Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities is a research paper published in Genome Research (2021). On theSindex it has a DataRank of 0.801. It has been cited 207 times.
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Base Score Contribution
0.801
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Institutes of Health
Grant: K00CA222750
National Institutes of Health Pathway
Grant: K99HD092542
NICHD NIH HHS
Grant: R00 HD092542
Cancer Research UK
Grant: 24042
National Institutes of Health
Grant: 3K00CA222750-03S1
Statistical Methods for Characterizing Tumor Heterogeneity at the Single Cell Level
National Institutes of Health
Grant: 1K99HD092542-01
Development and function of a neural circuit underlying sex-specificity of social behaviors
National Science Foundation
Grant: 2047611
CAREER: Statistical approaches and computational tools for analyzing spatially-resolved single-cell transcriptomics data
Cancer Research UK
CRUK IMAXT
Cancer Research UK
Fields of Study
MeSH Terms
Keywords
Additional file 1 of ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction
Additional file 1 of ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction
Additional file 1 of SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Additional file 1 of SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Additional file 5 of SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Additional file 5 of SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics
Additional file 3 of ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction
Additional file 3 of ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction
Additional file 3 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
Additional file 3 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
Additional file 6 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
Additional file 6 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
Additional file 11 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
Additional file 11 of Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions
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