Statistical analysis of multiple regions-of-interest in multiplexed spatial proteomics data

Abstract Multiplexed spatial proteomics reveals the spatial organization of cells in tumors, which is associated with important clinical outcomes such as survival and treatment response. This spatial organization is often summarized using spatial summary statistics, including Ripley’s K and Besag’s...

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Veröffentlicht in:	Briefings in bioinformatics 2024-09, Vol.25 (6)
Hauptverfasser:	Samorodnitsky, Sarah, Wu, Michael C
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Breast cancer Cancer Carcinoma, Non-Small-Cell Lung - genetics Carcinoma, Non-Small-Cell Lung - metabolism Carcinoma, Non-Small-Cell Lung - pathology Cell size Cell survival Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - genetics Colorectal Neoplasms - metabolism Colorectal Neoplasms - pathology Data Interpretation, Statistical Humans Lung cancer Lung Neoplasms - genetics Lung Neoplasms - metabolism Lung Neoplasms - pathology Multiplexing Neoplasms - genetics Neoplasms - metabolism Non-small cell lung carcinoma Performance evaluation Problem Solving Protocol Proteomics Proteomics - methods Resampling Small cell lung carcinoma Spatial data Statistical analysis Summaries Triple Negative Breast Neoplasms - genetics Triple Negative Breast Neoplasms - metabolism Triple Negative Breast Neoplasms - pathology Tumors
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