{"title":"利用转录动力学划分细胞类型","authors":"Yicheng Gao, Qi Liu","doi":"10.1038/s43588-024-00691-8","DOIUrl":null,"url":null,"abstract":"A recent study proposes an approach that integrates unspliced and spliced mRNA count data by leveraging shared biophysical states across cells, offering a more interpretable and consistent framework for determining cell clusters based on transcriptional kinetics.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 9","pages":"657-658"},"PeriodicalIF":12.0000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Delineating cell types with transcriptional kinetics\",\"authors\":\"Yicheng Gao, Qi Liu\",\"doi\":\"10.1038/s43588-024-00691-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A recent study proposes an approach that integrates unspliced and spliced mRNA count data by leveraging shared biophysical states across cells, offering a more interpretable and consistent framework for determining cell clusters based on transcriptional kinetics.\",\"PeriodicalId\":74246,\"journal\":{\"name\":\"Nature computational science\",\"volume\":\"4 9\",\"pages\":\"657-658\"},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature computational science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s43588-024-00691-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-024-00691-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Delineating cell types with transcriptional kinetics
A recent study proposes an approach that integrates unspliced and spliced mRNA count data by leveraging shared biophysical states across cells, offering a more interpretable and consistent framework for determining cell clusters based on transcriptional kinetics.