The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Accurate estimation of cell composition in bulk expression through robust integration of single-cell information
Open accessAbstract
We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.
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Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Obesity is associated with macrophage accumulation in adipose tissue
Weisberg, S. P., McCann, D., Desai, M., Rosenbaum, M., Leibel, R. L., & Ferrante, A. W. (2003) · Journal of Clinical Investigation · doi:10.1172/jci200319246 · Cited by 10,041
UMAP: Uniform Manifold Approximation and Projection
McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018) · Journal of Open Source Software · doi:10.21105/joss.00861 · Cited by 9,826
The metabolic phenotype in obesity: Fat mass, body fat distribution, and adipose tissue function
Goossens, G. H. (2017) · Obesity Facts · doi:10.1159/000471488 · Cited by 8,481
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
The immune contexture in human tumours: impact on clinical outcome
Fridman, W. H., Pagès, F., Sautès-Fridman, C., & Galon, J. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3245 · Cited by 4,981
Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479
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Massier, L., Jalkanen, J., Elmastas, M., Zhong, J., Wang, T., Nono Nankam, P. A., Frendo-Cumbo, S., Bäckdahl, J., Subramanian, N., Sekine, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36983-2 · Cited by 195
Zaitsev, A., Chelushkin, M., Dyikanov, D., Cheremushkin, I., Shpak, B., Nomie, K., Zyrin, V., Nuzhdina, E., Lozinsky, Y., Zotova, A., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.006 · Cited by 139
An exhausted-like microglial population accumulates in aged and APOE4 genotype Alzheimer’s brains
Millet, A., Ledo, J. H., & Tavazoie, S. F. (2024) · Immunity · doi:10.1016/j.immuni.2023.12.001 · Cited by 132
OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing
Zeng, Z., Ma, Y., Hu, L., Tan, B., Liu, P., Wang, Y., Xing, C., Xiong, Y., & Du, H. (2024) · Nature Communications · doi:10.1038/s41467-024-50194-3 · Cited by 102
Metadata
- Authors
- Jew, B., Alvarez, M., Rahmani, E., Miao, Z., Ko, A., Garske, K. M., Sul, J. H., Pietiläinen, K. H., Pajukanta, P., & Halperin, E.
- Venue
- Nature Communications
- Published
- 2020-04-24
- Volume
- 11
- Issue
- 1
- Pages
- 1971-1971
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3019556496
- Cited by
- 440
- References
- 43
- FWCI
- 13.48
- Type
- journal article
- Language
- en
- Open access
- gold
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