{"title":"BFAST: joint dimension reduction and spatial clustering with Bayesian factor analysis for zero-inflated spatial transcriptomics data.","authors":"Yang Xu, Dian Lv, Xuanxuan Zou, Liang Wu, Xun Xu, Xin Zhao","doi":"10.1093/bib/bbae594","DOIUrl":null,"url":null,"abstract":"<p><p>The development of spatially resolved transcriptomics (ST) technologies has made it possible to measure gene expression profiles coupled with cellular spatial context and assist biologists in comprehensively characterizing cellular phenotype heterogeneity and tissue microenvironment. Spatial clustering is vital for biological downstream analysis. However, due to high noise and dropout events, clustering spatial transcriptomics data poses numerous challenges due to the lack of effective algorithms. Here we develop a novel method, jointly performing dimension reduction and spatial clustering with Bayesian Factor Analysis for zero-inflated Spatial Transcriptomics data (BFAST). BFAST has showcased exceptional performance on simulation data and real spatial transcriptomics datasets, as proven by benchmarking against currently available methods. It effectively extracts more biologically informative low-dimensional features compared to traditional dimensionality reduction approaches, thereby enhancing the accuracy and precision of clustering.</p>","PeriodicalId":9209,"journal":{"name":"Briefings in bioinformatics","volume":"25 6","pages":""},"PeriodicalIF":6.8000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570543/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Briefings in bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/bib/bbae594","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
The development of spatially resolved transcriptomics (ST) technologies has made it possible to measure gene expression profiles coupled with cellular spatial context and assist biologists in comprehensively characterizing cellular phenotype heterogeneity and tissue microenvironment. Spatial clustering is vital for biological downstream analysis. However, due to high noise and dropout events, clustering spatial transcriptomics data poses numerous challenges due to the lack of effective algorithms. Here we develop a novel method, jointly performing dimension reduction and spatial clustering with Bayesian Factor Analysis for zero-inflated Spatial Transcriptomics data (BFAST). BFAST has showcased exceptional performance on simulation data and real spatial transcriptomics datasets, as proven by benchmarking against currently available methods. It effectively extracts more biologically informative low-dimensional features compared to traditional dimensionality reduction approaches, thereby enhancing the accuracy and precision of clustering.
期刊介绍:
Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data.
The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.