The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data
Open accessAbstract
Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.
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Cites
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Robust enumeration of cell subsets from tissue expression profiles
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Double-slit photoelectron interference in strong-field ionization of the neon dimer
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Cited by
He, S., Bhatt, R., Brown, C., Brown, E. A., Buhr, D. L., Chantranuvatana, K., Danaher, P., Dunaway, D., Garrison, R. G., Geiss, G., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01483-z · Cited by 814
Self-citationUnsupervised spatially embedded deep representation of spatial transcriptomics
Xu, H., Fu, H., Long, Y., Ang, K. S., Sethi, R., Chong, K., Li, M., Uddamvathanak, R., Lee, H. K., Ling, J., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01283-x · Cited by 391
Benchmarking and integration of methods for deconvoluting spatial transcriptomic data
Yan, L., & Sun, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac805 · Cited by 92
Senolytic therapy alleviates physiological human brain aging and COVID-19 neuropathology
Aguado, J., Amarilla, A. A., Taherian Fard, A., Albornoz, E. A., Tyshkovskiy, A., Schwabenland, M., Chaggar, H. K., Modhiran, N., Gómez-Inclán, C., Javed, I., et al. (2023) · Nature Aging · doi:10.1038/s43587-023-00519-6 · Cited by 80
Jury-Garfe, N., Redding-Ochoa, J., You, Y., Martínez, P., Karahan, H., Chimal-Juárez, E., Johnson, T. S., Zhang, J., Resnick, S., Kim, J., et al. (2024) · Acta Neuropathologica · doi:10.1007/s00401-024-02775-1 · Cited by 22
“Cell deconvolution and cell abundance analysis were performed using the NanoString SpatialDecon R library [17].”
Method
Metadata
- Authors
- Danaher, P., Kim, Y., Nelson, B., Griswold, M., Yang, Z., Piazza, E., & Beechem, J. M.
- Venue
- Nature Communications
- Published
- 2022-01-19
- Volume
- 13
- Issue
- 1
- Pages
- 385-385
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4206199829
- Cited by
- 292
- References
- 51
- FWCI
- 17.13
- Type
- journal article
- Language
- en
- Open access
- gold
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- Danaher, P., Kim, Y., Nelson, B., Griswold, M., Yang, Z., Piazza, E., & Beechem, J. M. (2022). Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nature Communications. https://doi.org/10.1038/s41467-022-28020-5
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
