Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptom...Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptomics,enable researchers to map gene expression patterns within tissues,offering unprecedented insights into cellular functions and disease pathology.Common methods for deriving spatial relationships include density-based methods(quadrat analysis,kernel density estimators)and distance-based methods(nearest-neighbor distance[NND],Ripley’s K function).While density-based methods are effective for visualization,they struggle with quantification due to sensitivity to parameters and complex significance tests.In contrast,distance-based methods offer robust frameworks for hypothesis testing,quantifying spatial clustering or dispersion,and facilitating comparisons with models such as uniform random distributions or Poisson processes[1,2].展开更多
基金Daniel Shafiee Kermany,Ju Young Ahn,Matthew Vasquez,Lin Wang,Kai Liu,Raksha Raghunathan,Jianting Sheng,Hong Zhao,and Stephen Tin Chi Wong are supported by NCI U01CA252553,NCI R01CA238727,NCI R01CA177909,NCI R01CA244413John S.Dunn Research Foundation,and Ting Tsung and Wei Fong Chao Foundation+3 种基金Xiang Hong-Fei Zhang,Zhan Xu,Xiaoxin Hao,Weijie Zhang are supported by US Department of Defense DAMD W81XWH-16-1-0073(Era of Hope Scholarship)NCI R01CA183878,NCI R01CA251950,NCI U01CA252553,DAMD W81XWH-20-1-0375Breast Cancer Research Foundation,and McNair Medical Institute.Vahid Afshar-Kharghan,Min Soon Cho,Wendolyn Carlos-AlcaldeHani Lee are supported by NCI R01CA177909,NCI R01CA016672,NCI R01CA275762,and NCI P50CA217685.
文摘Spatial statistics are crucial for analyzing clustering patterns in various spaces,such as the distribution of trees in a forest or stars in the sky.Advances in spatial biology,such as single-cell spatial transcriptomics,enable researchers to map gene expression patterns within tissues,offering unprecedented insights into cellular functions and disease pathology.Common methods for deriving spatial relationships include density-based methods(quadrat analysis,kernel density estimators)and distance-based methods(nearest-neighbor distance[NND],Ripley’s K function).While density-based methods are effective for visualization,they struggle with quantification due to sensitivity to parameters and complex significance tests.In contrast,distance-based methods offer robust frameworks for hypothesis testing,quantifying spatial clustering or dispersion,and facilitating comparisons with models such as uniform random distributions or Poisson processes[1,2].
