{"title":"SNPmanifold:检测单细胞克隆和谱系从单核苷酸变异使用二项变分自编码器","authors":"Hoi Man Chung, Yuanhua Huang","doi":"10.1186/s13059-025-03803-3","DOIUrl":null,"url":null,"abstract":"Single-nucleotide-variant (SNV) clone assignment of high-covariance single-cell lineage tracing data remains a challenge due to hierarchical mutation structure and many missing signals. We develop SNPmanifold, a Python package that learns an SNV embedding manifold using a binomial variational autoencoder to give an efficient and interpretable cell-cell distance metric. We demonstrate that SNPmanifold is a suitable tool for analysis of complex, single-cell SNV mutation data, such as in the context of demultiplexing a large number of donors and somatic lineage tracing via mitochondrial SNV data and can reveal insights into single-cell clonality and lineages more accurately and comprehensively than existing methods.","PeriodicalId":12611,"journal":{"name":"Genome Biology","volume":"73 1","pages":""},"PeriodicalIF":10.1000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SNPmanifold: detecting single-cell clonality and lineages from single-nucleotide variants using binomial variational autoencoder\",\"authors\":\"Hoi Man Chung, Yuanhua Huang\",\"doi\":\"10.1186/s13059-025-03803-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Single-nucleotide-variant (SNV) clone assignment of high-covariance single-cell lineage tracing data remains a challenge due to hierarchical mutation structure and many missing signals. We develop SNPmanifold, a Python package that learns an SNV embedding manifold using a binomial variational autoencoder to give an efficient and interpretable cell-cell distance metric. We demonstrate that SNPmanifold is a suitable tool for analysis of complex, single-cell SNV mutation data, such as in the context of demultiplexing a large number of donors and somatic lineage tracing via mitochondrial SNV data and can reveal insights into single-cell clonality and lineages more accurately and comprehensively than existing methods.\",\"PeriodicalId\":12611,\"journal\":{\"name\":\"Genome Biology\",\"volume\":\"73 1\",\"pages\":\"\"},\"PeriodicalIF\":10.1000,\"publicationDate\":\"2025-09-26\",\"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/s13059-025-03803-3\",\"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/s13059-025-03803-3","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}
SNPmanifold: detecting single-cell clonality and lineages from single-nucleotide variants using binomial variational autoencoder
Single-nucleotide-variant (SNV) clone assignment of high-covariance single-cell lineage tracing data remains a challenge due to hierarchical mutation structure and many missing signals. We develop SNPmanifold, a Python package that learns an SNV embedding manifold using a binomial variational autoencoder to give an efficient and interpretable cell-cell distance metric. We demonstrate that SNPmanifold is a suitable tool for analysis of complex, single-cell SNV mutation data, such as in the context of demultiplexing a large number of donors and somatic lineage tracing via mitochondrial SNV data and can reveal insights into single-cell clonality and lineages more accurately and comprehensively than existing methods.
Genome BiologyBiochemistry, 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.