摘要
目的探讨空间主成分分析(spatial principal component analysis,spatial PCA)在卵巢癌(ovarian cancer,OC)空间转录组学数据空间域识别中的应用,识别在基因表达和组织学上空间一致的区域,检测不同组织区域基因表达的异质性。方法采用spatial PCA对卵巢癌10x空间转录组学数据进行空间域识别,并与BASS、STAGATE两种空间域识别方法作比较;绘制RGB图可视化降维后的低维成分;筛选空间可变基因(spatially variable genes,SVGs),进行差异表达(differential expression,DE)分析和功能富集分析。结果spatial PCA识别出8个卵巢癌空间域,RGB图像显示空间域识别结果对数据缩放稳定,且相邻区域颜色相似;检测到每个空间域SVGs数量范围为112~2928个,筛选出差异具有统计学意义的1个GO生物学过程和3个蛋白质复合物。结论spatialPCA可以更准确地识别空间域聚类,筛选出的潜在生物标志物及通路,为卵巢癌的异质性研究及针对性治疗提供了依据。
Objective Identify spatial domains in spatial transcriptomic data of ovarian cancer using the spatial dimension reduction method called spatial principle component analysis(spatial PCA).To evaluate the application of spatial principal component analysis(spatial PCA)for spatial domain identification in ovarian cancer(OC)spatial transcriptomics data.Identify spatially coherent regions in gene expression and histology and then detect the heterogeneity in gene expression across different tissue regions.Methods Perform spatial domain identification on ovarian cancer 10x spatial transcriptomic data using spatial PCA,and compare it with two other methods of spatial domain identification,BASS and STAGATE.Visualize the low-dimensional components after dimensionality reduction by plotting an RGB image.Filter spatially variable genes(SVGs)and perform differential expression(DE)and RGB plots were generated to visualize low-dimensional embeddings.Spatially variable genes(SVGs)were identified,followed by differential expression(DE)and functional enrichment analysis.Results Eight spatial domains were identified in ovarian cancer using spatial PCA.The RGB image displays stable results of spatial domain identification across data scales,with similar colors in adjacent regions.The number of SVGs in each domain ranging from 112 to 2928.One GO biological process and three protein complexes with statistically significant were selected.Conclusion spatial PCA can more accurately identify spatial domain clustering.The identified potential biomarkers and pathways provide a basis for the study of heterogeneity and targeted therapies for ovarian cancer.
作者
刘改琴
杨琪
田雅昕
贾聪聪
房瑞玲
余红梅
张岩波
曹红艳
Liu Gaiqin;Yang Qi;Tian Yaxin(Division of Health Statistics,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment,Shanxi Medical University,Taiyuan 030001)
出处
《中国卫生统计》
北大核心
2025年第6期843-848,855,共7页
Chinese Journal of Health Statistics
基金
国家自然科学基金(82473739,82273742)
山西省基础研究计划(202303021211130)
山西省回国留学人员科研资助项目(2024-081)
山西省高等教育“百亿工程”科技引导专项。
关键词
spatial
PCA降维
空间转录组
空间域识别
卵巢癌
Spatial PCA dimensionality reduction
Spatial transcriptomics
Spatial domain identification
Ovarian cancer
