The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Beyond bulk: a review of single cell transcriptomics methodologies and applications
Open accessAbstract
Single cell RNA sequencing (scRNA-seq) is a promising approach to study the transcriptomes of individual cells in the brain and the central nervous system (CNS). This technology acts as a bridge between neuroscience, computational biology and systems biology, enabling an unbiased and novel understanding of the cellular composition of the brain and CNS. Gene expression at the single cell resolution is often noisy, sparse, and high-dimensional, creating challenges for computational analysis of such data. In this review, we overview fundamental sample preparation and data analysis processes of scRNA-seq and provide a comparative perspective for analyzing and visualizing these data
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Cited by
Generalizing RNA velocity to transient cell states through dynamical modeling
Bergen, V., Lange, M., Peidli, S., Wolf, F. A., & Theis, F. J. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0591-3 · Cited by 2,951
Robust decomposition of cell type mixtures in spatial transcriptomics
Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-00830-w · Cited by 1,487
Next-Generation Sequencing Technology: Current Trends and Advancements
Satam, H., Joshi, K., Mangrolia, U., Waghoo, S., Zaidi, G., Rawool, S., Thakare, R. P., Banday, S., Mishra, A. K., Das, G., et al. (2023) · Biology · doi:10.3390/biology12070997 · Cited by 1,299
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320
Xu, X., Crow, M., Rice, B. R., Li, F., Harris, B., Liu, L., Demesa-Arevalo, E., Lu, Z., Wang, L., Fox, N., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2020.12.015 · Cited by 264
Metadata
- Authors
- Kulkarni, A., Anderson, A. G., Merullo, D. P., & Konopka, G.
- Venue
- Current Opinion in Biotechnology
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 58
- Pages
- 129-136
- ISSN
- 0958-1669 (ISSN-L)
- OpenAlex
- W2938910572
- Cited by
- 356
- References
- 35
- FWCI
- 3.11
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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