{"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/droplasso-a-robust-variant-of-lasso-for","title":"DropLasso: A robust variant of Lasso for single cell RNA-seq data","arxiv_id":"1802.09381","date":"2018-02-26","proceeding":null,"authors":["Beyrem Khalfaoui","Jean-Philippe Vert"],"abstract":"Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure\nthe genome-wide transcriptome of many individual cells in parallel, but results\nin noisy data with many dropout events. Existing methods to learn molecular\nsignatures from bulk transcriptomic data may therefore not be adapted to\nscRNA-seq data, in order to automatically classify individual cells into\npredefined classes. We propose a new method called DropLasso to learn a\nmolecular signature from scRNA-seq data. DropLasso extends the dropout\nregularisation technique, popular in neural network training, to esti- mate\nsparse linear models. It is well adapted to data corrupted by dropout noise,\nsuch as scRNA-seq data, and we clarify how it relates to elastic net\nregularisation. We provide promising results on simulated and real scRNA-seq\ndata, suggesting that DropLasso may be better adapted than standard regularisa-\ntions to infer molecular signatures from scRNA-seq data.","url_abs":"http://arxiv.org/abs/1802.09381v1","url_pdf":"http://arxiv.org/pdf/1802.09381v1.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":"droplasso-a-robust-variant-of-lasso-for","repo_url":"https://github.com/jpvert/droplasso","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"droplasso-a-robust-variant-of-lasso-for","repo_url":"https://github.com/DanielGoman/DropLasso","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"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}