{"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/protrank-bypassing-the-imputation-of-missing","title":"ProtRank: Bypassing the imputation of missing values in differential expression analysis of proteomic data","arxiv_id":"1909.13667","date":"2019-09-30","proceeding":null,"authors":[],"abstract":"Data from discovery proteomic and phosphoproteomic experiments typically\ninclude missing values that correspond to proteins that have not been\nidentified in the analyzed sample. Replacing the missing values with random\nnumbers, a process known as \"imputation\", avoids apparent infinite fold-change\nvalues. However, the procedure comes at a cost: Imputing a large number of\nmissing values has the potential to significantly impact the results of the\nsubsequent differential expression analysis. We propose a method that\nidentifies differentially expressed proteins by ranking their observed changes\nwith respect to the changes observed for other proteins. Missing values are\ntaken into account by this method directly, without the need to impute them. We\nillustrate the performance of the new method on two distinct datasets and show\nthat it is robust to missing values and, at the same time, provides results\nthat are otherwise similar to those obtained with edgeR which is a state-of-art\ndifferential expression analysis method. The new method for the differential\nexpression analysis of proteomic data is available as an easy to use Python\npackage.","url_abs":"http://arxiv.org/abs/1909.13667v1","url_pdf":"http://arxiv.org/pdf/1909.13667v1.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":"protrank-bypassing-the-imputation-of-missing","repo_url":"https://github.com/8medom/ProtRank","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"imputation","task_name":"Imputation"},{"task_slug":"missing-values","task_name":"Missing Values"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}