{"title":"NLSDeconv：一种有效的细胞型反褶积方法，用于空间转录组学数据。","authors":"Yunlu Chen, Feng Ruan, Ji-Ping Wang","doi":"10.1093/bioinformatics/btae747","DOIUrl":null,"url":null,"abstract":"<p><strong>Summary: </strong>Spatial transcriptomics (ST) allows gene expression profiling within intact tissue samples but lacks single-cell resolution. This necessitates computational deconvolution methods to estimate the contributions of distinct cell types. This article introduces NLSDeconv, a novel cell-type deconvolution method based on non-negative least squares, along with an accompanying Python package. Benchmarking against 18 existing deconvolution methods on various ST datasets demonstrates NLSDeconv's competitive statistical performance and superior computational efficiency.</p><p><strong>Availability and implementation: </strong>NLSDeconv is freely available at https://github.com/tinachentc/NLSDeconv as a Python package.</p>","PeriodicalId":93899,"journal":{"name":"Bioinformatics (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11696698/pdf/","citationCount":"0","resultStr":"{\"title\":\"NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data.\",\"authors\":\"Yunlu Chen, Feng Ruan, Ji-Ping Wang\",\"doi\":\"10.1093/bioinformatics/btae747\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Summary: </strong>Spatial transcriptomics (ST) allows gene expression profiling within intact tissue samples but lacks single-cell resolution. This necessitates computational deconvolution methods to estimate the contributions of distinct cell types. This article introduces NLSDeconv, a novel cell-type deconvolution method based on non-negative least squares, along with an accompanying Python package. Benchmarking against 18 existing deconvolution methods on various ST datasets demonstrates NLSDeconv's competitive statistical performance and superior computational efficiency.</p><p><strong>Availability and implementation: </strong>NLSDeconv is freely available at https://github.com/tinachentc/NLSDeconv as a Python package.</p>\",\"PeriodicalId\":93899,\"journal\":{\"name\":\"Bioinformatics (Oxford, England)\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-12-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11696698/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioinformatics (Oxford, England)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/bioinformatics/btae747\",\"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 (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/bioinformatics/btae747","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
NLSDeconv: an efficient cell-type deconvolution method for spatial transcriptomics data.
Summary: Spatial transcriptomics (ST) allows gene expression profiling within intact tissue samples but lacks single-cell resolution. This necessitates computational deconvolution methods to estimate the contributions of distinct cell types. This article introduces NLSDeconv, a novel cell-type deconvolution method based on non-negative least squares, along with an accompanying Python package. Benchmarking against 18 existing deconvolution methods on various ST datasets demonstrates NLSDeconv's competitive statistical performance and superior computational efficiency.
Availability and implementation: NLSDeconv is freely available at https://github.com/tinachentc/NLSDeconv as a Python package.
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