Benchmarking single-cell RNA-sequencing protocols for cell atlas projects
By E. Mereu, Atefeh Lafzi, C. Moutinho, C. Ziegenhain, Davis J. McCarthy, A. Álvarez-Varela, E. Batlle, Sagar, Dominic Grün, J. Lau and 30 more. Nature Biotechnology, 2020
413 citations recorded by the source corpus.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/16427167f541cf06f9200dfc81ab78f168695559
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Citations in the corpus
- Integrated analysis of multimodal single-cell data (2020, background): “Despite tremendous functional diversity, distinct populations of T cells such as effector, regulatory, gd, and mucosal associated invariant T (MAIT), often cannot be effectively separated by scRNA-seq alone, even when using the most sensitive and cutting-edge technologies (Ding et al., 2020; Mereu e”
- Cell segmentation in imaging-based spatial transcriptomics (2021, methodology): “uring the last decade, single-cell transcriptomic technologies gained great popularity, with single-cell RNA-sequencing (scRNA-seq) becoming the preferred approach for characterizing the state of complex tissues 1–4 .”
- CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data (2022, background): “In recent years, advances in tissue isolation and highthroughput sequencing at the single-cell level have enabled the generation of single-cell RNA-sequencing (scRNA-seq) datasets, which are increasingly entering the public domain (21).”
- Assessment of computational methods for the analysis of single-cell ATAC-seq data (2019, result): “The low copy number re-sults in an inherent per-cell data sparsity, where only 1 – 10% of expected accessible peaks are detected in single cells from scATAC-seq data (Additional file 1: Figure S1), compared to 10 – 45% of expressed genes detected in single cells from scRNA-seq data [2, 3].”