Scanorama: integrating large and diverse single-cell transcriptomic datasets

Bonnie Berger, Brian L. Hie, Soochi Kim, Thomas A. Rando, Bryan Bryson

Published: 2024-06-06 DOI: 10.1038/s41596-024-00991-3

single-cell RNA sequencing

Scanorama

data integration

batch effects

Scanpy

AI 解读

The GFP thermal shift assay for screening ligand and lipid interactions to solute carrier transporters

Co-fractionation–mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain

Simultaneously measuring the methylation of parent and daughter strands of replicated DNA at the single-molecule level by Hammer-seq

The Nodewalk assay to quantitate chromatin fiber interactomes in very small cell populations

Nondestructive production of exosomes loaded with ultrathin palladium nanosheets for targeted bio-orthogonal catalysis

An optimized culture system for efficient derivation of porcine expanded potential stem cells from preimplantation embryos and by reprogramming somatic cells

Engineering SARS-CoV-2 using a reverse genetic system

cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA

Growing and making nano- and microcrystals

Composable microfluidic spinning platforms for facile production of biomimetic perfusable hydrogel microtubes

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References

Tabula Sapiens, C. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).
Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376, eabl4290 (2022).
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022).
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 e1821 (2019).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Polanski, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).
Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17, e9620 (2021).
Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).
Yu, X., Xu, X., Zhang, J. & Li, X. Batch alignment of single-cell transcriptomics data using deep metric learning. Nat. Commun. 14, 960 (2023).
Hie, B., Cho, H., DeMeo, B., Bryson, B. & Berger, B. Geometric sketching compactly summarizes the single-cell transcriptomic landscape. Cell Syst. 8, 483–493.e487 (2019).
Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).
Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nat. Methods 16, 715–721 (2019).
Dasgupta, S. & Freund, Y. Random projection trees and low dimensional manifolds. Proc. Annual ACM Symposium on Theory of Computing, (2008).
Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).
Buttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A. & Theis, F. J. A test metric for assessing single-cell RNA-seq batch correction. Nat. Methods 16, 43–49 (2019).
Halko, N., Martinsson, P. -G. & Tropp, J. A. Preprint at arXiv:0909.4061 (2009).
Yu, Y. W., Daniels, N. M., Danko, D. C. & Berger, B. Entropy-scaling search of massive biological data. Cell Syst. 1, 130–140 (2015).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Harris et al. Array programming with NumPy. Nature 585, 357–362 (2020).
Virshup, I., Rybakov, S., Theis, F.J., Angerer, P. & Wolf, F.A. anndata: annotated data. Preprint at bioRxiv 2021.2012.2016.473007 (2021).
Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).
Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).
Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).
Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360 e344 (2016).
Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 e383 (2016).
Grun, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19, 266–277 (2016).
Lawlor, N. et al. Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes. Genome Res. 27, 208–222 (2017).
Segerstolpe, A. et al. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab. 24, 593–607 (2016).
Shalek, A. K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).
Davie, K. et al. A single-cell transcriptome atlas of the aging Drosophila brain. Cell 174, 982–998 e920 (2018).
Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).
DeMeo, B. & Berger, B. SCA: recovering single-cell heterogeneity through information-based dimensionality reduction. Genome Biol. 24, 195 (2023).
Cho, H., Berger, B. & Peng, J. Generalizable and scalable visualization of single-cell data using neural networks. Cell Syst. 7, 185–191e4 (2018).
Singh, R., Hie, B., Narayan, A. & Berger, B. Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities. Genome Biol. 22, 131 (2021).

Scanorama: integrating large and diverse single-cell transcriptomic datasets

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