[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-328814-105":59,"doc-detail-328814-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","identifying-cancer-cells-from-calling-single-nucleotide-variants-in-scrna-seq-data","Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data","","Single-cell RNA sequencing (scRNA-seq) helps study cancer cell states and heterogeneity, yet tumor microenvironments mix healthy and cancerous cells, making separation difficult from transcriptomics alone. Somatic single-nucleotide variants (SNVs) in scRNA-seq could label cancer cells, but SNV calling is challenging due to uncertainty, sparsity, and the prevalence of germline variants, RNA edits, and sequencing or processing errors. CCLONE addresses these issues by jointly identifying cancer clonal populations and their associated variants across multiple datasets.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/identifying-cancer-cells-from-calling-single-nucleotide-variants-in-scrna-seq-data/328814/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/identifying-cancer-cells-from-calling-single-nucleotide-variants-in-scrna-seq-data/328814.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-26","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is identifying cancer cells from scRNA-seq data difficult?","Question",{"text":112,"@type":113},"Tumor microenvironments usually contain mixtures of healthy and cancerous cells, and transcriptomics alone often cannot fully separate these populations.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What makes somatic SNV calling in scRNA-seq data challenging?",{"text":117,"@type":113},"Most variants in short-read scRNA-seq data may be germline rather than somatic, can reflect RNA edits or transcription-related effects, and are affected by sequencing and processing errors; additionally, only variants in actively transcribed regions are observed.",{"name":119,"@type":110,"acceptedAnswer":120},"How does CCLONE address these challenges?",{"text":121,"@type":113},"CCLONE is an interpretable tool designed to handle uncertainty and sparsity of SNVs called from scRNA-seq data. It jointly identifies cancer clonal populations and their associated variants.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},328814,1790243622,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Gene expression  \nIdentifying cancer cells from calling single-nucleotide variants in scRNA-seq data  \nValrie Marot-Lassauzaie 1,2, Sergi Beneyto-Calabuig3,4, Benedikt Obermayer5, Lars Velten3,4, Dieter Beule5,6, Laleh Haghverdi 1, 􀀃  \n1 Max-Delbr€uck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Hannoversche Str. 28, 10115 Berlin, Germany  \n2Charit – Universit€atsmedizin Berlin, Corporate Member of Freie Universit€at Berlin and Humboldt-Universit€at zu Berlin, Charitplatz 1, 10117 Berlin, Germany  \n3Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003 Barcelona, Spain 4 Universitat Pompeu Fabra (UPF), Barcelona, Spain  \n5Core Unit Bioinformatics, Berlin Institute of Health at Charit – Universit€atsmedizin Berlin, 10117 Berlin, Germany 6 Max-Delbr€uck-Center for Molecular Medicine in the Helmholtz Association (MDC), Robert-R€ossle-Str. 10, 13125 Berlin, Germany  \n􀀃 Corresponding author. Max-Delbr€uck-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Hannoversche Str. 28, 10115 Berlin, Germany. E-mail: [laleh.haghverdi@mdc-berlin.de](laleh.haghverdi@mdc-berlin.de)  \nAssociate Editor: Janet Kelso  \nAbstract  \nMotivation: Single-cell RNA sequencing (scRNA-seq) data are widely used to study cancer cell states and their heterogeneity. However, the tumour microenvironment is usually a mixture of healthy and cancerous cells and it can be difficult to fully separate these two populations based on transcriptomics alone. If available, somatic single-nucleotide variants (SNVs) observed in the scRNA-seq data could be used to identify the cancer population and match that information with the single cells’ expression profile. However, calling somatic SNVs in scRNA-seq data is a challenging task, as most variants seen in the short-read data are not somatic, but can instead be germline variants, RNA edits or transcription, sequencing, or processing errors. In addition, only variants present in actively transcribed regions for each individual cell will be seen in the data. Results: To address these challenges, we develop CCLONE (Cancer Cell Labelling On Noisy Expression), an interpretable tool adapted to handle the uncertainty and sparsity of SNVs called from scRNA-seq data. CCLONE jointly identifies cancer clonal populations, and their associated variants. We apply CCLONE on two acute myeloid leukaemia datasets and one lung adenocarcinoma dataset and show that CCLONE captures both genetic clones and somatic events for multiple patients. These results show how CCLONE can be used to gather insight into the course of the disease and the origin of cancer cells in scRNA-seq data.  \nAvailability and implementation: Source code is [available at](available at github.com/HaghverdiLab/CCLONE)[ github.com/HaghverdiLab/CCLONE](available at github.com/HaghverdiLab/CCLONE).  \n1 Introduction  \nCancer is a multistep process driven by somatic mutations in which healthy cells progressively evolve into cancerous states. Quantifying how cancer cell states differ from healthy states helps us understand this disease and provides potential therapeutic targets. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to study cancer cell states and their heterogeneity. However, the sampled tumour microenvironment is usually a mixture of healthy and cancerous cells (Hanahan and Weinberg 2011), and fully separating these two populations can be difficult based on transcriptomics alone. Measuring mutational status and gene expression in the same single cell would allow us to more accurately identify the cancer population through mutations and relate that information to the observed transcriptional states. However, sequencing the entire genome and transcriptome of the same individual cell is costly and has low throughput (Dey et al. 2015, Macaulay et al. 2015, Cheow et al. 201","cbCaiu4xzcZgvpkC","https://ap.wps.com/l/cbCaiu4xzcZgvpkC","pdf",5983997,11,"English","# Abstract\n## Motivation\n## Results\n## Availability and implementation\n# Introduction","[{\"question\":\"Why is identifying cancer cells from scRNA-seq data difficult?\",\"answer\":\"Tumor microenvironments usually contain mixtures of healthy and cancerous cells, and transcriptomics alone often cannot fully separate these populations.\"},{\"question\":\"What makes somatic SNV calling in scRNA-seq data challenging?\",\"answer\":\"Most variants in short-read scRNA-seq data may be germline rather than somatic, can reflect RNA edits or transcription-related effects, and are affected by sequencing and processing errors; additionally, only variants in actively transcribed regions are observed.\"},{\"question\":\"How does CCLONE address these challenges?\",\"answer\":\"CCLONE is an interpretable tool designed to handle uncertainty and sparsity of SNVs called from scRNA-seq data. It jointly identifies cancer clonal populations and their associated variants.\"}]","Identifying cancer cells from calling single-nucleotide variants in scRNA-seq data | PDF",1789981141,28]