Statistics and data science in imaging最新文献

筛选

英文中文

Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade Serous Ovarian and Triple Negative Breast Cancer. 单细胞多重成像数据的基准空间共定位方法在高级别浆液性卵巢癌和三阴性乳腺癌中的应用。

Statistics and data science in imaging Pub Date : 2025-01-01 Epub Date: 2025-01-15 DOI: 10.1080/29979676.2024.2437947

Alex C Soupir, Ishaan V Gadiyar, Bryan R Helm, Coleman R Harris, Simon N Vandekar, Lauren C Peres, Robert J Coffey, Julia Wrobel, Siyuan Ma, Brooke L Fridley

{"title":"Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade Serous Ovarian and Triple Negative Breast Cancer.","authors":"Alex C Soupir, Ishaan V Gadiyar, Bryan R Helm, Coleman R Harris, Simon N Vandekar, Lauren C Peres, Robert J Coffey, Julia Wrobel, Siyuan Ma, Brooke L Fridley","doi":"10.1080/29979676.2024.2437947","DOIUrl":"10.1080/29979676.2024.2437947","url":null,"abstract":"Single-cell multiplex imaging (scMI) measures cell locations and phenotypes within a tissue and can be used to understand the tumor microenvironment. In scMI studies, it is often of interest to quantify spatial co-localization of immune cells and its association with clinical outcomes; however, it remains unknown which of the many available spatial indices have adequate power to detect spatial within-sample co-localization and its association with patient outcomes, such as survival. In this study, the performance of six frequentist metrics of spatial co-localization used in scMI studies were evaluated. Simulated data was used to assess the power and type I error of these spatial metrics to detect signficant co-localization. Furthermore, these spatial co-localization methods were applied to two scMI studies - a high-grade serous ovarian cancer (HGSOC) study and triple negative breast cancer (TNBC) study - to detect within-sample co-localization between cell types and their sensitivity to detect differences in survival across samples. In the simulation study, Ripley's K had the greatest power to identify co-localization followed closely by pair correlation g; all other statistics showed little power across all simulation scenarios. In the application of the methods to cancer studies, the results consistently point to pair correlation g and Ripley's K as indices with the most power for detecting significant co-localization in scMI data. Furthermore, pair correlation g, Ripley's K, and the scLMM index were most effective for estimating between-sample associations between level of co-localization and survival.","PeriodicalId":520439,"journal":{"name":"Statistics and data science in imaging","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11883755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0