Joint profiling of chromatin accessibility and gene expression in thousands of single cells
Open accessAbstract
Single-cell chromatin and RNA analysis: Single-cell analyses have begun to provide insight into the differences among and within the individual cells that make up a tissue or organism. However, technological barriers owing to the small amount of material present in each single cell have prevented parallel analyses. Cao et al. present sci-CAR, a pooled barcode method that jointly analyzes both the RNA transcripts and chromatin profiles of single cells. By applying sci-CAR to lung adenocarcinoma cells and mouse kidney tissue, the authors demonstrate precision in assessing expression and genome accessibility at a genome-wide scale. The approach provides an improvement over bulk analysis, which can be confounded by differing cellular subgroups. Science , this issue p. 1380
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?
Milo may also be useful for single-cell assays beyond scRNA-seq, including multi-omic assays.
“Milo may also be applicable to single-cell assays other than scRNA-seq, including multi-omic assays [35–39].”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [36]
Multimodal single-cell approaches are gaining traction within hepatology, allowing combination readouts from the same cell, such as simultaneous acquisition of transcriptomic and epigenomic information.
“Furthermore, multimodal single-cell approaches are swiftly gaining traction within hepatology, allowing ‘combination readouts’ from the same cell, for example simultaneous acquisition of transcriptomic and epigenomic information, again maximising our ability to identify the key therapeutic targets in pathological cells.165,166”
From Understanding the cellular interactome of non-alcoholic fatty liver disease (2022) · cites this paper as [165]
Novel therapeutics developed for one fibrotic disorder may be applicable to a wide range of fibrotic diseases due to shared pathways across organs.
“Novel therapeutics developed for one fibrotic disorder may be applicable to a wide range of fibrotic diseases due to shared pathways across organs that are uncovered by this work.”
From Fibrosis: from mechanisms to medicines (2020) · cites this paper as [182]
The cardinal identity of a cell is probably already instilled at the postmitotic stage during development, and its subsequent phenotypic differentiation and expression are regulated by extrinsic signals such as cell interactions and neural activity.
“The cardinal identity of a cell is probably already instilled at the postmitotic stage during development9,61,62,63,64, and its subsequent phenotypic differentiation and expression are regulated by extrinsic signals such as cell interactions and neural activity.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [62]
Sci-CAR-seq paired combinatorial indexing with ATAC-seq to examine chromatin states and gene expression together in more than 11,200 nuclei.
“To achieve higher throughput in single-cell analysis, Sci-CAR-seq combined single-cell combinatorial indexing55 with ATAC-seq to provide deep insights into chromatin states jointly with gene expression in over 11,200 nuclei56.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [56]
Single-cell multiomics allows uncovering mechanisms of gene regulation across different omics layers but lacks spatial information, which is crucial to understanding cellular function in tissue.
“Single-cell multiomics allows uncovering of the mechanisms of gene regulation across different omics layers6–9 but lacks spatial information, which is crucial to understanding cellular function in tissue.”
From Spatial epigenome–transcriptome co-profiling of mammalian tissues (2023) · cites this paper as [8]
Single-cell measurements of transcriptional states, chromatin accessibility, and DNA methylation have provided a better understanding of heterogeneity in regulatory mechanisms during hematopoiesis.
“Single-cell measurements of transcriptional states, chromatin accessibility, and DNA methylation, have also provided a better understanding of the heterogeneity that exists in such regulatory mechanisms during hematopoiesis (Buenrostro et al., 2018; Cao et al., 2018; Ludwig et al., 2019b; Ulirsch et al., 2019).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Cao et al., 2018
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
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 · 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
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · 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
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · 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
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · see citing works
Self-citationFIMO: 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
A pathology atlas of the human cancer transcriptome
Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al. (2017) · Science · doi:10.1126/science.aan2507 · Cited by 3,371 · 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
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
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works
Self-citationMultiplex 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
Self-citationFUNDAMENTALS OF PLANARIAN REGENERATION
Reddien, P. W., & Alvarado, A. S. (2004) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.20.010403.095114 · Cited by 1,108 · see citing works
Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease
Park, J., Shrestha, R., Qiu, C., Kondo, A., Huang, S., Werth, M., Li, M., Barasch, J., & Suszták, K. (2018) · Science · doi:10.1126/science.aar2131 · Cited by 1,065 · see citing works
Chromatin accessibility pre-determines glucocorticoid receptor binding patterns
John, S., Sabo, P. J., Thurman, R. E., Sung, M.-H., Biddie, S. C., Johnson, T. A., Hager, G. L., & Stamatoyannopoulos, J. A. (2011) · Nature Genetics · doi:10.1038/ng.759 · Cited by 954 · 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
Self-citationParallel single-cell sequencing links transcriptional and epigenetic heterogeneity
Angermueller, C., Clark, S. J., Lee, H. J., Macaulay, I. C., Teng, M. J., Hu, T. X., Krueger, F., Smallwood, S. A., Ponting, C. P., Voet, T., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3728 · Cited by 717 · see citing works
Clark, S. J., Argelaguet, R., Kapourani, C.-A., Stubbs, T. M., Lee, H. J., Alda-Catalinas, C., Krueger, F., Sanguinetti, G., Kelsey, G., Marioni, J. C., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03149-4 · Cited by 657 · see citing works
Hou, Y., Guo, H., Cao, C., Li, X., Hu, B., Zhu, P., Wu, X., Wen, L., Tang, F., Huang, Y., et al. (2016) · Cell Research · doi:10.1038/cr.2016.23 · Cited by 647 · see citing works
Massively multiplex single-cell Hi-C
Ramani, V., Deng, X.-X., Qiu, R., Gunderson, K., Steemers, F. J., Disteche, C., Noble, W. S., Duan, Z., & Shendure, J. (2016) · Nature Methods · doi:10.1038/nmeth.4155 · Cited by 545 · see citing works
Self-citationReddy, T. E., Pauli, F., Sprouse, R. O., Neff, N. F., Newberry, K. M., Garabedian, M. J., & Myers, R. M. (2009) · Genome Research · doi:10.1101/gr.097022.109 · Cited by 543 · see citing works
Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells
Guo, F., Li, L., Li, J., Wu, X., Hu, B., Zhu, P., Wen, L., & Tang, F. (2017) · Cell Research · doi:10.1038/cr.2017.82 · Cited by 374 · 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
Self-citationSimultaneous profiling of transcriptome and DNA methylome from a single cell
Hu, Y., Huang, K., An, Q., Du, G., Hu, G., Xue, J., Zhu, X., Wang, C.-Y., Xue, Z., & Fan, G. (2016) · Genome Biology · doi:10.1186/s13059-016-0950-z · Cited by 300 · see citing works
Sequencing thousands of single-cell genomes with combinatorial indexing
Vitak, S. A., Torkenczy, K. A., Rosenkrantz, J. L., Fields, A. J., Christiansen, L., Wong, M. H., Carbone, L., Steemers, F. J., & Adey, A. (2017) · Nature Methods · doi:10.1038/nmeth.4154 · Cited by 294 · see citing works
Self-citationHighly scalable generation of DNA methylation profiles in single cells
Mulqueen, R. M., Pokholok, D., Norberg, S. J., Torkenczy, K. A., Fields, A. J., Sun, D., Sinnamon, J. R., Shendure, J., Trapnell, C., O'Roak, B. J., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4112 · Cited by 289 · see citing works
Self-citationTranscriptomes of major renal collecting duct cell types in mouse identified by single-cell RNA-seq
Chen, L., Lee, J. W., Chou, C.-L., Nair, A. V., Battistone, M. A., Păunescu, T. G., Merkulova, M., Breton, S., Verlander, J. W., Wall, S. M., et al. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1710964114 · Cited by 267 · see citing works
Pott, S. (2017) · eLife · doi:10.7554/elife.23203 · Cited by 230 · see citing works
Amini, S., Pushkarev, D., Christiansen, L., Kostem, E., Royce, T., Turk, C., Pignatelli, N., Adey, A., Kitzman, J. O., Vijayan, K., et al. (2014) · Nature Genetics · doi:10.1038/ng.3119 · Cited by 189 · see citing works
Self-citationDirect GR Binding Sites Potentiate Clusters of TF Binding across the Human Genome
Vockley, C. M., D’Ippolito, A. M., McDowell, I. C., Majoros, W. H., Safi, A., Song, L., Crawford, G. E., & Reddy, T. E. (2016) · Cell · doi:10.1016/j.cell.2016.07.049 · Cited by 182 · see citing works
Reddy, T. E., Gertz, J., Crawford, G. E., Garabedian, M. J., & Myers, R. M. (2012) · Molecular and Cellular Biology · doi:10.1128/mcb.00062-12 · Cited by 149 · see citing works
High-Throughput Single-Cell Sequencing with Linear Amplification
Yin, Y. A., Jiang, Y., Lam, K.-W. G., Berletch, J. B., Distèche, C. M., Noble, W. S., Steemers, F. J., Camerini‐Otero, R. D., Adey, A., & Shendure, J. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.08.002 · Cited by 119 · see citing works
Self-citationGlucocorticoid receptor coordinates transcription factor-dominated regulatory network in macrophages
Chinenov, Y. V., Coppo, M., Gupte, R., Sacta, M. A., & Rogatsky, I. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-656 · Cited by 90 · see citing works
Using the Wash U Epigenome Browser to Examine Genome‐Wide Sequencing Data
Zhou, X., & Wang, T. (2012) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1010s40 · Cited by 68 · see citing works
Human Protein Atlas (www.proteinatlas.org/)
(2008) · Springer eBooks · doi:10.1007/978-1-4020-6754-9_7930 · Cited by 27 · see citing works
Park, J., Shrestha, R., Qiu, C., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/203125 · Cited by 9 · see citing works
Yin, Y. A., Jiang, Y., Berletch, J. B., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/338053 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Integrated analysis of multimodal single-cell data
Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508 · see citing works
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490 · see citing works
Self-citationFibrosis: from mechanisms to medicines
Henderson, N. C., Rieder, F., & Wynn, T. A. (2020) · Nature · doi:10.1038/s41586-020-2938-9 · Cited by 1,868 · see citing works
Chromatin accessibility and the regulatory epigenome
Klemm, S. L., Shipony, Z., & Greenleaf, W. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0089-8 · Cited by 1,793 · see citing works
Single-cell chromatin state analysis with Signac
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01282-5 · Cited by 1,769 · see citing works
A comparison of single-cell trajectory inference methods
Saelens, W., Cannoodt, R., Todorov, H., & Saeys, Y. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0071-9 · Cited by 1,670 · see citing works
Single-cell transcriptional diversity is a hallmark of developmental potential
Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A., Bharadwaj, A., Berger, M. J., Ilagan, F., Kuo, A. H., Hsieh, R. W., Cai, S., et al. (2020) · Science · doi:10.1126/science.aax0249 · Cited by 1,571 · see citing works
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · 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
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
Methods and applications for single-cell and spatial multi-omics
Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244 · see citing works
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
“Holistic representations of cells can be obtained only with analyses across modalities 245 , whereby several modalities of the same cells are jointly examined.”
BackgroundMOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091 · see citing works
CD8+ T cell differentiation and dysfunction in cancer
Philip, M., & Schietinger, A. (2022) · Nature Reviews Immunology · doi:10.1038/s41577-021-00574-3 · Cited by 1,052 · see citing works
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
DNA methylation aging clocks: challenges and recommendations
Bell, C. G., Lowe, R., Adams, P. D., Baccarelli, A. A., Beck, S., Bell, J. T., Christensen, B. C., Gladyshev, V. N., Heijmans, B. T., Horvath, S., et al. (2019) · Genome Biology · doi:10.1186/s13059-019-1824-y · Cited by 1,025 · see citing works
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Differential abundance testing on single-cell data using k-nearest neighbor graphs
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D., & Marioni, J. C. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01033-z · Cited by 938 · 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
A human cell atlas of fetal gene expression
Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., Zager, M. A., Aldinger, K. A., Blecher-Gonen, R., Zhang, F., et al. (2020) · Science · doi:10.1126/science.aba7721 · Cited by 764 · see citing works
Self-citationMimitou, E. P., Cheng, A., Montalbano, A., Hao, S., Stoeckius, M., Legut, M., Roush, T., Herrera, A., Papalexi, E., Ouyang, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0392-0 · Cited by 520 · see citing works
Mimitou, E. P., Lareau, C. A., Chen, K. Y., Zorzetto-Fernandes, A. L., Hao, Y., Takeshima, Y., Luo, W., Huang, T.-S., Yeung, B. Z., Papalexi, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00927-2 · Cited by 529 · see citing works
Applications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545 · see citing works
Single-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524 · 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
Self-citationThe evolving metabolic landscape of chromatin biology and epigenetics
Dai, Z., Ramesh, V., & Locasale, J. W. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0270-8 · Cited by 494 · see citing works
Spatial epigenome–transcriptome co-profiling of mammalian tissues
Zhang, D., Deng, Y., Kukanja, P., Agirre, E., Bartosovic, M., Dong, M., Ma, C., Ma, S., Su, G., Bao, S., et al. (2023) · Nature · doi:10.1038/s41586-023-05795-1 · Cited by 483 · see citing works
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works
From reads to insight: a hitchhiker’s guide to ATAC-seq data analysis
Yan, F., Powell, D., Curtis, D. J., & Wong, N. C. (2020) · Genome biology · doi:10.1186/s13059-020-1929-3 · Cited by 467 · see citing works
Muto, Y., Wilson, P. C., Ledru, N., Wu, H., Dimke, H., Waikar, S. S., & Humphreys, B. D. (2021) · Nature Communications · doi:10.1038/s41467-021-22368-w · Cited by 460 · see citing works
Multi-omics profiling of mouse gastrulation at single-cell resolution
Argelaguet, R., Clark, S. J., Mohammed, H., Stapel, L. C., Krueger, C., Kapourani, C.-A., Imaz-Rosshandler, I., Lohoff, T., Xiang, Y., Hanna, C. W., et al. (2019) · Nature · doi:10.1038/s41586-019-1825-8 · Cited by 450 · see citing works
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
He, X., Liu, X., Zuo, F., Shi, H., & Jing, J. (2022) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2022.12.009 · Cited by 474 · see citing works
Direct cell reprogramming: approaches, mechanisms and progress
Wang, H., Yang, Y., Liu, J., & Li, Q. (2021) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00335-z · Cited by 438 · see citing works
A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex
Yao, Z., Liu, H., Xie, F., Fischer, S., Adkins, R. S., Aldridge, A. I., Ament, S. A., Bartlett, A., Behrens, M. M., Van den Berge, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03500-8 · Cited by 398 · see citing works
A human cell atlas of fetal chromatin accessibility
Domcke, S., Hill, A. J., Daza, R. M., Cao, J., O’Day, D. R., Pliner, H. A., Aldinger, K. A., Pokholok, D., Zhang, F., Milbank, J. H., et al. (2020) · Science · doi:10.1126/science.aba7612 · Cited by 375 · see citing works
Self-citationZhu, X., Chen, Z., Shen, W., Huang, G., Sedivy, J. M., Wang, H., & Ju, Z. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00646-9 · Cited by 410 · see citing works
Single-cell multimodal omics: the power of many
Zhu, C., Preissl, S., & Ren, B. (2020) · Nature Methods · doi:10.1038/s41592-019-0691-5 · Cited by 386 · see citing works
Immune ageing at single-cell resolution
Mogilenko, D. A., Shchukina, I., & Artyomov, M. N. (2021) · Nature reviews. Immunology · doi:10.1038/s41577-021-00646-4 · Cited by 392 · see citing works
MultiVI: deep generative model for the integration of multi-modal data
Ashuach, T., Gabitto, M. I., Jordan, M. I., & Yosef, N. (2021) · doi:10.1101/2021.08.20.457057 · Cited by 385 · see citing works
An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
Zhu, C., Yu, M., Huang, H., Juric, I., Abnousi, A., Hu, R., Lucero, J., Behrens, M. M., Hu, M., & Ren, B. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0323-x · Cited by 344 · see citing works
Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics
Nam, A. S., Chaligné, R., & Landau, D. A. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0265-5 · Cited by 381 · see citing works
Joint analysis of heterogeneous single-cell RNA-seq dataset collections
Barkas, N., Petukhov, V., Nikolaeva, D., Lozinsky, Y., Demharter, S., Khodosevich, K., & Kharchenko, P. V. (2019) · Nature Methods · doi:10.1038/s41592-019-0466-z · Cited by 292 · see citing works
The epigenetic basis of cellular heterogeneity
Carter, B. C., & Zhao, K. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-00300-0 · Cited by 314 · see citing works
Single-cell RNA sequencing in cardiovascular development, disease and medicine
Paik, D. T., Cho, S., Tian, L., Chang, H. Y., & Wu, J. C. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-0359-y · Cited by 306 · see citing works
Joint profiling of histone modifications and transcriptome in single cells from mouse brain
Zhu, C., Zhang, Y., Li, Y. E., Lucero, J., Behrens, M. M., & Ren, B. (2021) · Nature Methods · doi:10.1038/s41592-021-01060-3 · Cited by 301 · see citing works
Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy
Gohil, S. H., Iorgulescu, J. B., Braun, D. A., Keskin, D. B., & Livak, K. J. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-00449-x · Cited by 282 · see citing works
Frodermann, V., Rohde, D., Courties, G., Severe, N., Schloss, M. J., Amatullah, H., McAlpine, C. S., Cremer, S., Hoyer, F. F., Ji, F., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0633-x · Cited by 292 · see citing works
Integrative Methods and Practical Challenges for Single-Cell Multi-omics
Ma, A., McDermaid, A., Xu, J., Chang, Y., & Ma, Q. (2020) · Trends in biotechnology · doi:10.1016/j.tibtech.2020.02.013 · Cited by 272 · see citing works
New paradigms on hematopoietic stem cell differentiation
Cheng, H., Zheng, Z., & Cheng, T. (2019) · Protein & Cell · doi:10.1007/s13238-019-0633-0 · Cited by 275 · see citing works
Datlinger, P., Rendeiro, A. F., Boenke, T., Senekowitsch, M., Krausgruber, T., Barreca, D., & Bock, C. (2021) · Nature Methods · doi:10.1038/s41592-021-01153-z · Cited by 261 · see citing works
Swanson, E., Lord, C., Reading, J., Heubeck, A. T., Genge, P. C., Thomson, Z., Weiss, M. D., Li, X.-J., Savage, A. K., Green, R. R., et al. (2021) · eLife · doi:10.7554/elife.63632 · Cited by 267 · see citing works
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 · see citing works
Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis
Ramachandran, P., Matchett, K. P., Dobie, R., Wilson-Kanamori, J. R., & Henderson, N. C. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0304-x · Cited by 253 · see citing works
Characterizing cis-regulatory elements using single-cell epigenomics
Preissl, S., Gaulton, K. J., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-022-00509-1 · Cited by 251 · see citing works
Multi-omics integration in the age of million single-cell data
Miao, Z., Humphreys, B. D., McMahon, A. P., & Kim, J. (2021) · Nature Reviews Nephrology · doi:10.1038/s41581-021-00463-x · Cited by 249 · see citing works
Concepts and limitations for learning developmental trajectories from single cell genomics
Tritschler, S., Büttner, M., Fischer, D. S., Lange, M., Bergen, V., Lickert, H., & Theis, F. J. (2019) · Development · doi:10.1242/dev.170506 · Cited by 233 · see citing works
The diversity of GABAergic neurons and neural communication elements
Huang, Z. J., & Paul, A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0195-4 · Cited by 201 · see citing works
Papalexi, E., Mimitou, E. P., Butler, A. W., Foster, S., Bracken, B., Mauck, W. M., Wessels, H.-H., Hao, Y., Yeung, B. Z., Smibert, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00778-2 · Cited by 238 · see citing works
Single-cell analysis targeting the proteome
Labib, M., & Kelley, S. O. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-0162-7 · Cited by 233 · see citing works
High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery
Yang, X., Kui, L., Tang, M., Li, D., Wei, K., Chen, W., Miao, J., & Dong, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00019 · Cited by 231 · see citing works
Microglia and Astrocytes in Disease: Dynamic Duo or Partners in Crime?
Liddelow, S. A., Marsh, S. E., & Stevens, B. (2020) · Trends in Immunology · doi:10.1016/j.it.2020.07.006 · Cited by 226 · see citing works
The single-cell eQTLGen consortium
van der Wijst, M. G. P., de Vries, D., Groot, H. E., Trynka, G., Hon, C.-C., Bonder, M. J., Stegle, O., Nawijn, M. C., Idaghdour, Y., van der Harst, P., et al. (2020) · eLife · doi:10.7554/elife.52155 · Cited by 215 · see citing works
Modern views of machine learning for precision psychiatry
Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 225 · see citing works
McCord, R. P., Kaplan, N., & Giorgetti, L. (2020) · Molecular Cell · doi:10.1016/j.molcel.2019.12.021 · Cited by 204 · see citing works
Miao, Z., Balzer, M. S., Ma, Z., Liu, H., Wu, J., Shrestha, R., Arányi, T., Kwan, A., Kondo, A., Pontoglio, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22266-1 · Cited by 209 · 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
Jointly defining cell types from multiple single-cell datasets using LIGER
Liu, J., Gao, C., Sodicoff, J., Kozareva, V., Macosko, E. Z., & Welch, J. D. (2020) · Nature Protocols · doi:10.1038/s41596-020-0391-8 · Cited by 203 · see citing works
Adipose tissue at single-cell resolution
Maniyadath, B., Zhang, Q., Gupta, R. K., & Mandrup, S. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.02.002 · Cited by 207 · see citing works
Gene regulatory programmes of tissue regeneration
Goldman, J. A., & Poss, K. D. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0239-7 · Cited by 200 · see citing works
Human and mouse trigeminal ganglia cell atlas implicates multiple cell types in migraine
Yang, L., Xu, M., Bhuiyan, S. A., Li, J., Zhao, J., Cohrs, R. J., Susterich, J. T., Signorelli, S., Green, U., Stone, J. R., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.003 · Cited by 188 · see citing works
The molecular cytoarchitecture of the adult mouse brain
Langlieb, J., Sachdev, N., Balderrama, K. S., Nadaf, N. M., Raj, M., Murray, E. M., Webber, J. T., Vanderburg, C. R., Gazestani, V., Tward, D. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06818-7 · Cited by 240 · see citing works
Advances in single-cell omics and multiomics for high-resolution molecular profiling
Lim, J., Park, C., Kim, M., Kim, H., Kim, J., & Lee, D.-S. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01186-2 · Cited by 193 · see citing works
Immunogenomic Landscape of Hematological Malignancies
Dufva, O., Pölönen, P., Brück, O. E., Keränen, M. A. I., Klievink, J., Mehtonen, J., Huuhtanen, J., Kumar, A., Malani, D., Siitonen, S. M., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.06.002 · Cited by 188 · see citing works
The road ahead in genetics and genomics
McGuire, A. L., Gabriel, S., Tishkoff, S. A., Wonkam, A., Chakravarti, A., Furlong, E. E. M., Treutlein, B., Meissner, A., Chang, H. Y., López-Bigas, N., et al. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0272-6 · Cited by 188 · see citing works
The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution
Dorrity, M. W., Alexandre, C. M., Hamm, M. O., Vigil, A.-L. B. G., Fields, S., Queitsch, C., & Cuperus, J. T. (2021) · Nature Communications · doi:10.1038/s41467-021-23675-y · Cited by 189 · see citing works
Jin, S., Zhang, L., & Nie, Q. H. (2020) · Genome biology · doi:10.1186/s13059-020-1932-8 · Cited by 174 · see citing works
The Nuclear Receptor ESRRA Protects from Kidney Disease by Coupling Metabolism and Differentiation
Dhillon, P., Park, J., del Pozo, C. H., Li, L., Doke, T., Huang, S., Zhao, J., Kang, H. M., Shrestra, R., Balzer, M. S., et al. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.11.011 · Cited by 171 · see citing works
Vistain, L. F., & Tay, S. (2021) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2021.01.013 · Cited by 177 · see citing works
Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq
Liu, Z., Chen, Y., Xia, Q., Liu, M., Xu, H., Chi, Y., Deng, Y., & Xing, D. (2023) · Science · doi:10.1126/science.adg3797 · Cited by 169 · see citing works
Sci-fate characterizes the dynamics of gene expression in single cells
Cao, J., Zhou, W. Y., Steemers, F. J., Trapnell, C., & Shendure, J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0480-9 · Cited by 162 · see citing works
Self-citationSingle nucleus multi-omics identifies human cortical cell regulatory genome diversity
Luo, C., Liu, H., Xie, F., Armand, E. J., Siletti, K., Bakken, T. E., Fang, R., Doyle, W. I., Stuart, T., Hodge, R. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100107 · Cited by 167 · see citing works
Cobolt: integrative analysis of multimodal single-cell sequencing data
Gong, B., Zhou, Y., & Purdom, E. (2021) · Genome Biology · doi:10.1186/s13059-021-02556-z · Cited by 168 · see citing works
BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
Wu, K., Yost, K. E., Chang, H. Y., & Zou, J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2023070118 · Cited by 164 · see citing works
scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning
Lin, Y., Wu, T.-Y., Wan, S., Yang, J. Y. H., Wong, W. H., & Wang, Y. X. R. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01161-6 · Cited by 166 · see citing works
Single-cell multiomics sequencing reveals the functional regulatory landscape of early embryos
Wang, Y., Yuan, P., Yan, Z., Yang, M., Huo, Y., Nie, Y., Zhu, X., Qiao, J., & Yan, L. (2021) · Nature Communications · doi:10.1038/s41467-021-21409-8 · Cited by 161 · see citing works
Unraveling Hematopoiesis through the Lens of Genomics
Liggett, L. A., & Sankaran, V. G. (2020) · Cell · doi:10.1016/j.cell.2020.08.030 · Cited by 163 · see citing works
Shao, X., Lu, X., Liao, J., Chen, H., & Fan, X. (2020) · Protein & Cell · doi:10.1007/s13238-020-00727-5 · Cited by 162 · see citing works
Turner, A. W., Hu, S. S., Mosquera, J. V., Feng, W., Hodonsky, C. J., Wong, D., Auguste, G. E., Song, Y., Sol‐Church, K., Farber, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01069-0 · Cited by 151 · see citing works
Multi-domain translation between single-cell imaging and sequencing data using autoencoders
Yang, K., Belyaeva, A., Venkatachalapathy, S., Damodaran, K., Katcoff, A., Radhakrishnan, A., Shivashankar, G. V., & Uhler, C. (2021) · Nature Communications · doi:10.1038/s41467-020-20249-2 · Cited by 149 · see citing works
The evolving concept of cell identity in the single cell era
Morris, S. A. (2019) · Development · doi:10.1242/dev.169748 · Cited by 154 · see citing works
Janosevic, D., Myslinski, J., McCarthy, T. W., Zollman, A., Syed, F., Xuei, X., Gao, H., Liu, Y., Collins, K. S., Cheng, Y., et al. (2021) · eLife · doi:10.7554/elife.62270 · Cited by 149 · see citing works
Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation
Baek, S., & Lee, I. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.06.012 · Cited by 151 · see citing works
Multimodal single-cell chromatin analysis with SignacPreprint
Stuart, T., Srivastava, A., Lareau, C. A., & Satija, R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.09.373613 · Cited by 150 · see citing works
A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data
Minoura, K., Abe, K., Nam, H., Nishikawa, H., & Shimamura, T. (2021) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100071 · Cited by 147 · see citing works
Characterizing the ecological and evolutionary dynamics of cancer
Zahir, N., Sun, R., Gallahan, D., Gatenby, R. A., & Curtis, C. N. (2020) · Nature Genetics · doi:10.1038/s41588-020-0668-4 · Cited by 141 · see citing works
Evaluating measures of association for single-cell transcriptomics
Skinnider, M. A., Squair, J. W., & Foster, L. J. (2019) · Nature Methods · doi:10.1038/s41592-019-0372-4 · Cited by 145 · see citing works
Recent advances in single-cell sequencing technologies
Wen, L., & Tang, F. (2022) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbac002 · Cited by 127 · see citing works
Model-based deep embedding for constrained clustering analysis of single cell RNA-seq data
Tian, T., Zhang, J., Lin, X., Wei, Z., & Hakonarson, H. H. (2021) · Nature Communications · doi:10.1038/s41467-021-22008-3 · Cited by 146 · see citing works
- Authors
- 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., Steemers, F. J., Adey, A. C., Trapnell, C., & Shendure, J.
- Affiliations
- University of Washington (US)
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Illumina (United States) (US)
- Oregon Health & Science University (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Cusanovich, Darren A.
- Ramani, Vijay
- Trapnell, Cole
- Shendure, Jay
- ORCID
- Venue
- Science
- Published
- 2018-09-28
- Volume
- 361
- Issue
- 6409
- Pages
- 1380-1385
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6571013
- OpenAlex
- W2889236955
- Cited by
- 938
- References
- 42
- Citations by year
- 2026: 102 · 2025: 109 · 2024: 113 · 2023: 132 · 2022: 125 · 2021: 156 · 2020: 117 · 2019: 74 · 2018: 9
- FWCI
- 31.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (DGE-1256082)
- National Institutes of Health (DP1HG007811, R01HG006283, DP2 HD088158, R35GM124704)
- Paul G. Allen Frontiers Foundation
- Howard Hughes Medical Institute
- W. M. Keck Foundation
- Dale. F. Frey Award for Breakthrough Scientists
- Alfred P. Sloan Foundation Research Fellowship
- National Heart, Lung, and Blood Institute (T32HL007828)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- chromatin accessibility, single-cell transcriptomics, combinatorial indexing, dexamethasone treatment, mouse kidney, cis-regulatory elements, gene expression regulation, single-cell multi-omics
- MeSH terms
- A549 Cells, Animals, Chromatin, Dexamethasone, Gene Expression Regulation, Humans, Kidney, Transcription, Genetic, Gene Expression Profiling, Genomics, NIH 3T3 Cells, Regulatory Elements, Transcriptional, Mice, HEK293 Cells, Single-Cell Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
