{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fdr-corrected-sparse-canonical-correlation","title":"FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics","arxiv_id":"1705.04312","date":"2017-05-11","proceeding":null,"authors":["Alexej Gossmann","Pascal Zille","Vince Calhoun","Yu-Ping Wang"],"abstract":"Reducing the number of false discoveries is presently one of the most\npressing issues in the life sciences. It is of especially great importance for\nmany applications in neuroimaging and genomics, where datasets are typically\nhigh-dimensional, which means that the number of explanatory variables exceeds\nthe sample size. The false discovery rate (FDR) is a criterion that can be\nemployed to address that issue. Thus it has gained great popularity as a tool\nfor testing multiple hypotheses. Canonical correlation analysis (CCA) is a\nstatistical technique that is used to make sense of the cross-correlation of\ntwo sets of measurements collected on the same set of samples (e.g., brain\nimaging and genomic data for the same mental illness patients), and sparse CCA\nextends the classical method to high-dimensional settings. Here we propose a\nway of applying the FDR concept to sparse CCA, and a method to control the FDR.\nThe proposed FDR correction directly influences the sparsity of the solution,\nadapting it to the unknown true sparsity level. Theoretical derivation as well\nas simulation studies show that our procedure indeed keeps the FDR of the\ncanonical vectors below a user-specified target level. We apply the proposed\nmethod to an imaging genomics dataset from the Philadelphia Neurodevelopmental\nCohort. Our results link the brain connectivity profiles derived from brain\nactivity during an emotion identification task, as measured by functional\nmagnetic resonance imaging (fMRI), to the corresponding subjects' genomic data.","url_abs":"http://arxiv.org/abs/1705.04312v4","url_pdf":"http://arxiv.org/pdf/1705.04312v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fdr-corrected-sparse-canonical-correlation","repo_url":"https://github.com/agisga/FDRcorrectedSCCA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}