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The method models and removes effects of adjustment variables while preserving biological variables targeted for analysis. It introduces the study design as biological variables and adjustment variables, formulates a linear model for probe intensities, and describes input options, outputs, normalization variants, diagnostic checking, and simulation details for single- and double-channel data, including reference and preprocessed designs.",{"@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/bioconductor-snm-package-tutorial/291011/",{"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/bioconductor-snm-package-tutorial/291011.png","ImageObject",300,407,{"name":92,"@type":93},"Anna Hans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of the snm package?","Question",{"text":112,"@type":113},"The snm package provides supervised normalization for microarray data by modeling and removing systematic variation attributed to adjustment variables while not affecting biological variables of interest.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the tutorial describe biological vs. adjustment variables?",{"text":117,"@type":113},"Biological variables represent factors targeted by the analysis, such as disease status or treatment, while adjustment variables capture other systematic complexities driven by the study design.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the microarray intensity model structured?",{"text":121,"@type":113},"For each probe i and array j, the tutorial expresses the observed intensity as a linear combination of biological-variable effects, adjustment-variable effects, intensity-dependent smooth effects, and an error term, then extends this model across all arrays into matrix form.","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},291011,1791433077,{"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":24,"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":41,"preview_page_count":4},5909892332657,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Bioconductor✬s snm package  \nBrig Mecham and John D. Storey  \nemail: [jstorey@princeton.edu](jstorey@princeton.edu)  \nNovember 1, 2022  \nContents  \n1 Overview 1  \n2 Introduction 2  \n3 Basic Usage 3  \n3.1 Input ............................................. 3  \n3.2 Output ............................................ 4  \n3.3 Single Channel Normalization ............................... 5  \n3.4 Double Channel Normalization .............................. 7  \n3.5 Reference Design ...................................... 7  \n3.6 Preprocessed Normalization ................................ 8  \n4 Diagnosing the snm output 8  \n5 Simulation Details 10  \n5.1 Single Channel Data .................................... 11  \n5.2 Double Channel Data ................................... 11  \n5.3 Reference Design ...................................... 11  \n5.4 Preprocessed Data ..................................... 12  \n1 Overview  \nThis document provides a tutorial for using the snm package. The package consists of a single main function, snm, for normalizing data based on a set of study-speci✜c variables. Many questions about snm will hopefully be answered by the documentation or references. As with any R package, detailed information on functions, their arguments and values, can be obtained in the help ✜les. To view the help ✜le for the function snm within R, type ?snm. If you identify bugs related to basic usage please contact the authors directly. Otherwise, any questions or problems regarding snm will most e✣ciently be addressed on the Bioconductor mailing list, [http://stat.ethz.ch/mailman/listinfo/bioconductor](http://stat.ethz.ch/mailman/listinfo/bioconductor).  \nThe remainder of this document is organized as follows. Section 2 provides a brief introduction to the concepts and ideas that underlie this software package. In section 3 we provide three examples intended to demonstrate how to use the snm package to normalize microarray data. Section 4 contains a description of how to diagnose any problems with the normalized data returned from an  \nsnm function call using a simple diagnostic plot. In section 3.1 we cover some of the snm options that can be set for more control. Finally, in section 5 we provide details on the simulated data sets explored in section three.  \n2 Introduction  \nThe snm package contains functions for implementing supervised normalization of microrarray data. Within any given study, we identify and model the sources of systematic variation with study-speci✜c variables, which are driven by the study design. These study-speci✜c variables fall into one of two categories: biological variables or adjustment variables. We de✜ne biological variables to be those whose relationships with nucleic acid variation are the target of the analysis. The other variables, which arise from complexities of the biological or experimental paradigm from which the data are generated, are what we call adjustment variables. SNM is designed to model and remove the e✛ects of adjustment variables on the data without in✢uencing the biological variables of interest. This algorithm was described in detail in [2] .  \nThe complete data from a microarray experiment consists of three terms: the observed probe intensities, biological variables, and adjustment variables. The intensities are usually presented asan m 􀀂 n matrix, Y, where m and n describe the number of probes and arrays in the entire study, respectively. De✜ne y ij as the observed intensity for probe i = 1; :::; m on array j = 1; ::; n, andyi = (yi1; yi2; :::; yin) as the observed intensities for probe i across the n arrays. The kth biological variable for array j , x kj, describes factors of interest such as disease status, experimental treatment, or time point. All d covariates for an individual sample j are denoted by the vector xj, and we group all n such vectors into a d 􀀂 n matrix X = (x1 ; :::; xn) .  \nSimilarly, the `th adjustment variable for array j , z `j, parameterizes variables to be","cbCairCEFXfcS2iz","https://ap.wps.com/l/cbCairCEFXfcS2iz","pdf",396423,12,"English","# Contents\n## 1 Overview\n## 2 Introduction\n## 3 Basic Usage\n## 4 Diagnosing the snm output\n## 5 Simulation Details","[{\"question\":\"What is the purpose of the snm package?\",\"answer\":\"The snm package provides supervised normalization for microarray data by modeling and removing systematic variation attributed to adjustment variables while not affecting biological variables of interest.\"},{\"question\":\"How does the tutorial describe biological vs. adjustment variables?\",\"answer\":\"Biological variables represent factors targeted by the analysis, such as disease status or treatment, while adjustment variables capture other systematic complexities driven by the study design.\"},{\"question\":\"How is the microarray intensity model structured?\",\"answer\":\"For each probe i and array j, the tutorial expresses the observed intensity as a linear combination of biological-variable effects, adjustment-variable effects, intensity-dependent smooth effects, and an error term, then extends this model across all arrays into matrix form.\"}]","Bioconductor snm package - Tutorial | PDF",1789643177]