[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123307-en":3,"doc-seo-123307-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123307,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Statistical Machine Learning Methods for Integrating Single-Cell Genomics Data","This capstone project integrates two single-cell mouse brain datasets: single-cell high-throughput chromatin conformation (scHi-C) and single-cell RNA sequencing (scRNA-seq). The work recreates the scGAD (single-cell Gene Associating Domain) machine learning algorithm from scratch and evaluates the accuracy of the recreated method. The study is designed to verify that the published scGAD research is correct, enabling further development of new integration algorithms. Step-by-step reconstruction and the conceptual rationale behind each implementation decision guide the analysis.","UC Riverside  \nUCR Honors Capstones 2024-2025  \nTitle  \nStatistical Machine Learning Methods for Integrating Single-Cell Genomics Data  \nPermalink  \n[https://escholarship.org/uc/item/8256n8fv](https://escholarship.org/uc/item/8256n8fv)  \nAuthor  \nBlackwell, Jake D  \nPublication Date  \n2025-07-01  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nSTATISTICAL MACHINE LEARNING METHODS FOR INTEGRATING SINGLE-CELL  \nGENOMICS DATA  \nBy  \nJake Donald Blackwell  \nA capstone project submitted for Graduation with University Honors  \nMay 08, 2025  \nUniversity Honors  \nUniversity of California, Riverside  \nAPPROVED  \nDr. Wenxiu Ma  \nDepartment of Statistics  \nDr. Begoña Echeverria, Howard H Hays Jr. Chair  \nUniversity Honors  \nABSTRACT  \nThis project consists of using two types of single-cell datasets that are scanned from a developing mouse brain: Single-cell high-throughput chromatin conformation (scHi-C) and single-cell RNA sequencing (scRNA-seq). The goal of this project is to integrate the scHi-C data and scRNA-seq data by recreating the machine learning algorithm scGAD (single-cell Gene Associating Domain) from scratch and analyzing the accuracy of the recreated algorithm. We chose to recreate the scGAD algorithm from scratch in order to confirm that the research papers backing this algorithm are correct so that more research can be done into making new integration algorithms. I will explain step-by-step how we were able to accurately recreate the scGAD algorithm while also explaining the conceptual reasoning behind the decisions made at each step.  \nACKNOWLEDGEMENTS  \nI would like to sincerely thank my faculty mentor Dr. Wenxiu Ma and Rui Ma for always being available to work with me on this project as I learned how to apply my skills in Computer Engineering in the field of Bioinformatics. I would also like to thank Claire Lu and Paimon Goulart for working alongside me on this project, and Dr. Analisa Flores, Dr. Mariam Salloum, and the National Science Foundation (NSF) for providing this opportunity.  \nTABLE OF CONTENTS  \nINTRODUCTION......................................................................................................................... 6  \nMETHODOLOGY........................................................................................................................ 7  \nI. Background Information..................................................................................................... 7  \nThe scHi-C Data.................................................................................................................. 7  \nThe Gene Annotations Data.................................................................................................9  \nThe scRNA-seq Data......................................................................................................... 10  \nII. Interpreting the scHi-C Data........................................................................................... 10  \nFiltering.............................................................................................................................. 10  \nBinning............................................................................................................................... 10  \nGrouping............................................................................................................................ 11  \nIII. Interpreting the Gene Annotations Data...................................................................... 12  \nThe Body Region............................................................................................................... 12  \nThe Promoter Region......................................................................................................... 13  \nIV. Mapping the Gene Annotations Data onto the scHi-C Data........................................ 14  \nAssigning Genes to the scHi-C Data..........................","cbCaip5N9PomsJcG","https://ap.wps.com/l/cbCaip5N9PomsJcG","pdf",3829931,1,31,"English","en",105,"# Introduction\n# Methodology\n## Background Information\n## Interpreting the scHi-C Data\n## Interpreting the Gene Annotations Data\n## Mapping the Gene Annotations Data onto the scHi-C Data\n## Creating the Score Matrix\n## The Integration Process\n# Results\n# Discussion\n# Conclusion\n# References","[{\"question\":\"What two datasets are integrated in this project?\",\"answer\":\"The project integrates scHi-C data and scRNA-seq data scanned from a developing mouse brain.\"},{\"question\":\"Why does the project recreate the scGAD algorithm from scratch?\",\"answer\":\"Recreating scGAD is used to confirm that the original research papers correctly support the algorithm, so further research can build new integration methods.\"},{\"question\":\"How is integration carried out at a high level?\",\"answer\":\"Integration uses inputs including scGAD and scRNA-seq data, cell types, and plotting of results, following the outlined interpretation and mapping steps.\"}]","Statistical Machine Learning Methods for Integrating Single-Cell Genomics Data | 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two datasets are integrated in this project?","Question",{"text":75,"@type":76},"The project integrates scHi-C data and scRNA-seq data scanned from a developing mouse brain.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why does the project recreate the scGAD algorithm from scratch?",{"text":80,"@type":76},"Recreating scGAD is used to confirm that the original research papers correctly support the algorithm, so further research can build new integration methods.",{"name":82,"@type":73,"acceptedAnswer":83},"How is integration carried out at a high level?",{"text":84,"@type":76},"Integration uses inputs including scGAD and scRNA-seq data, cell types, and plotting of results, following the outlined interpretation and mapping 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