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A simple two-tissue model links transcript abundance to tissue composition and shows how scaling relationships distort relative expression. Parameterized with biologically realistic variables and real examples, the study highlights key experimental design and analysis practices to reduce false inferences of regulatory differences.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/tissue-scaling-and-transcript-abundance-inferring-regulatory-change-from-gene-expression/278104/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/tissue-scaling-and-transcript-abundance-inferring-regulatory-change-from-gene-expression/278104.png","ImageObject",442,249,{"name":88,"@type":89},"Anda","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-21","2026-09-15",true,{"@type":98,"interactionType":99,"userInteractionCount":76},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"Why can differential transcript abundance be misinterpreted as regulatory change?","Question",{"text":108,"@type":109},"Because RNA-Seq comparisons from heterogeneous tissue samples can produce variation in transcript abundance due to tissue composition, even when regulatory regulation has not changed.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What does the two-tissue model in the study demonstrate?",{"text":113,"@type":109},"It describes how transcript abundance depends on tissue composition in a two-tissue system and how that relationship varies under different scaling relationships.",{"name":115,"@type":106,"acceptedAnswer":116},"How do tissue scaling effects influence inferred relative expression?",{"text":117,"@type":109},"The results show tissue scaling can substantially affect relative transcript abundance, potentially overwhelming signals intended to reflect regulatory differences.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},278104,1789507002,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":140},962075006959,"https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297","1 Inferring regulatory change from gene expression: the confounding  \n2 effects oftissue scaling  \n3  \n4 Stephen H. Montgomery 1,2 and Judith E. Mank 1 5  \n6 1 Dept. Genetics, Evolution and Environment, University College London, London  \n7 WC1E 6BT, UK  \n8 2 Corresponding author: [Stephen.Montgomery@cantab.net](Stephen.Montgomery@cantab.net)  \n9  \n10 Key words: allometry, isometry, grade-shifts, gene expression, RNA-Seq, regulatory  \n11 evolution  \n12  \n13 Running title: Tissue scaling and transcript abundance 14  \n15  \n16  \n17  \n18  \n19  \n20  \n21  \n22  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34 Abstract  \n35 Comparative studies of gene expression are often designed with the aim of identifying  \n36 regulatory changes associated with phenotypic variation. In recent years large-scale  \n37 transcriptome sequencing methods have increasingly been applied to non-model  \n38 organisms to ask important ecological or evolutionary questions. Although  \n39 experimental design varies, many of these studies have been based on RNA libraries  \n40 obtained from heterogeneous tissue samples, for example homogenised whole bodies.  \n41 Comparisons between groups of samples that vary in tissue composition can introduce  \n42 sufficient variation in RNA abundance to produce patterns of differential expression  \n43 that are mistakenly interpreted as evidence of regulatory differences. Here we present  \n44 a simple model that demonstrates this effect. The model describes the relationship  \n45 between transcript abundance and tissue composition in a two-tissue system, and how  \n46 this relationship varies under different scaling relationships. Using a range of  \n47 biologically realistic variables, including real biological examples, to parameterise the  \n48 model we highlight the potentially severe influence of tissue scaling on relative  \n49 transcript abundance. We use these results to identify key aspects of experimental  \n50 design and analysis that can help to limit the influence of tissue scaling on the  \n51 inference of regulatory difference from comparative studies of gene expression. 52  \n53  \n54  \n55  \n56  \n57  \n58  \n59  \n60  \n61  \n62  \n63  \n64  \n65 Introduction  \n66 A substantial amount of intra- and inter-specific diversity results from regulatory  \n67 variation. Within species, a single genome can encode multiple distinct phenotypes by  \n68 varying expression levels for the underlying loci. Examples of regulatory-based  \n69 phenotypes include social insect castes (Toth et al. 2008), some instances of plastic  \n70 alternative morphs such as dominant and subordinate turkeys (Pointer et al. 2013) or  \n71 territorial, satellite and sneaker males in wrasses (Alonzo et al. 2000; Stiver et al.  \n72 2015), caring and non-caring in beetles (Parker et al. 2015), and a substantial  \n73 proportion of differences between males and females (Moczek & Rose 2009; Khila et 74 al. 2012) . Similarly, across species or divergent populations, gene regulation provides  \n75 an important route for the evolution of diversity (Carroll 2008; Stern & Orgogozo 76 2008) with many adaptive phenotypic changes linked to regulatory evolution (e.g.  \n77 Shapiro et al. 2004; Steiner et al. 2007) .  \n78 Given the importance of regulatory variation in shaping phenotypic diversity, 79 transcriptome analyses based on RNA-Seq methods are increasingly used in  \n80 evolutionary and ecological studies with the explicit aim of identifying genes that  \n81 underlie phenotypic variation. These studies assume that differential gene expression  \n82 is the result of altered transcriptional regulation which lead to phenotypic differences  \n83 between groups of individuals. In many cases functional validation experiments have  \n84 demonstrated causative relationships between variation in gene expression and  \n85 variation in phenotypic development (e.g Abzhanov et al. 2006; Khila et al. 2012) .  \n86 However, functional validation is often inhibited by the polygenic nature of","cbCailhp5U3u8pEi","https://ap.wps.com/l/cbCailhp5U3u8pEi","pdf",1375644,37,"English","# Abstract\n# Introduction\n## Regulatory variation and phenotypic diversity\n## RNA-Seq based inference of regulatory change\n## Non-regulatory sources: tissue composition effects\n## Experimental designs and tissue homogenization","[{\"question\":\"Why can differential transcript abundance be misinterpreted as regulatory change?\",\"answer\":\"Because RNA-Seq comparisons from heterogeneous tissue samples can produce variation in transcript abundance due to tissue composition, even when regulatory regulation has not changed.\"},{\"question\":\"What does the two-tissue model in the study demonstrate?\",\"answer\":\"It describes how transcript abundance depends on tissue composition in a two-tissue system and how that relationship varies under different scaling relationships.\"},{\"question\":\"How do tissue scaling effects influence inferred relative expression?\",\"answer\":\"The results show tissue scaling can substantially affect relative transcript abundance, potentially overwhelming signals intended to reflect regulatory differences.\"}]","Tissue scaling and transcript abundance - Inferring regulatory change from gene expression | PDF",13]