{"title":"合成 DNA 条形码识别 scRNA-seq 数据集中的单体并评估双体算法。","authors":"Ziyang Zhang, Madeline E Melzer, Keerthana M Arun, Hanxiao Sun, Carl-Johan Eriksson, Itai Fabian, Sagi Shaashua, Karun Kiani, Yaara Oren, Yogesh Goyal","doi":"10.1016/j.xgen.2024.100592","DOIUrl":null,"url":null,"abstract":"<p><p>Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, \"singletCode,\" to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.</p>","PeriodicalId":72539,"journal":{"name":"Cell genomics","volume":" ","pages":"100592"},"PeriodicalIF":11.1000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293576/pdf/","citationCount":"0","resultStr":"{\"title\":\"Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.\",\"authors\":\"Ziyang Zhang, Madeline E Melzer, Keerthana M Arun, Hanxiao Sun, Carl-Johan Eriksson, Itai Fabian, Sagi Shaashua, Karun Kiani, Yaara Oren, Yogesh Goyal\",\"doi\":\"10.1016/j.xgen.2024.100592\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, \\\"singletCode,\\\" to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.</p>\",\"PeriodicalId\":72539,\"journal\":{\"name\":\"Cell genomics\",\"volume\":\" \",\"pages\":\"100592\"},\"PeriodicalIF\":11.1000,\"publicationDate\":\"2024-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293576/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cell genomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xgen.2024.100592\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell genomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.xgen.2024.100592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/25 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
单细胞 RNA 测序(scRNA-seq)数据集除了包含真正的单细胞(或称单细胞)外,还包含在测序过程中聚合的细胞(或称双细胞)。在 scRNA-seq 中高保真地识别单细胞是避免假阴性和假阳性发现的必要条件。虽然已经提出了几种方法,但它们通常都是在高度异构的数据集上进行测试,缺乏对真正单体的先验知识。在这里,我们利用带有合成引入的 DNA 条形码的数据集进行了一项迄今为止尚未探索过的应用:提取地面真实单体。我们展示了我们的框架 "singletCode "的可行性,以评估各种情况下的现有双码检测方法。我们还利用我们的地面实况单点来训练一个概念验证机器学习分类器,该分类器的性能优于其他双重检测算法。我们的综合框架可以识别地面实况单字,并在非条码数据集中实现稳健的双字检测。
Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms.
Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.