Tang, F. et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
Cusanovich, D. A. et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).
Karemaker, I. D. & Vermeulen, M. Single-cell DNA methylation profiling: technologies and biological applications. Trends Biotechnol. 36, 952–965 (2018).
Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
Chen, S., Lake, B. B. & Zhang, K. High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell. Nat. Biotechnol. 37, 1452–1457 (2019).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
Rao, N., Clark, S. & Habern, O. Bridging genomics and tissue pathology: 10x genomics explores new frontiers with the visium spatial gene expression solution. Genet. Eng. Biotechnol. News 40, 50–51 (2020).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).
Efremova, M. & Teichmann, S. A. Computational methods for single-cell omics across modalities. Nat. Methods 17, 14–17 (2020).
Cao, Y., Yang, P. & Yang, J. Y. H. A benchmark study of simulation methods for single-cell RNA sequencing data. Nat. Commun. 12, 6911 (2021).
Crowell, H. L., Morillo Leonardo, S. X., Soneson, C. & Robinson, M. D. The shaky foundations of simulating single-cell RNA sequencing data. Genome Biol. 24, 62 (2023).
Sun, T., Song, D., Li, W. V. & Li, J. J. scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. Genome Biol. 22, 163 (2021).
Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J.-P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018).
Crowell, H. L. et al. Muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020).
Cannoodt, R., Saelens, W., Deconinck, L. & Saeys, Y. Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells. Nat. Commun. 12, 3942 (2021).
Dibaeinia, P. & Sinha, S. Sergio: a single-cell expression simulator guided by gene regulatory networks. Cell Syst. 11, 252–271 (2020).
Papadopoulos, N., Gonzalo, P. R. & Söding, J. Prosstt: probabilistic simulation of single-cell RNA-seq data for complex differentiation processes. Bioinformatics 35, 3517–3519 (2019).
Tian, J., Wang, J. & Roeder, K. Esco: single cell expression simulation incorporating gene co-expression. Bioinformatics 37, 2374–2381 (2021).
Navidi, Z., Zhang, L. & Wang, B. simATAC: a single-cell ATAC-seq simulation framework. Genome Biol. 22, 74 (2021).
Li, W. V. & Li, J. J. A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics 35, i41–i50 (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11, 166 (2020).
Ma, Y. & Zhou, X. Spatially informed cell-type deconvolution for spatial transcriptomics. Nat. Biotechnol. 40, 1349–1359 (2022).
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).
Elosua-Bayes, M., Nieto, P., Mereu, E., Gut, I. & Heyn, H. SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes. Nucleic Acids Res. 49, e50 (2021).
Yan, G. & Li, J. J. scReadSim: a single-cell multi-omics read simulator. Preprint at bioRxiv https://doi.org/10.1101/2022.05.29.493924 (2022).
Cao, K., Hong, Y. & Wan, L. Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona. Bioinformatics 38, 211–219 (2022).
Argelaguet, R., Cuomo, A. S. E., Stegle, O. & Marioni, J. C. Computational principles and challenges in single-cell data integration. Nat. Biotechnol. 39, 1202–1215 (2021).
Fang, J. et al. Clustering deviation index (CDI): a robust and accurate internal measure for evaluating scRNA-seq data clustering. Genome Biol. 23, 269 (2022).
Duò, A., Robinson, M. D. & Soneson, C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Res. 7, 1441 (2018).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
Ji, Z. & Ji, H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117 (2016).
Stasinopoulos, D. M. & Rigby, R. A. Generalized additive models for location scale and shape (GAMLSS) in R. J. Stat. Softw. 23, 1–46 (2008).
Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).
Wood, S. N. Generalized Additive Models: An Introduction with R (Chapman and Hall/CRC, 2006).
Kammann, E. E. & Wand, M. P. Geoadditive models. J. R. Stat. Soc. C 52, 1–18 (2003).
Czado, C. Analyzing Dependent Data with Vine Copulas (Springer, 2019).
Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A. & Satija, R. Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341 (2021).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
Zhu, J., Sun, S. & Zhou, X. SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies. Genome Biol. 22, 184 (2021).
Li, B. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19, 662–670 (2022).
Lütge, A. et al. CellMixS: quantifying and visualizing batch effects in single-cell RNA-seq data. Life Sci. Alliance 4, e202001004 (2021).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
Zeng, D. et al. IOBR: multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Front. Immunol. 12, 687975 (2021).
Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).
Moriel, N. et al. Novosparc: flexible spatial reconstruction of single-cell gene expression with optimal transport. Nat. Protoc. 16, 4177–4200 (2021).
Song, D., Wang, Q. & Li, J. J. scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Zenodo https://doi.org/10.5281/zenodo.7110761 (2022).
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC
best SCSCSC