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We developed TACCO, a versatile and efficient computational annotation framework, leveraging growing single-cell and spatial omics data to identify cell types and states, decipher molecular and cellular tissue structure, and resolve differentiation trajectories.
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References
Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019). This is a review article on integration of different data types at single-cell resolution.
Aldridge, S. & Teichmann, S. A. Single cell transcriptomics comes of age. Nat. Commun. 11, 4307 (2020). This review article covers technological advancements and discoveries enabled by single-cell transcriptomics.
Palla, G. et al. Spatial components of molecular tissue biology. Nat. Biotechnol. 40, 308–318 (2022). This review article discusses computational methods for spatial molecular analysis.
Peyre, G. & Cuturi, M. Computational optimal transport: with applications to data science. Found. Trends Mach. Learn. 11, 355–607 (2019). This paper reviews optimal transport theory, algorithmic foundations and applications for data science.
Avraham-Davidi, I. et al. “Integrative single cell and spatial transcriptomics of colorectal cancer reveals multicellular functional units that support tumor progression.” Preprint at bioRxiv https://doi.org/10.1101/2022.10.02.508492 (2022). TACCO integrates scRNA-seq and Slide-Seq to create a spatial map of healthy and dysplastic colon cellular ecosystems and their association with disease progression.
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This is a summary of: Mages, S. et al. TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-01657-3 (2023).
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Computational framework decomposes and annotates single-cell and spatial omics.
Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01658-2
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DOI: https://doi.org/10.1038/s41587-023-01658-2
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Joan Lupo
