{"title":"TWCOM:用于推断空间分辨转录组学数据中细胞间通讯的 R 软件包","authors":"Dongyuan Wu, Susmita Datta","doi":"10.1093/bioadv/vbae101","DOIUrl":null,"url":null,"abstract":"\n \n \n The inference of cell-cell communication is important, as it unveils the intricate cellular behaviors at the molecular level, providing crucial insights essential for understanding complex biological processes and informing targeted interventions in various pathological contexts. Here, we present TWCOM, an R package that implements a Tweedie distribution-based model for accurate cell-cell communication inference. Operating under a generalized additive model framework, TWCOM adeptly handles both single-cell resolution and spot-based spatially resolved transcriptomics data, providing a versatile tool for robust biological sample analysis.\n \n \n \n The R package TWCOM is available at https://github.com/dongyuanwu/TWCOM. Comprehensive documentation is included with the package.\n","PeriodicalId":505477,"journal":{"name":"Bioinformatics Advances","volume":"1 9","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"TWCOM: an R package for inference of cell-cell communication on spatially resolved transcriptomics data\",\"authors\":\"Dongyuan Wu, Susmita Datta\",\"doi\":\"10.1093/bioadv/vbae101\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n \\n \\n The inference of cell-cell communication is important, as it unveils the intricate cellular behaviors at the molecular level, providing crucial insights essential for understanding complex biological processes and informing targeted interventions in various pathological contexts. Here, we present TWCOM, an R package that implements a Tweedie distribution-based model for accurate cell-cell communication inference. Operating under a generalized additive model framework, TWCOM adeptly handles both single-cell resolution and spot-based spatially resolved transcriptomics data, providing a versatile tool for robust biological sample analysis.\\n \\n \\n \\n The R package TWCOM is available at https://github.com/dongyuanwu/TWCOM. Comprehensive documentation is included with the package.\\n\",\"PeriodicalId\":505477,\"journal\":{\"name\":\"Bioinformatics Advances\",\"volume\":\"1 9\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics Advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/bioadv/vbae101\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioinformatics Advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioadv/vbae101","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
细胞-细胞通讯的推断非常重要,因为它揭示了分子水平上错综复杂的细胞行为,为理解复杂的生物过程提供了至关重要的见解,并为在各种病理环境下进行有针对性的干预提供了信息。在此,我们介绍 TWCOM,它是一个 R 软件包,用于实现基于特威迪分布的模型,以准确推断细胞间通讯。TWCOM 在广义相加模型框架下运行,能很好地处理单细胞分辨率和基于点的空间分辨率转录组学数据,为稳健的生物样本分析提供了一个多功能工具。 R 软件包 TWCOM 可在 https://github.com/dongyuanwu/TWCOM 上下载。该软件包包含全面的文档。
TWCOM: an R package for inference of cell-cell communication on spatially resolved transcriptomics data
The inference of cell-cell communication is important, as it unveils the intricate cellular behaviors at the molecular level, providing crucial insights essential for understanding complex biological processes and informing targeted interventions in various pathological contexts. Here, we present TWCOM, an R package that implements a Tweedie distribution-based model for accurate cell-cell communication inference. Operating under a generalized additive model framework, TWCOM adeptly handles both single-cell resolution and spot-based spatially resolved transcriptomics data, providing a versatile tool for robust biological sample analysis.
The R package TWCOM is available at https://github.com/dongyuanwu/TWCOM. Comprehensive documentation is included with the package.