Computational methods for single-cell omics across modalities

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

Spatial reconstruction of single-cell gene expression data

Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055

Scenic: single-cell regulatory network inference and clustering.

Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520

CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449

Simultaneous epitope and transcriptome measurement in single cells

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098

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

MOFA+: 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

Single-cell spatial reconstruction reveals global division of labour in the mammalian liver

Halpern, K. B., Shenhav, R., Matcovitch-Natan, O., Tóth, B., Lemze, D., Golan, M., Massasa, E. E., Baydatch, S., Landen, S., Moor, A. E., et al. (2017) · Nature · doi:10.1038/nature21065 · Cited by 1,059

Multiplexed quantification of proteins and transcripts in single cells

Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832

Lineage Tracing in Humans Enabled by Mitochondrial Mutations and Single-Cell Genomics

Ludwig, L. S., Lareau, C. A., Ulirsch, J. C., Christian, E., Muus, C., Li, L. H., Pelka, K., Ge, W. H., Oren, Y., Brack, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.01.022 · Cited by 554

High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin

Achim, K., Pettit, J.-B., Saraiva, L. R., Gavriouchkina, D., Larsson, T., Arendt, D., & Marioni, J. C. (2015) · Nature Biotechnology · doi:10.1038/nbt.3209 · Cited by 395

The Drosophila embryo at single-cell transcriptome resolution

Karaiskos, N., Wahle, P., Alles, J., Boltengagen, A., Ayoub, S., Kipar, C., Kocks, C., Rajewsky, N., & Zinzen, R. P. (2017) · Science · doi:10.1126/science.aan3235 · Cited by 382

Single-Cell Transcriptomics Meets Lineage Tracing

Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375

Conditional density-based analysis of T cell signaling in single-cell data

Krishnaswamy, S., Spitzer, M. H., Mingueneau, M., Bendall, S. C., Litvin, O., Stone, E., Pe’er, D., & Nolan, G. P. (2014) · Science · doi:10.1126/science.1250689 · Cited by 225

Profiling of Pluripotency Factors in Single Cells and Early Embryos

Hainer, S. J., Bošković, A., McCannell, K. N., Rando, O. J., & Fazzio, T. G. (2019) · Cell · doi:10.1016/j.cell.2019.03.014 · Cited by 203

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

Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA

Xu, J., Nuno, K. A., Litzenburger, U. M., Qi, Y., Corces, M. R., Majeti, R., & Chang, H. Y. (2019) · eLife · doi:10.7554/elife.45105 · Cited by 147

MATCHER: manifold alignment reveals correspondence between single cell transcriptome and epigenome dynamics

Welch, J. D., Hartemink, A. J., & Prins, J. F. (2017) · Genome Biology · doi:10.1186/s13059-017-1269-0 · Cited by 140

Cell-type-specific signaling networks in heterocellular organoids

Qin, X., Sufi, J., Vlckova, P., Kyriakidou, P., Acton, S. E., Li, V. S. W., Nitz, M., & Tape, C. J. (2020) · Nature Methods · doi:10.1038/s41592-020-0737-8 · Cited by 134

A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements

Lopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M. I., & Yosef, N. (2019) · arXiv · doi:10.48550/arxiv.1905.02269 · Cited by 105

Nested effects models for high-dimensional phenotyping screens

Markowetz, F. M., Kostka, D., Troyanskaya, O. G., & Spang, R. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm178 · Cited by 103

Single-Cell Multi-omics: An Engine for New Quantitative Models of Gene Regulation

Packer, J. S., & Trapnell, C. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.06.001 · Cited by 85

Statistical single cell multi-omics integration

Colomé‐Tatché, M., & Theis, F. J. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.01.003 · Cited by 72

CellPhoneDB v2.0: Inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexesPreprint

Efremova, M., Vento-Tormo, M., Teichmann, S. A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/680926 · Cited by 71

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63

Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expressionPreprint

Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia‐Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/630996 · Cited by 57

MOFA+: a probabilistic framework for comprehensive integration of structured single-cell dataPreprint

Argelaguet, R., Arnol, D., Bredikhin, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/837104 · Cited by 23

Cardelino: Integrating whole exomes and single-cell transcriptomes to reveal phenotypic impact of somatic variantsPreprint

McCarthy, D. J., Rostom, R., Huang, Y., Kunz, D. J., Danecek, P., Bonder, M. J., Hagai, T., Wang, W., Gaffney, D. J., Simons, B. D., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/413047 · Cited by 13

A Joint Model of RNA Expression and Surface Protein Abundance in Single CellsPreprint

Gayoso, A., Lopez, R., Steier, Z., Regier, J., Streets, A., & Yosef, N. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/791947 · Cited by 13

Single cell network analysis with a mixture of Nested Effects Models

Pirkl, M., & Beerenwinkel, N. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty602 · Cited by 12

Single-Cell Signalling Analysis of Heterocellular OrganoidsPreprint

Qin, X. Q., Sufi, J., Vlckova, P., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/659896 · Cited by 8

CRISPR Screening in Single Cells

Henriksson, J. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9240-9_23 · Cited by 3

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

Fibrosis: 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

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

Spatial omics and multiplexed imaging to explore cancer biology

Lewis, S. M., Asselin-Labat, M.-L., Nguyen, Q. H., Berthelet, J., Tan, X., Wimmer, V. C., Mérino, D., Rogers, K. L., & Naik, S. H. (2021) · Nature Methods · doi:10.1038/s41592-021-01203-6 · Cited by 623

Joint probabilistic modeling of single-cell multi-omic data with totalVI

Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592

Statistical single cell multi-omics integration

Colomé‐Tatché, M., & Theis, F. J. (2018) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2018.01.003 · Cited by 72

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

Computational strategies for single-cell multi-omics integration

Adossa, N. A., Khan, S., Rytkönen, K. T., & Elo, L. L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.04.060 · Cited by 106

Challenges and emerging directions in single-cell analysis

Yuan, G., Cai, L., Elowitz, M. B., Enver, T., Fan, G., Guo, G., Irizarry, R. A., Kharchenko, P. V., Kim, J., Orkin, S. H., et al. (2017) · Genome biology · doi:10.1186/s13059-017-1218-y · Cited by 349

Unraveling Heterogeneity in Transcriptome and Its Regulation Through Single-Cell Multi-Omics Technologies

Xing, Q. R., Cipta, N. O., Hamashima, K., Liou, Y., Koh, C.-G., & Loh, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00662 · Cited by 31