Cell2location maps fine-grained cell types in spatial transcriptomics

By V. Kleshchevnikov, Artem Shmatko, E. Dann, Alexander Aivazidis, Hamish W. King, Tong Li, R. Elmentaite, A. Lomakin, Veronika R. Kedlian, Adam Gayoso and 10 more. Nature Biotechnology, 2022

1,006 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1d3223f68d6196c9fa9ec01f4a9d2299c7de1027

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Malte D. Luecken
2. Luke Zappia
3. H. Schiller
4. Fabian J Theis

In Molecular Biology

1. Malte D. Luecken
2. Fabian J Theis
3. Luke Zappia
4. H. Schiller
5. Anna C. Schaar
6. F. Drost
7. Christopher Lance
8. Anastasia Litinetskaya

Citations in the corpus

• Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “Using the ground truth of the locations for the L5/6 excitatory neurons, we found that SpatialDWLS, RCTD, Tangram, Cell2location, and Stereoscope had PCC values of 0.88, 0.86, 0.85, 0.83, and 0.81 for the assignations of the L5/6 excitatory neurons, higher than other integration methods (Extended Da”
• Best practices for single-cell analysis across modalities (2023, methodology): “Methods for this purpose vary broadly with respect to their assumptions and their definition of SVGs, and there is no consensus on how to best identify SVGs. SPARK 233 and SpatialDE 234 , for example, leverage spatial correlation testing, BayesSpace 227 uses Markov random fields, spaGCN 229 uses…”
• Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, neutral): “28 and 29).”
• Methods and applications for single-cell and spatial multi-omics (2023, background): “single-cell data to identify the cellular constituents of a particular tissue region 149–151 .”