Benchmarking cell-type clustering methods for spatially resolved transcriptomics data is a research paper published in Briefings in Bioinformatics (2022). On theSindex it has a DataRank of 0. It has been cited 83 times.
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National Institutes of Health
Grant: R35GM142702
National Science Foundation
Grant: IIS-2128307
National Science Foundation
Grant: BCS-2152822
National Science Foundation
Grant: DMS-2210371
Rutgers Busch Biomedical Grant
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Keywords
Additional file 2 of Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Additional file 2 of Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Additional file 1 of spVC for the detection and interpretation of spatial gene expression variation
Additional file 1 of spVC for the detection and interpretation of spatial gene expression variation
Additional file 2 of spVC for the detection and interpretation of spatial gene expression variation
Additional file 2 of spVC for the detection and interpretation of spatial gene expression variation
Additional file 3 of spVC for the detection and interpretation of spatial gene expression variation
Additional file 3 of spVC for the detection and interpretation of spatial gene expression variation
Additional file 3 of Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Additional file 3 of Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Additional file 1 of Benchmarking clustering, alignment, and integration methods for spatial transcriptomics
Additional file 1 of Benchmarking clustering, alignment, and integration methods for spatial transcriptomics