{"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/robust-lineage-reconstruction-from-high","title":"Robust Lineage Reconstruction from High-Dimensional Single-Cell Data","arxiv_id":"1601.02748","date":"2016-01-12","proceeding":null,"authors":["Gregory Giecold","Eugenio Marco","Lorenzo Trippa","Guo-Cheng Yuan"],"abstract":"Single-cell gene expression data provide invaluable resources for systematic\ncharacterization of cellular hierarchy in multi-cellular organisms. However,\ncell lineage reconstruction is still often associated with significant\nuncertainty due to technological constraints. Such uncertainties have not been\ntaken into account in current methods. We present ECLAIR, a novel computational\nmethod for the statistical inference of cell lineage relationships from\nsingle-cell gene expression data. ECLAIR uses an ensemble approach to improve\nthe robustness of lineage predictions, and provides a quantitative estimate of\nthe uncertainty of lineage branchings. We show that the application of ECLAIR\nto published datasets successfully reconstructs known lineage relationships and\nsignificantly improves the robustness of predictions. In conclusion, ECLAIR is\na powerful bioinformatics tool for single-cell data analysis. It can be used\nfor robust lineage reconstruction with quantitative estimate of prediction\naccuracy.","url_abs":"http://arxiv.org/abs/1601.02748v1","url_pdf":"http://arxiv.org/pdf/1601.02748v1.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":"robust-lineage-reconstruction-from-high","repo_url":"https://github.com/GGiecold/ECLAIR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"robust-lineage-reconstruction-from-high","repo_url":"https://github.com/GGiecold/Cluster-Ensembles","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"robust-lineage-reconstruction-from-high","repo_url":"https://github.com/GGiecold/Cluster_Ensembles","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"robust-lineage-reconstruction-from-high","repo_url":"https://github.com/kultzak/MCLA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}