空间转录组学数据的可解释空间梯度分析

IF 10.1 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Genome Biology Pub Date : 2025-08-08 DOI:10.1186/s13059-025-03716-1

Qingnan Liang, Luisa Solis Soto, Cara Haymaker, Ken Chen

{"title":"空间转录组学数据的可解释空间梯度分析","authors":"Qingnan Liang, Luisa Solis Soto, Cara Haymaker, Ken Chen","doi":"10.1186/s13059-025-03716-1","DOIUrl":null,"url":null,"abstract":"Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"27 1","pages":""},"PeriodicalIF":10.1000,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"LSGI: interpretable spatial gradient analysis for spatial transcriptomics data\",\"authors\":\"Qingnan Liang, Luisa Solis Soto, Cara Haymaker, Ken Chen\",\"doi\":\"10.1186/s13059-025-03716-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.\",\"PeriodicalId\":12611,\"journal\":{\"name\":\"Genome Biology\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":10.1000,\"publicationDate\":\"2025-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Genome Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s13059-025-03716-1\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Genome Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13059-025-03716-1","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

细胞解剖和信号传导在不同的生态位中有所不同，这可以诱导细胞亚群中基因表达的梯度。这种空间转录组梯度（STG）是肿瘤内异质性的重要来源。我们提出了局部空间梯度推断（LSGI），这是一个计算框架，可以系统地从空间转录组学（ST）数据中识别具有突出的、可解释的stg的空间位置。我们在肿瘤ST数据集中展示了LSGI，并识别了泛癌和肿瘤类型特异性通路，这些通路具有分级模式，突出了与肿瘤内空间转录异质性相关的通路。LSGI支持可解释的STG分析，这可以从越来越多的肿瘤ST数据集中揭示肿瘤生物学的新见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

LSGI: interpretable spatial gradient analysis for spatial transcriptomics data

Cellular anatomy and signaling vary across niches, which can induce gradated gene expressions in subpopulations of cells. Such spatial transcriptomic gradient (STG) makes a significant source of intra-tumor heterogeneity. We present Local Spatial Gradient Inference (LSGI), a computational framework that systematically identifies spatial locations with prominent, interpretable STGs from spatial transcriptomic (ST) data. We demonstrate LSGI in tumor ST datasets and identify pan-cancer and tumor-type specific pathways with gradated patterns, highlighting the ones related to spatial transcriptional intratumoral heterogeneity. LSGI enables interpretable STG analysis, which can reveal novel insights in tumor biology from the increasingly reported tumor ST datasets.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Genome Biology Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

21.00

自引率

3.30%

发文量

241

审稿时长

2 months

期刊介绍： Genome Biology stands as a premier platform for exceptional research across all domains of biology and biomedicine, explored through a genomic and post-genomic lens. With an impressive impact factor of 12.3 (2022),* the journal secures its position as the 3rd-ranked research journal in the Genetics and Heredity category and the 2nd-ranked research journal in the Biotechnology and Applied Microbiology category by Thomson Reuters. Notably, Genome Biology holds the distinction of being the highest-ranked open-access journal in this category. Our dedicated team of highly trained in-house Editors collaborates closely with our esteemed Editorial Board of international experts, ensuring the journal remains on the forefront of scientific advances and community standards. Regular engagement with researchers at conferences and institute visits underscores our commitment to staying abreast of the latest developments in the field.