The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Estimation of immune cell content in tumour tissue using single-cell RNA-seq data
Open accessAbstract
As interactions between the immune system and tumour cells are governed by a complex network of cell–cell interactions, knowing the specific immune cell composition of a solid tumour may be essential to predict a patient’s response to immunotherapy. Here, we analyse in depth how to derive the cellular composition of a solid tumour from bulk gene expression data by mathematical deconvolution, using indication-specific and cell type-specific reference gene expression profiles (RGEPs) from tumour-derived single-cell RNA sequencing data. We demonstrate that tumour-derived RGEPs are essential for the successful deconvolution and that RGEPs from peripheral blood are insufficient. We distinguish nine major cell types, as well as three T cell subtypes. Using the tumour-derived RGEPs, we can estimate the content of many tumours associated immune and stromal cell types, their therapeutically relevant ratios, as well as an improved gene expression profile of the malignant cells.
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LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Salmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
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
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Gene name errors are widespread in the scientific literature
Ziemann, M., Eren, Y., & El-Osta, A. (2016) · Genome Biology · doi:10.1186/s13059-016-1044-7 · Cited by 167
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Finotello, F., Mayer, C., Plattner, C., Laschober, G., Rieder, D., Hackl, H., Krogsdam, A., Loncova, Z., Posch, W., Wilflingseder, D., et al. (2019) · Genome Medicine · doi:10.1186/s13073-019-0638-6 · Cited by 1,542
Baccin, C., Al-Sabah, J., Velten, L., Helbling, P. M., Grünschläger, F., Hernández-Malmierca, P., Nombela-Arrieta, C., Steinmetz, L. M., Trumpp, A., & Haas, S. (2020) · Nature Cell Biology · doi:10.1038/s41556-019-0439-6 · Cited by 904
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Kieffer, Y., Hocine, H. R., Gentric, G., Pelon, F., Bernard, C., Bourachot, B., Lameiras, S., Albergante, L., Bonneau, C., Guyard, A., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1384 · Cited by 910
Benchmarking of cell type deconvolution pipelines for transcriptomics data
Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P., & De Preter, K. (2020) · Nature Communications · doi:10.1038/s41467-020-19015-1 · Cited by 446
Metadata
- Authors
- Schelker, M., Feau, S., Du, J., Ranu, N., Klipp, E., MacBeath, G., Schoeberl, B., & Raue, A.
- Venue
- Nature Communications
- Published
- 2017-12-11
- Volume
- 8
- Issue
- 1
- Pages
- 2032-2032
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2950779253
- Cited by
- 285
- References
- 8
- FWCI
- 9.90
- Type
- journal article
- Language
- en
- Open access
- gold
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- Schelker, M., Feau, S., Du, J., Ranu, N., Klipp, E., MacBeath, G., Schoeberl, B., & Raue, A. (2017). Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nature Communications. https://doi.org/10.1038/s41467-017-02289-3
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