[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126348-en":3,"doc-seo-126348-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126348,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","STIGMA - Single-cell tissue-specific gene prioritization using machine learning","STIGMA is a machine learning framework designed to prioritize candidate genes underlying congenital diseases when many genes remain functionally uncharacterized. It leverages single-cell RNA-seq data to model temporal dynamics of gene expression across cell types during healthy organogenesis. By learning tissue-specific expression heterogeneity, STIGMA improves over prior scoring methods that do not account for variability among cell subpopulations. Validation on mouse limb and human fetal heart single-cell datasets shows effective prioritization of disease-associated variants and genes.","i An update to this article is included at the end  \n ARTICLE  \nSTIGMA: Single-cell tissue-specific gene prioritization using machine learning  \nGraphical abstract Authors  \nSaranya Balachandran, Cesar A. Prada-Medina,  \nMartin A. Mensah, ..., Martin Kircher, Varun K.A. Sreenivasan,  \nMalte Spielmann  \nCorrespondence  \n[varun.sreenivasan@uksh.de](varun.sreenivasan@uksh.de) (V.K.A.S.),  \n[malte.spielmann@uksh.de](malte.spielmann@uksh.de) (M.S.)  \nSingle-cell tissue-specific gene prioritization using machine learning (STIGMA) is an approach to prioritize candidate genes for congenital diseases. STIGMA uses single-cell RNA-seq data to capture the dynamics of gene expression within cell populations across developmental time, making it a powerful tool for the discovery of disease-associated genes.  \nBalachandran et al., 2024, The American Journal of Human Genetics 111, 338–349  \nFebruary 1, 2024 􀀁 2023 The Authors.  \n[https://doi.org/10.1016/j.ajhg.2023.12.011](https://doi.org/10.1016/j.ajhg.2023.12.011)  \nll  \nSTIGMA: Single-cell tissue-specific  \ngene prioritization using machine learning  \nSaranya Balachandran, 1 Cesar A. Prada-Medina,2, 11 Martin A. Mensah,3,4,5 Juliane Glaser, 10 Naseebullah Kakar, 1,6 Inga Nagel, 1 Jelena Pozojevic, 1 Enrique Audain,7,8,9 Marc-Phillip Hitz, 7,8,9 Martin Kircher, 1 Varun K.A. Sreenivasan,1,* and Malte Spielmann 1,2,8,*  \nSummary  \nClinical exome and genome sequencing have revolutionized the understanding of human disease genetics. Yet many genes remain functionally uncharacterized, complicating the establishment of causal disease links for genetic variants. While several scoring methods have been devised to prioritize these candidate genes, these methods fall short of capturing the expression heterogeneity across cell subpopulations within tissues. Here, we introduce single-cell tissue-specific gene prioritization using machine learning (STIGMA), an approach that leverages single-cell RNA-seq (scRNA-seq) data to prioritize candidate genes associated with rare congenital diseases. STIGMA prioritizes genes by learning the temporal dynamics of gene expression across cell types during healthy organogenesis. To assess the efficacy of our framework, we applied STIGMA to mouse limb and human fetal heart scRNA-seq datasets. In a cohort of individuals with congenital limb malformation, STIGMA prioritized 469 variants in 345 genes, with UBA2 as a notable example. For congenital heart defects, we detected 34 genes harboring nonsynonymous de novo variants (nsDNVs) in two or more individuals from a set of 7,958 individuals, including the ortholog of Prdm1, which is associated with hypoplastic left ventricle and hypoplastic aortic arch. Overall, our findings demonstrate that STIGMA effectively prioritizes tissue-specific candidate genes by utilizing singlecell transcriptome data. The ability to capture the heterogeneity of gene expression across cell populations makes STIGMA a powerful tool for the discovery of disease-associated genes and facilitates the identification of causal variants underlying human genetic disorders.  \nIntroduction  \nThe widespread introduction of next-generation sequencing approaches has rendered the analysis of genes a routine in the clinical setting. It has benefited the ongoing discovery, functional annotation, and disease mappings of genes (e.g., HPO,1 OMIM2)3 as well as improvements in tools and resources to call, annotate, prioritize, and filter variants within these genes (e.g., gnomAD,4 DECIPHER5) . As a result, the diagnostic yield with genome or exome sequencing has been steadily increasing, recently reaching 41% .3 However, to date, a causal disease link has been established for variants in only about 5,000 genes.2,6 Consequently, many potentially deleterious variants in genes of unknown function are classified as variants of uncertain significance (VUSs) and do not contribute to a diagnosis of rare diseases, until further validated by experimental verification, e.g., using in situ hy","cbCaiqrY7mmlfHkD","https://ap.wps.com/l/cbCaiqrY7mmlfHkD","pdf",3497959,6,1,15,"English","en",105,"# Summary\n## Clinical background and need for gene prioritization\n## STIGMA approach using single-cell RNA-seq\n## Evaluation on mouse and human datasets","[{\"question\":\"What problem does STIGMA address in congenital disease genetics?\",\"answer\":\"STIGMA addresses the challenge that many genes remain functionally uncharacterized, making it difficult to establish causal links between genetic variants and rare congenital diseases.\"},{\"question\":\"How does STIGMA prioritize candidate genes?\",\"answer\":\"STIGMA uses single-cell RNA-seq data to learn temporal dynamics of gene expression across cell types during healthy organogenesis, producing tissue-specific gene rankings.\"},{\"question\":\"How was STIGMA validated?\",\"answer\":\"The framework was applied to mouse limb and human fetal heart single-cell RNA-seq datasets, where it prioritized variants and genes relevant to congenital limb malformation and congenital heart defects.\"}]","STIGMA - Single-cell tissue-specific gene prioritization using machine learning | PDF",1785904603,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"stigma-single-cell-tissue-specific-gene-prioritization-using-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/stigma-single-cell-tissue-specific-gene-prioritization-using-machine-learning/126348/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does STIGMA address in congenital disease genetics?","Question",{"text":77,"@type":78},"STIGMA addresses the challenge that many genes remain functionally uncharacterized, making it difficult to establish causal links between genetic variants and rare congenital diseases.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does STIGMA prioritize candidate genes?",{"text":82,"@type":78},"STIGMA uses single-cell RNA-seq data to learn temporal dynamics of gene expression across cell types during healthy organogenesis, producing tissue-specific gene rankings.",{"name":84,"@type":75,"acceptedAnswer":85},"How was STIGMA validated?",{"text":86,"@type":78},"The framework was applied to mouse limb and human fetal heart single-cell RNA-seq datasets, where it prioritized variants and genes relevant to congenital limb malformation and congenital heart defects.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]