41467-019-13056-x · Cited by 1,118 · see citing works
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113 · see citing works
Long-range enhancer–promoter contacts in gene expression control
Schoenfelder, S., & Fraser, P. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0128-0 · Cited by 1,102 · cited 1× · see citing works
“Proximal and distal interactions of enhancers with other regulatory elements have been identified by analyzing 3D structures of chromatin [79].”
BackgroundVISTA Enhancer Browser—a database of tissue-specific human enhancers
Visel, A., Minovitsky, S., Dubchak, I., & Pennacchio, L. A. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkl822 · Cited by 1,080 · see citing works
The selection and function of cell type-specific enhancers
Heinz, S., Romanoski, C. E., Benner, C., & Glass, C. K. (2015) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3949 · Cited by 1,010 · see citing works
Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., Olsen, B. N., Mumbach, M. R., Pierce, S. E., Corces, M. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0206-z · Cited by 980 · see citing works
Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Cao, J., Cusanovich, D. A., Ramani, V., Aghamirzaie, D., Pliner, H. A., Hill, A. J., Daza, R. M., McFaline-Figueroa, J. L., Packer, J. S., Christiansen, L., et al. (2018) · Science · doi:10.1126/science.aau0730 · Cited by 938 · see citing works
Mathelier, A., Fornes, O., Arenillas, D. J., Chen, C.-Y., Denay, G., Lee, J., Shi, W., Shyr, C., Tan, G., Worsley-Hunt, R., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1176 · Cited by 937 · see citing works
Visualizing Genomic Data Using Gviz and Bioconductor
Hahne, F., & Ivánek, R. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3578-9_16 · Cited by 893 · see citing works
High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
Chen, S., Lake, B. B., & Zhang, K. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0290-0 · Cited by 810 · see citing works
Cicero predicts cis-regulatory DNA interactions from single-cell chromatin accessibility data
Pliner, H. A., Packer, J. S., McFaline-Figueroa, J. L., Cusanovich, D. A., Daza, R. M., Aghamirzaie, D., Srivatsan, S., Qiu, X., Jackson, D., Minkina, A., et al. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.06.044 · Cited by 789 · see citing works
Organoid single-cell genomic atlas uncovers human-specific features of brain development
Kanton, S., Boyle, M. J., He, Z., Santel, M., Weigert, A., Sanchís-Calleja, F., Guijarro, P., Sidow, L., Fleck, J. S., Han, D., et al. (2019) · Nature · doi:10.1038/s41586-019-1654-9 · Cited by 761 · see citing works
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility
Cusanovich, D. A., Hill, A. J., Aghamirzaie, D., Daza, R. M., Pliner, H. A., Berletch, J. B., Filippova, G. N., Huang, X., Christiansen, L., DeWitt, W. S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.052 · Cited by 759 · see citing works
Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data
Valouev, A., Johnson, D. S., Sundquist, A., Medina, C., Anton, E., Batzoglou, S., Myers, R. M., & Sidow, A. (2008) · Nature Methods · doi:10.1038/nmeth.1246 · Cited by 695 · 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
Supervised classification enables rapid annotation of cell atlases
Pliner, H. A., Shendure, J., & Trapnell, C. (2019) · doi:10.1101/538652 · Cited by 498 · see citing works
Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia
Granja, J. M., Klemm, S., McGinnis, L. M., Kathiria, A. S., Mezger, A., Corces, M. R., Parks, B., Gars, E., Liedtke, M., Zheng, G. X. Y., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0332-7 · Cited by 498 · see citing works
cisTopic: cis-regulatory topic modeling on single-cell ATAC-seq data
Bravo González-Blas, C., Minnoye, L., Papasokrati, D., Aibar, S., Hulselmans, G., Christiaens, V., Davie, K., Wouters, J., & Aerts, S. (2019) · Nature Methods · doi:10.1038/s41592-019-0367-1 · Cited by 456 · see citing works
Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility
Lareau, C. A., Duarte, F. M., Chew, J. G., Kartha, V. K., Burkett, Z. D., Kohlway, A. S., Pokholok, D., Aryee, M. J., Steemers, F. J., Lebofsky, R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0147-6 · Cited by 444 · see citing works
Genome-scale functional characterization of Drosophila developmental enhancers in vivo
Kvon, E. Z., Kazmar, T., Stampfel, G., Yáñez-Cuna, J. O., Pagani, M., Schernhuber, K., Dickson, B. J., & Stark, A. (2014) · Nature · doi:10.1038/nature13395 · Cited by 436 · see citing works
Super-enhancers delineate disease-associated regulatory nodes in T cells
Vahedi, G., Kanno, Y., Furumoto, Y., Jiang, K., Parker, S. C. J., Erdos, M. R., Davis, S. R., Roychoudhuri, R., Restifo, N. P., Gadina, M., et al. (2015) · Nature · doi:10.1038/nature14154 · Cited by 369 · see citing works
The cis-regulatory dynamics of embryonic development at single-cell resolution
Cusanovich, D. A., Reddington, J. P., Garfield, D. A., Daza, R. M., Aghamirzaie, D., Marco-Ferreres, R., Pliner, H. A., Christiansen, L., Qiu, X., Steemers, F. J., et al. (2018) · Nature · doi:10.1038/nature25981 · Cited by 363 · see citing works
Corces, M. R., Shcherbina, A., Kundu, S., Gloudemans, M. J., Frésard, L., Granja, J. M., Louie, B. H., Eulalio, T., Shams, S., Bagdatli, S. T., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00721-x · Cited by 358 · see citing works
Preissl, S., Fang, R., Huang, H., Zhao, Y., Raviram, R., Gorkin, D. U., Zhang, Y., Sos, B. C., Afzal, V., Dickel, D. E., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0079-3 · Cited by 332 · see citing works
Assessment of computational methods for the analysis of single-cell ATAC-seq data
Chen, H., Lareau, C., Andreani, T., Vinyard, M. E., Garcia, S. P., Clement, K., Andrade-Navarro, M. A., Buenrostro, J. D., & Pinello, L. (2019) · Genome Biology · doi:10.1186/s13059-019-1854-5 · Cited by 332 · see citing works
Rubin, A. J., Parker, K. R., Satpathy, A. T., Qi, Y., Wu, B., Ong, A. J., Mumbach, M. R., Ji, A. L., Kim, D. S., Cho, S. W., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.022 · Cited by 314 · see citing works
Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM
Chen, H., Albergante, L., Hsu, J. Y., Lareau, C. A., Bosco, G. L., Guan, J., Zhou, S., Gorban, A. N., Bauer, D. E., Aryee, M. J., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09670-4 · Cited by 284 · see citing works
Statistical significance of variables driving systematic variation in high-dimensional data
Chung, N. C., & Storey, J. D. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu674 · Cited by 247 · see citing works
Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity
Liu, L., Liu, C., Quintero, A., Wu, L., Yuan, Y., Wang, M., Cheng, M., Leng, L., Xu, L., Dong, G., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08205-7 · Cited by 243 · see citing works
Predicting cell-type–specific gene expression from regions of open chromatinPreprint
Natarajan, A., Yardımcı, G. G., Sheffield, N. C., Crawford, G. E., & Ohler, U. (2012) · Genome Research · doi:10.1101/gr.135129.111 · Cited by 242 · see citing works
Interrogation of human hematopoiesis at single-cell and single-variant resolution
Ulirsch, J. C., Lareau, C. A., Bao, E. L., Ludwig, L. S., Guo, M. H., Benner, C., Satpathy, A. T., Kartha, V. K., Salem, R. M., Hirschhorn, J. N., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0362-6 · Cited by 208 · see citing works
SCALE method for single-cell ATAC-seq analysis via latent feature extraction
Xiong, L., Xu, K., Tian, K., Shao, Y., Tang, L., Gao, G., Zhang, M., Jiang, T., & Zhang, Q. C. (2019) · Nature Communications · doi:10.1038/s41467-019-12630-7 · Cited by 206 · see citing works
Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations
Duren, Z., Chen, X. X., Zamanighomi, M., Zeng, W., Satpathy, A. T., Chang, H. Y., Wang, Y., & Wong, W. H. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1805681115 · Cited by 201 · see citing works
Jia, G., Preussner, J., Chen, X., Guenther, S., Yuan, X.-J., Yekelchyk, M., Kuenne, C., Looso, M., Zhou, Y., Teichmann, S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07307-6 · Cited by 197 · see citing works
Developmental trajectory of prehematopoietic stem cell formation from endothelium
Zhu, Q., Gao, P., Tober, J. M., Bennett, L. F., Chen, C., Uzun, Y., Li, Y., Howell, E. D., Mumau, M. D., Yu, W., et al. (2020) · Blood · doi:10.1182/blood.2020004801 · Cited by 163 · see citing works
Transcript-indexed ATAC-seq for precision immune profiling
Satpathy, A. T., Saligrama, N., Buenrostro, J. D., Wei, Y., Wu, B., Rubin, A. J., Granja, J. M., Lareau, C. A., Li, R., Qi, Y., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0008-8 · Cited by 152 · see citing works
Schmidt, E. M., Zhang, J., Zhou, W., Chen, J., Mohlke, K. L., Chen, Y., & Willer, C. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv201 · Cited by 150 · see citing works
High-throughput chromatin accessibility profiling at single-cell resolution
Mezger, A., Klemm, S. L., Mann, I. K., Brower, K. K., Mir, A. A., Bostick, M. L., Farmer, A. A., Fordyce, P. M., Linnarsson, S., & Greenleaf, W. J. (2018) · Nature Communications · doi:10.1038/s41467-018-05887-x · Cited by 149 · see citing works
Rai, V., Quang, D. X., Erdos, M. R., Cusanovich, D. A., Daza, R. M., Narisu, N., Zou, L. S., Didion, J. P., Guan, Y., Shendure, J., et al. (2019) · Molecular Metabolism · doi:10.1016/j.molmet.2019.12.006 · Cited by 141 · see citing works
EpiScanpy: integrated single-cell epigenomic analysis
Danese, A., Richter, M. L., Chaichoompu, K., Fischer, D. S., Theis, F. J., & Colomé‐Tatché, M. (2021) · Nature Communications · doi:10.1038/s41467-021-25131-3 · Cited by 131 · see citing works
Benchmarking atlas-level data integration in single-cell genomicsPreprint
Luecken, M. D., Büttner, M., Chaichoompu, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.111161 · Cited by 122 · see citing works
Unsupervised clustering and epigenetic classification of single cells
Zamanighomi, M., Lin, Z., Daley, T., Chen, X. X., Duren, Z., Schep, A. N., Greenleaf, W. J., & Wong, W. H. (2018) · Nature Communications · doi:10.1038/s41467-018-04629-3 · Cited by 120 · see citing works
Chung, C., Ma, Z., Dravis, C., Preißl, S., Poirion, O., Luna, G., Hou, X., Giraddi, R. R., Ren, B., & Wahl, G. M. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.089 · Cited by 105 · see citing works
Dimensionality Reduction Via Graph Structure Learning
Mao, Q., Wang, L., Steve Goodison, B., & Sun, Y. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783309 · Cited by 104 · see citing works
EpiScanpy: integrated single-cell epigenomic analysisPreprint
Danese, A., Richter, M., Fischer, D. S., Theis, F. J., & Colomé-Tatché, M. (2019) · bioRxiv · doi:10.1101/648097 · Cited by 88 · see citing works
Bravo González‐Blas, C., Quan, X., Duran‐Romaña, R., Taskiran, I. I., Koldere, D., Davie, K., Christiaens, V., Makhzami, S., Hulselmans, G., de Waegeneer, M., et al. (2020) · Molecular Systems Biology · doi:10.15252/msb.20209438 · Cited by 88 · see citing works
Nucleome Dynamics during Retinal Development
Norrie, J. L., Lupo, M. S., Xu, B., Aldiri, I., Valentine, M. B., Putnam, D. K., Griffiths, L. M., Zhang, J., Johnson, D. A., Easton, J. B., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.08.002 · Cited by 87 · see citing works
Chen, X., Litzenburger, U. M., Wei, Y., Schep, A. N., LaGory, E. L., Choudhry, H., Giaccia, A. J., Greenleaf, W. J., & Chang, H. Y. (2018) · Nature Communications · doi:10.1038/s41467-018-07115-y · Cited by 80 · see citing works
UROPA: a tool for Universal RObust Peak Annotation
Kondili, M., Fust, A., Preussner, J., Kuenne, C. T., Braun, T. W., & Looso, M. (2017) · Scientific Reports · doi:10.1038/s41598-017-02464-y · Cited by 77 · see citing works
Jointly embedding multiple single-cell omics measurementsPreprint
Liu, J., Huang, Y., Singh, R., Vert, J., & Noble, W. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/644310 · Cited by 73 · see citing works
Parallel bimodal single-cell sequencing of transcriptome and chromatin accessibilityPreprint
Xing, Q. R., Farran, C. A. E., Zeng, Y. Y., Yi, Y., Warrier, T., Gautam, P., Collins, J. J., Xu, J., Dröge, P., Koh, C.-G., et al. (2020) · Genome Research · doi:10.1101/gr.257840.119 · Cited by 72 · see citing works
The accessible chromatin landscape of the murine hippocampus at single-cell resolutionPreprint
Sinnamon, J. R., Torkenczy, K. A., Linhoff, M. W., Vitak, S. A., Mulqueen, R. M., Pliner, H. A., Trapnell, C., Steemers, F. J., Mandel, G., & Adey, A. (2019) · Genome Research · doi:10.1101/gr.243725.118 · Cited by 70 · see citing works
REDfly: the transcriptional regulatory element database for Drosophila
Rivera, J., Keränen, S. V. E., Gallo, S. M., & Halfon, M. S. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky957 · Cited by 68 · see citing works
Jointly Embedding Multiple Single-Cell Omics Measurements
Liu, J., Huang, Y., Singh, R., Vert, J., & Noble, W. S. (2019) · PubMed · doi:10.4230/lipics.wabi.2019.10 · Cited by 67 · see citing works
Classifying cells with Scasat, a single-cell ATAC-seq analysis tool
Baker, S. M., Rogerson, C., Hayes, A., Sharrocks, A. D., & Rattray, M. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky950 · Cited by 66 · see citing works
ArchR: An integrative and scalable software package for single-cell chromatin accessibility analysisPreprint
Granja, J. M., Corces, M. R., Pierce, S. E., et al. (2020) · bioRxiv · doi:10.1101/2020.04.28.066498 · Cited by 61 · see citing works
scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data
Yu, W., Uzun, Y., Zhu, Q., Chen, C., & Tan, K. (2020) · Genome biology · doi:10.1186/s13059-020-02008-0 · Cited by 60 · see citing works
DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data
Zeng, W.-W., Chen, X., Duren, Z., Wang, Y., Jiang, R., & Wong, W. H. (2019) · Nature Communications · doi:10.1038/s41467-019-12547-1 · Cited by 59 · see citing works
Single-cell regulome data analysis by SCRAT
Ji, Z., Zhou, W., & Ji, H. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx315 · Cited by 55 · see citing works
BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization
de Boer, C. G., & Regev, A. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2255-6 · Cited by 48 · see citing works
Simultaneous Profiling of Gene Expression and Chromatin Accessibility in Single Cells
Reyes, M., Billman, K., Hacohen, N., & Blainey, P. C. (2019) · Advanced Biosystems · doi:10.1002/adbi.201900065 · Cited by 48 · see citing works
Fogarty, M. P., Panhuis, T. M., Vadlamudi, S., Buchkovich, M. L., & Mohlke, K. L. (2013) · Diabetes · doi:10.2337/db12-0972 · Cited by 42 · see citing works
Single cell epigenomic atlas of the developing human brain and organoids
Ziffra, R. S., Kim, C. N., Wilfert, A., Turner, T. N., Haeussler, M., Casella, A. M., Przytycki, P. F., Kreimer, A., Pollard, K. S., Ament, S. A., et al. (2019) · doi:10.1101/2019.12.30.891549 · Cited by 33 · see citing works
Zhao, C., Hu, S. S., Huo, X., & Zhang, Y. (2017) · PLoS ONE · doi:10.1371/journal.pone.0180583 · Cited by 28 · see citing works
Destin: toolkit for single-cell analysis of chromatin accessibility
Urrutia, E., Chen, L., Zhou, H., & Jiang, Y. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz141 · Cited by 24 · see citing works
González‐Blas, C. B., Quan, X., Duran‐Romaña, R., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.19.882381 · Cited by 17 · see citing works
Epigenetic landscape and AAV targeting of human neocortical cell classes
Mich, J. K., & Gradinaru, V. (2019) · CaltechAUTHORS (California Institute of Technology) · Cited by 15 · see citing works
Kanton, S., Boyle, M. J., He, Z., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/685057 · Cited by 15 · see citing works
Open chromatin and diabetes risk
Groop, L. (2010) · Nature Genetics · doi:10.1038/ng0310-190 · Cited by 13 · see citing works
Single-cell epigenomic identification of inherited risk loci in Alzheimer’s and Parkinson’s diseasePreprint
Corces, M., Shcherbina, A., Kundu, S., Gloudemans, M., Frésard, L., Granja, J. M., Louie, B. H., Shams, S., Bagdatli, S., Mumbach, M. R., et al. (2020) · bioRxiv · doi:10.1101/2020.01.06.896159 · Cited by 13 · see citing works
Improved single-cell ATAC-seq reveals chromatin dynamics of in vitro corticogenesisPreprint
Mulqueen, R. M., DeRosa, B. A., Thornton, C. A., Sayar, Z., Torkenczy, K. A., Fields, A. J., Wright, K. M., Nan, X., Ramji, R., Steemers, F. J., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/637256 · Cited by 12 · see citing works
Parallel Bimodal Single-cell Sequencing of Transcriptome and Chromatin AccessibilityPreprint
Xing, Q. R., Farran, C. A. E., Yi, Y., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/829960 · Cited by 11 · see citing works
Flagged first, then by citations
Research works printed but not linked: 5
- [23]Fang R, Preissl S, Hou X, Lucero J, Wang X, Motamedi A, Shiau AK, Mukamel EA, Zhang Y, Behrens MM, et al. Fast and Accurate Clustering of Single Cell Epigenomes Reveals Cis-Regulatory Elements in Rare Cell Types. bioRxiv 2019:615179.
- [39]Schep A. motifmatchr: Fast Motif Matching in R R package version 1.10.0. edition; 2020.
- [50]Przytycki PF, Pollard KS. Semi-supervised identification of cell populations in single-cell ATAC-seq. bioRxiv 2019:847657.
- [61]Tangherloni A, Ricciuti F, Besozzi D, Liò P, Cvejic A. Single cell ATAC-seq identifies broad changes in neuronal abundance and chromatin accessibility in Down Syndrome. bioRxiv 2019:727867.
- [84]Graybuck LT, Daigle TL, Sedeño-Cortés AE, Walker M, Kalmbach B, Lenz GH, Nguyen TN, Garren E, Kim TK, Siverts LA, et al. Prospective, brain-wide labeling of neuronal subclasses with enhancer-driven AAVs. bioRxiv 2020:525014.
Cited by
Sorted by citations
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
Human disease models in drug development
Loewa, A., Feng, J. J., & Hedtrich, S. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00063-3 · Cited by 331 · see citing works
Chromatin accessibility profiling methods
Minnoye, L., Marinov, G. K., Krausgruber, T., Pan, L., Marand, A. P., Secchia, S., Greenleaf, W. J., Furlong, E. E. M., Zhao, K., Schmitz, R. J., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00008-9 · Cited by 206 · see citing works
A fast, scalable and versatile tool for analysis of single-cell omics data
Zhang, K., Zemke, N. R., Armand, E. J., & Ren, B. (2024) · Nature Methods · doi:10.1038/s41592-023-02139-9 · Cited by 185 · see citing works
Deep learning applications in single-cell genomics and transcriptomics data analysis
Erfanian, N., Heydari, A. A., Feriz, A. M., Picazo, P. I., Derakhshani, A., GhasemiGol, M., Farahpour, M., Razavi, S. M., Nasseri, S., Safarpour, H., et al. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.115077 · Cited by 162 · see citing works
‘Multi-omics’ data integration: applications in probiotics studies
Kwoji, I. D., Aiyegoro, O. A., Okpeku, M., & Adeleke, M. A. (2023) · npj Science of Food · doi:10.1038/s41538-023-00199-x · Cited by 138 · see citing works
Heinrich, S., Craig, A. J., Ma, L., Heinrich, B., Greten, T. F., & Wang, X. W. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.11.036 · Cited by 104 · see citing works
Contemporary perspectives on heterotopic ossification
Hwang, C. D., Pagani, C. A., Nunez, J. H., Cherief, M., Qin, Q., Gomez-Salazar, M. A., Kadaikal, B., Kang, H., Chowdary, A. R., Patel, N., et al. (2022) · JCI Insight · doi:10.1172/jci.insight.158996 · Cited by 108 · see citing works
scAAGA: Single cell data analysis framework using asymmetric autoencoder with gene attention
Meng, R., Yin, S., Sun, J., Hu, H., & Zhao, Q. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107414 · Cited by 100 · see citing works
Single-cell immunology: Past, present, and future
Ginhoux, F., Yalin, A., Dutertre, C., & Amit, I. (2022) · Immunity · doi:10.1016/j.immuni.2022.02.006 · Cited by 93 · see citing works
Epigenomic heterogeneity as a source of tumour evolution
Laisné, M., Lupien, M., & Vallot, C. (2024) · Nature reviews. Cancer · doi:10.1038/s41568-024-00757-9 · Cited by 89 · see citing works
Deep learning in spatially resolved transcriptomics: a comprehensive technical view
Zahedi, R., Ghamsari, M. R. E., Argha, A., MacPhillamy, C., Beheshti, A., Alizadehsani, R., Lovell, N. H., Lotfollahi, M., & Alinejad‐Rokny, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae082 · Cited by 74 · see citing works
Artificial intelligence-driven biomedical genomics
Guo, K., Wu, M., Soo, Z., Yang, Y. Y., Zhang, Y., Zhang, Q., Lin, H., Grosser, M., Venter, D. J., Zhang, G., et al. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110937 · Cited by 61 · see citing works
Bibliometric review of ATAC-Seq and its application in gene expression
Luo, L.-H., Gribskov, M., & Wang, S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac061 · Cited by 58 · see citing works
Zhang, B., Upadhyay, R., Hao, Y., Samanovic, M. I., Herati, R. S., Blair, J. D., Axelrad, J. E., Mulligan, M. J., Littman, D. R., & Satija, R. (2023) · Nature Immunology · doi:10.1038/s41590-023-01608-9 · Cited by 61 · see citing works
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 · see citing works
Epigenetic remodeling of the immune landscape in cancer: therapeutic hurdles and opportunities
Tien, F., Lu, H., Lin, S., & Tsai, H.-C. (2023) · Journal of Biomedical Science · doi:10.1186/s12929-022-00893-0 · Cited by 50 · see citing works
Intricacies of single-cell multi-omics data integration
Rautenstrauch, P., Vlot, A. H. C., Saran, S., & Ohler, U. (2021) · Trends in Genetics · doi:10.1016/j.tig.2021.08.012 · Cited by 47 · see citing works
Single-cell foundation models: bringing artificial intelligence into cell biology
Baek, S., Song, K., & Lee, I. (2025) · Experimental & Molecular Medicine · doi:10.1038/s12276-025-01547-5 · Cited by 46 · see citing works
Self-citationPatty, B. J., & Hainer, S. J. (2021) · Nature Protocols · doi:10.1038/s41596-021-00516-2 · Cited by 42 · see citing works
Single-cell RNA sequencing in skeletal muscle developmental biology
Cai, C., Yue, Y., & Yue, B. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114631 · Cited by 43 · see citing works
Artificial intelligence and machine learning applications for cultured meat
Todhunter, M. E., Jubair, S., Verma, R., Saqe, R., Shen, K., & Duffy, B. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1424012 · Cited by 46 · see citing works
AgeAnno: a knowledgebase of single-cell annotation of aging in human
Huang, K., Gong, H., Guan, J., Zhang, L., Hu, C., Zhao, W., Huang, L., Zhang, W., Kim, P., & Zhou, X. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac847 · Cited by 42 · see citing works
Loers, J. U., & Vermeirssen, V. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae382 · Cited by 33 · see citing works
Gao, W. N. D., Gallardo-Dodd, C. J., & Kutter, C. (2021) · Genome Research · doi:10.1101/gr.275944.121 · Cited by 29 · see citing works
Ontogenetic rules for the molecular diversification of hypothalamic neurons
Benevento, M., Hökfelt, T. G. M., & Harkany, T. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00615-3 · Cited by 28 · see citing works
Single-cell multiomics decodes regulatory programs for mouse secondary palate development
Yan, F., Suzuki, A., Iwaya, C., Pei, G., Chen, X., Yoshioka, H., Yu, M., Simon, L. M., Iwata, J., & Zhao, Z. (2024) · Nature Communications · doi:10.1038/s41467-024-45199-x · Cited by 27 · see citing works
B-Cell Receptor Repertoire: Recent Advances in Autoimmune Diseases
Wang, Q., Feng, D., Jia, S., Lu, Q., & Zhao, M. (2024) · Clinical Reviews in Allergy & Immunology · doi:10.1007/s12016-024-08984-6 · Cited by 26 · see citing works
Whole genome deconvolution unveils Alzheimer’s resilient epigenetic signature
Berson, E., Sreenivas, A., Phongpreecha, T., Perna, A., Grandi, F. C., Xue, L., Ravindra, N. G., Payrovnaziri, S. N., Mataraso, S. J., Kim, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40611-4 · Cited by 25 · see citing works
Sun, G., Fu, X., Zheng, Y., Hong, G., Liu, Z., Luo, B., Lei, J., Lv, D., Chang, M., Xiao, Y., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00896-0 · Cited by 28 · see citing works
Tang, S., Cui, X., Wang, R., Li, S., Li, S., Huang, X., & Chen, S. (2024) · Nature Communications · doi:10.1038/s41467-024-46045-w · Cited by 23 · see citing works
Adipose tissue macrophage heterogeneity in the single-cell genomics era
Kang, H., & Lee, J. (2024) · Molecules and Cells · doi:10.1016/j.mocell.2024.100031 · Cited by 23 · see citing works
Computational Methods for Single-Cell DNA Methylome Analysis
Iqbal, W., & Zhou, W. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.05.007 · Cited by 23 · see citing works
Cao, Y., Lin, Y., Patrick, E., Yang, P., & Yang, J. Y. H. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac590 · Cited by 21 · see citing works
Amodio, M., Youlten, S. E., Venkat, A., Juan, B. P. S., Chaffer, C. L., & Krishnaswamy, S. (2022) · Patterns · doi:10.1016/j.patter.2022.100577 · Cited by 21 · see citing works
Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits
Minow, M. A. A., Marand, A. P., & Schmitz, R. J. (2023) · Annual Review of Genetics · doi:10.1146/annurev-genet-022123-110824 · Cited by 21 · see citing works
Feng, L., Si, J., Yue, J., Zhao, M., Qi, W., Zhu, S., Mo, J., Wang, L., Lan, G., & Liang, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076413 · Cited by 19 · see citing works
Hu, H., & Quon, G. (2024) · Nature Communications · doi:10.1038/s41467-024-53971-2 · Cited by 18 · see citing works
Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution
Marín‐Sedeño, E., de Morentin, X. M., Pérez-Pomares, J. M., Gómez-Cabrero, D., & Ruiz‐Villalba, A. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.645276 · Cited by 18 · see citing works
Fundamental and practical approaches for single-cell ATAC-seq analysis
Shi, P., Nie, Y., Yang, J., Zhang, W., Tang, Z., & Xu, J. (2022) · aBIOTECH · doi:10.1007/s42994-022-00082-5 · Cited by 18 · see citing works
Considerations for reproducible omics in aging research
Singh, P. P., & Benayoun, B. A. (2023) · Nature Aging · doi:10.1038/s43587-023-00448-4 · Cited by 18 · see citing works
Building and analyzing metacells in single-cell genomics data
Bilous, M., Hérault, L., Gabriel, A. A., Teleman, M., & Gfeller, D. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00045-6 · Cited by 18 · see citing works
Deep Learning Applications in Single-Cell Omics Data AnalysisPreprint
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 · see citing works
Regner, M. J., García‐Recio, S., Thennavan, A., Wiśniewska, K., Méndez-Giráldez, R., Felsheim, B., Spanheimer, P. M., Parker, J. S., Perou, C. M., & Franco, H. L. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100765 · Cited by 19 · see citing works
Alsaigh, T., Bartolo, B. A. D., Mulangala, J., Figtree, G. A., & Leeper, N. J. (2021) · Circulation Research · doi:10.1161/circresaha.121.318265 · Cited by 15 · see citing works
scATAC-seq generates more accurate and complete regulatory maps than bulk ATAC-seq
Gur, E., & Hughes, J. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-87351-7 · Cited by 14 · see citing works
Filtering the Junk: Assigning Function to the Mosquito Non-Coding Genome
Farley, E. J., Eggleston, H., & Riehle, M. M. (2021) · Insects · doi:10.3390/insects12020186 · Cited by 14 · see citing works
A multi-omics approach to visualize early neuronal differentiation from hESCs in 4D
SAMARA, A. A., Spildrejorde, M., Sharma, A., Falck, M., Leithaug, M., Modafferi, S., Bjørnstad, P. M., Acharya, G., Gervin, K., Lyle, R., et al. (2022) · iScience · doi:10.1016/j.isci.2022.105279 · Cited by 14 · see citing works
Dimensionality reduction for visualizing high-dimensional biological data
Malepathirana, T., Senanayake, D., Vidanaarachchi, R., Gautam, V., & Halgamuge, S. K. (2022) · Biosystems · doi:10.1016/j.biosystems.2022.104749 · Cited by 13 · see citing works
Millard, N., Chen, J. H., Palshikar, M. G., Pelka, K., Spurrell, M., Price, C. O., He, J., Hacohen, N., Raychaudhuri, S., & Korsunsky, I. (2025) · Genome biology · doi:10.1186/s13059-025-03479-9 · Cited by 15 · see citing works
scMoC: single-cell multi-omics clustering
Eltager, M., Abdelaal, T., Mahfouz, A. E., & Reinders, M. J. T. (2022) · Bioinformatics Advances · doi:10.1093/bioadv/vbac011 · Cited by 13 · see citing works
SnapATAC2: a fast, scalable and versatile tool for analysis of single-cell omics dataPreprint
Zhang, K., Zemke, N. R., Armand, E. J., & Ren, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.11.557221 · Cited by 13 · see citing works
Sharma, A., Akshay, A., Rogne, M., & Eskeland, R. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab680 · Cited by 12 · see citing works
Exploring specialized metabolic pathways in medicinal plants with single-cell and spatial omics
Guo, L., Cai, M., Liao, X., Ma, G., & Fan, X. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.12.023 · Cited by 10 · see citing works
Wang, Z., Zhang, Y., Yu, Y., Zhang, J., Liu, Y., & Zou, Q. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24054784 · Cited by 11 · see citing works
Park, H. J., & Jung, H. (2022) · Animal Cells and Systems · doi:10.1080/19768354.2022.2110937 · Cited by 11 · see citing works
AtacAnnoR: a reference-based annotation tool for single cell ATAC-seq data
Tian, L., Xie, Y., Xie, Z., Tian, J., & Tian, W. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad268 · Cited by 10 · see citing works
A hitchhiker's guide to single‐cell epigenomics: Methods and applications for cancer research
Moreno‐Gonzalez, M., Sierra, I., & Kind, J. (2024) · International Journal of Cancer · doi:10.1002/ijc.35307 · Cited by 10 · see citing works
Interrogating the regulatory epigenome of cellular senescence
Thanos, D.-F., Ntintas, O. A., Athanasiadis, E. I., Papaspyropoulos, A., Petty, R., & Gorgoulis, V. G. (2025) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-025-05848-w · Cited by 10 · see citing works
Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies
Caligola, S., Sanctis, F. D., Canè, S., & Ugel, S. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.867880 · Cited by 10 · see citing works
Hu, K., Liu, H., Lawson, N. D., & Zhu, L. J. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.981859 · Cited by 10 · see citing works
Annotating the Insect Regulatory Genome
Asma, H., & Halfon, M. S. (2021) · Insects · doi:10.3390/insects12070591 · Cited by 9 · see citing works
Khandibharad, S., Kharat, K., & Singh, S. (2024) · Microbiology Spectrum · doi:10.1128/spectrum.03478-23 · Cited by 9 · see citing works
A review of single-cell transcriptomics and epigenomics studies in maternal and child health
Shu, C., Street, K., Breton, C. V., Bastain, T. M., & Wilson, M. L. (2024) · Epigenomics · doi:10.1080/17501911.2024.2343276 · Cited by 8 · see citing works
Single-Cell Sequencing on Marine Life: Application and Future Development
Li, J., Wang, H., & Li, C. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.906267 · Cited by 8 · see citing works
Assessing the multimodal tradeoffPreprint
Booeshaghi, A. S., Gao, F., & Pachter, L. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.12.08.471788 · Cited by 7 · see citing works
Taylor, R. S., Daniels, R. R., & Macqueen, D. J. (2025) · Genome Biology and Evolution · doi:10.1093/gbe/evaf076 · Cited by 7 · see citing works
Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data
Wang, X., Lian, Q., Dong, H., Xu, S., Su, Y., & Wu, X. (2024) · Genomics Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzae014 · Cited by 7 · see citing works
Single‐cell sequencing: Current applications in various tuberculosis specimen types
Zeng, Y., Ma, Q., Chen, J., Kong, X., Chen, Z., Liu, H., Liu, L., Yan, Q., Wang, X., & Lu, S. (2024) · Cell Proliferation · doi:10.1111/cpr.13698 · Cited by 6 · see citing works
Pierce, S. E., Kim, S. H., & Greenleaf, W. J. (2021) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2020.11.008 · Cited by 7 · see citing works
Single-Cell RNA Sequencing Approaches for Tracing T Cell Development
Oh, S., Gray, D. H. D., & Chong, M. M. W. (2021) · The Journal of Immunology · doi:10.4049/jimmunol.2100408 · Cited by 7 · see citing works
AAE-SC: A scRNA-Seq Clustering Framework Based on Adversarial Autoencoder
Wu, Y., Guo, Y., Xiao, Y., & Lao, S. (2020) · IEEE Access · doi:10.1109/access.2020.3027481 · Cited by 7 · see citing works
Deep Learning for Clustering Single-cell RNA-seq Data
Zhu, Y., Bai, L., Ning, Z., Fu, W., Liu, J., Jiang, L., Fei, S., Gong, S., Lu, L., Deng, M., et al. (2022) · Current Bioinformatics · doi:10.2174/1574893618666221130094050 · Cited by 6 · see citing works
scANANSE gene regulatory network and motif analysis of single-cell clusters
Smits, J. G. A., Arts, J. A., Frölich, S., Snabel, R. R., Heuts, B. M. H., Martens, J. H. A., van Heeringen, S. J., & Zhou, H. (2023) · F1000Research · doi:10.12688/f1000research.130530.2 · Cited by 6 · see citing works
Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers
Lo, Y., Liu, Y., Kammersgaard, M. B., Koladiya, A., Keyes, T., & Davis, K. L. (2023) · Seminars in Immunopathology · doi:10.1007/s00281-022-00981-1 · Cited by 6 · see citing works
Zhao, Z., Cui, H., & Cui, H. (2025) · Journal of Materials Chemistry B · doi:10.1039/d5tb00744e · Cited by 5 · see citing works
Transgelin defines pro-tumorigenic cancer-associated fibroblasts in pancreatic cancer
Wang, X., Shinjo, K., Kumegawa, K., Maruyama, R., Mii, S., Shiraki, Y., Nishimura, T., Murofushi, Y., Suzuki, M., Kabasawa, T., et al. (2025) · British Journal of Cancer · doi:10.1038/s41416-025-03299-5 · Cited by 7 · see citing works
Single-cell epigenetics and multiomics analysis in kidney research
Aihara, S., & Muto, Y. (2025) · Clinical and Experimental Nephrology · doi:10.1007/s10157-025-02679-8 · Cited by 5 · see citing works
A genome-wide comprehensive analysis of nucleosome positioning in yeast
Zeitler, L., André, K. M., Alberti, A., Wilkes, C. D., Soutourina, J., & Goldar, A. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011799 · Cited by 5 · see citing works
Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data
Wei, W., Xia, X., Li, T., Chen, Q., & Feng, X. (2024) · BMC Genomics · doi:10.1186/s12864-024-10322-1 · Cited by 5 · see citing works
Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings
LeRoy, N. J., Smith, J. P., Zheng, G., Rymuza, J., Gharavi, E., Brown, D. E., Zhang, A., & Sheffield, N. C. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae073 · Cited by 5 · see citing works
Single-Cell RNA Sequencing and Its Applications in Pituitary Research
Yang, S., Deng, C., Pu, C., Bai, X., Tian, C., Chang, M., & Feng, M. (2024) · Neuroendocrinology · doi:10.1159/000540352 · Cited by 5 · see citing works
Chen, W., Zeng, S., Zhong, J., Zou, J., Lei, Y., Chen, X., Mei, Q., & Luo, Q. (2025) · Discover Oncology · doi:10.1007/s12672-025-02419-0 · Cited by 5 · see citing works
Lin, S., & Jia, P. (2024) · GigaScience · doi:10.1093/gigascience/giae108 · Cited by 4 · see citing works
Advances in scCUT&Tag and computational analysis for single-cell gene regulatory element mapping
Wu, J., Wahiduzzaman, M., Yin, P., Sun, P., Chen, H., Ding, Y., & Wang, J. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag015 · Cited by 4 · see citing works
Illuminating cell states by a comprehensive and interpretable single cell foundation model
Wang, J., Tan, C., Gao, Z., Shao, S., Liu, S., & Li, S. Z. (2026) · Nature Communications · doi:10.1038/s41467-026-70071-5 · Cited by 4 · see citing works
Representing and extracting knowledge from single-cell data
Mihai, I. S., Chafle, S., & Henriksson, J. (2023) · Biophysical Reviews · doi:10.1007/s12551-023-01091-4 · Cited by 4 · see citing works
GRAIGH: Gene Regulation accessibility integrating GeneHancer database
Martini, L., Savino, A., Bardini, R., & Carlo, S. D. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385417 · Cited by 3 · see citing works
Yao, J., Li, L., Xu, T. W., Sun, Y., Jing, H., & Wang, C. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106587 · Cited by 4 · see citing works
Single-cell technologies to dissect heterogenous immune cell therapy products
Mueller, K. P., & Saha, K. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100343 · Cited by 4 · see citing works
Malepathirana, T., Senanayake, D., Gautam, V., & Halgamuge, S. K. (2021) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb49929.2021.9562805 · Cited by 4 · see citing works
LotOfCells: data visualization and statistics of single cell metadataPreprint
González-Velasco, Ó. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.23.595582 · Cited by 4 · see citing works
Epigenetic Alterations in Age-Related Macular Degeneration: Mechanisms and Implications
Kisswani, D., Carroll, C., Valdés‐Mora, F., & Rutar, M. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26157601 · Cited by 3 · see citing works
Enhancer Trajectories in Lineage Commitment: Regulatory Logic of States and Cooperation
Oh, M., Jeong, S., Kang, K., & Kim, S.-K. (2026) · Biomolecules · doi:10.3390/biom16010087 · Cited by 3 · see citing works
Dissecting epigenetic heterogeneity in single-cell DNA methylomes with a unified framework
Tang, S., Li, S., Zhang, G., Lyu, A., Li, H., & Chen, S. (2026) · Nature Communications · doi:10.1038/s41467-026-73171-4 · Cited by 3 · see citing works
A comparison of integration methods for single‐cell RNA sequencing data and ATAC sequencing data
Kan, Y., Wang, W., Qi, Y., Zhang, Z., Liang, X., & Jin, S. (2025) · Quantitative Biology · doi:10.1002/qub2.91 · Cited by 3 · see citing works
Ayub, A. L. P., Perestrelo, B. D. O., Pessoa, G. C., & Jasiulionis, M. G. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91081-1.00012-1 · Cited by 3 · see citing works
Tensor decomposition discriminates tissues using scATAC-seq
Taguchi, Y., & Turki, T. (2023) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2023.130360 · Cited by 3 · see citing works
BirthSeq, a new method to isolate and analyze dated cells in different vertebrates
Rueda-Alaña, E., Grillo, M., Vázquez, E., Salas, S. M., Senovilla-Ganzo, R., Escobar, L., Quintas, A., Benguría, A., Aransay, A. M., Bengoa‐Vergniory, N., et al. (2024) · Development · doi:10.1242/dev.202429 · Cited by 3 · see citing works
DOGMA-seq and multimodal, single-cell analysis in acute myeloid leukemia
Kim, J., Schanzer, N., Singh, R. S., Zaman, M. I., Medina, J. S. G., Proszynski, J., Ganesan, S., Landau, D. A., Park, C. Y., Melnick, A. M., et al. (2024) · International review of cytology · doi:10.1016/bs.ircmb.2024.08.001 · Cited by 3 · see citing works
- Authors
- Baek, S., & Lee, I.
- Affiliations
- Yonsei University (KR)
- Corresponding author
- Lee, Insuk
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2020-01
- Volume
- 18
- Pages
- 1429-1439
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC7327298
- OpenAlex
- W3034491533
- Cited by
- 151
- References
- 102
- Citations by year
- 2026: 19 · 2025: 23 · 2024: 38 · 2023: 30 · 2022: 21 · 2021: 17 · 2020: 3
- FWCI
- 1.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- YUHSpace (Yonsei University Medical Library) (submittedVersion, cc-by-nc-nd)
- Funders
- National Research Foundation of Korea
- Ministry of Science and ICT, South Korea (2018M3C9A5064709, 2018R1A5A2025079, 2019M3A9B6065192)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- single-cell ATAC-seq, chromatin accessibility, data preprocessing, epigenetic regulation, hypothesis generation, genotype-phenotype correlation, complex traits
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
