[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125169-en":3,"doc-seo-125169-105":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},125169,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods","Protocol describing how to reconcile differences in machine-learning (ML) classifier features derived from gene-expression data. The method loads the PathwaySpace R package, prepares analysis inputs, and builds pathway-space visualizations using density plots of gene sets. It further provides procedures to test whether seemingly distinct feature sets are related within pathway space, using a pathway-distance metric to quantify statistical distances between gene-list relationships for subtype-relevant biology.","UC Santa Cruz  \nUC Santa Cruz Previously Published Works  \nTitle  \nProtocol for assessing distances in pathway space for classifier feature sets from machine learning methods.  \nPermalink  \n[https://escholarship.org/uc/item/7r6921kj](https://escholarship.org/uc/item/7r6921kj)  \nJournal  \nSTAR Protocols, 6(2)  \nAuthors  \nTercan, Bahar  \nApolonio, Victor Chagas, Vinicius et al.  \nPublication Date  \n2025-03-18  \nDOI  \n10.1016/j.xpro.2025.103681  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nll  \nOPEN ACCESS  \nProtocol  \nProtocol for assessing distances in pathway space for classifier feature sets from machine learning methods  \nBahar Tercan, Victor H. Apolonio, Vinicius S. Chagas, ..., Peter W. Laird, Andrew D. Cherniack, Mauro A.A. Castro  \nbtercan@systemsbiology. org (B.T.) [peter.laird@vai.org](peter.laird@vai.org)[ ](peter.laird@vai.org)(P.W. L.)  \nachernia@broadinstitute. org (A. D.C.) [mauro.castro@ufpr.br](mauro.castro@ufpr.br)[ ](mauro.castro@ufpr.br)(M.A.A.C.)  \nHighlights  \nProtocoltotestwhether distinct gene sets reflect related biology  \nSteps for building a pathway space for graph-or networkbased distance analysis  \nInstructions for calculating a pathway distance metric for any pair of gene sets  \nGuidance on exploring relationships between gene sets in pathway space  \nTercan et al., STAR Protocols 6, 103681  \nJune 20, 2025 ª 2025 The Authors. Published by Elsevier Inc.  \n[https://doi.org/10.1016/](https://doi.org/10.1016/)[ ](https://doi.org/10.1016/)[j.xpro.2025.103681](j.xpro.2025.103681)  \nll  \nOPEN ACCESS  \nProtocol  \nProtocol for assessing distances in pathway space forclassifier feature sets from machine learning methods  \nBahar Tercan,1, 15, 16,* Victor H. Apolonio,2, 15 Vinicius S. Chagas,2 Christopher K. Wong,3 JordanA. Lee,4  \nChristina Yau,5,6 Christopher C. Benz,6 Joshua M. Stuart,3 Brian J. Karlberg,4 Kyle Ellrott,4 Jasleen K. Grewal,7, 13 Steven J. M. Jones,7 The Cancer Genome Atlas Analysis Network, Jean C. Zenklusen,8 A. Gordon Robertson,7, 14 Peter W. Laird,9,* Andrew D. Cherniack, 10, 11, 12,* and Mauro A.A. Castro2, 17,*  \n1Institute of Systems Biology, 401 Terry Avenue North, Seattle, WA 98109, USA  \n2Bioinformatics and Systems Biology Laboratory, Federal University of Parana´, Curitiba, PR 81520-260, Brazil  \n3UC Santa Cruz Genomics Institute and Department of Biomolecular Engineering, Santa Cruz, CA 95060, USA  \n4Oregon Health and Science University, Portland, OR 97239, USA  \n5University of California, San Francisco, Department of Surgery, San Francisco, CA 94158, USA  \n6Buck Institute for Research on Aging, Novato, CA 94945, USA  \n7Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada  \n8Center for Cancer Genomics, National Cancer Institute, Bethesda, MD 20892, USA  \n9Department of Epigenetics, Van Andel Institute, Grand Rapids, MI 49503, USA  \n10The Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA  \n11Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA  \n12Harvard Medical School, Boston, MA 02115, USA  \n13Present address: NVIDIA Corporation, Santa Clara, CA, USA  \n14Present address: Dxige Research Inc., Courtenay, BC V9N 1C2, Canada  \n15These authors contributed equally  \n16Technical contact  \n17Lead contact  \n*Correspondence: [btercan@systemsbiology.org](btercan@systemsbiology.org) (B.T.), [peter.laird@vai.org](peter.laird@vai.org) (P.W. L.), [achernia@broadinstitute.org](achernia@broadinstitute.org) (A. D.C.), [mauro.castro@ufpr.br](mauro.castro@ufpr.br) (M.A.A.C.)  \n[https://doi.org/10.1016/j.xpro.2025.103681](https://doi.org/10.1016/j.xpro.2025.103681)  \nSUMMARY  \nAs genes tend to be co-regulated as gene modules, feature selection in machine learning (ML) on gene expression data can be challenged by the complexity of gene regulation. Here, we present a protocol for reconciling differences in classifier features identified using different ML approa","cbCainhjxz5a2Hk5","https://ap.wps.com/l/cbCainhjxz5a2Hk5","pdf",3390155,1,12,"English","en",105,"# Overview\n## Protocol goals and rationale\n# Pathway space construction\n## Loading PathwaySpace and preparing inputs\n## Creating density plots of gene sets\n# Pathway distance assessment\n## Building and applying the pathway distance metric\n## Testing relationships between gene sets in pathway space","[{\"question\":\"What problem does the PathwaySpace protocol address?\",\"answer\":\"It addresses how to compare pathway distances between gene sets and determine whether distinct ML-derived classifier feature sets reflect related biology.\"},{\"question\":\"What are the main steps in executing the protocol?\",\"answer\":\"Load the PathwaySpace R package, prepare inputs for pathway-space analysis, construct pathway-space visualizations (including density plots), and then run pathway-distance procedures for gene-set comparisons.\"},{\"question\":\"How does the protocol evaluate relationships between gene sets?\",\"answer\":\"It uses a pathway-distance metric and provides procedures to test whether apparently distinct feature sets are related in pathway space, supporting interpretation for pathway- and subtype-relevant biology.\"}]","Protocol for assessing distances in pathway space for classifier feature sets from machine learning methods | 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problem does the PathwaySpace protocol address?","Question",{"text":75,"@type":76},"It addresses how to compare pathway distances between gene sets and determine whether distinct ML-derived classifier feature sets reflect related biology.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the main steps in executing the protocol?",{"text":80,"@type":76},"Load the PathwaySpace R package, prepare inputs for pathway-space analysis, construct pathway-space visualizations (including density plots), and then run pathway-distance procedures for gene-set comparisons.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the protocol evaluate relationships between gene sets?",{"text":84,"@type":76},"It uses a pathway-distance metric and provides procedures to test whether apparently distinct feature sets are related in pathway space, supporting interpretation for pathway- and subtype-relevant 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