Abstract
In this study, we introduce Robust estimation of Cell type proportions by Integrating single-reference-based DEconvolutions (ReCIDE), an innovative framework for robust estimation of cell type proportions by integrating single-reference-based deconvolutions. ReCIDE outperforms existing approaches in benchmark and real datasets, particularly excelling in estimating rare cell type proportions. Through exploratory analysis on public bulk data of triple-negative breast cancer (TNBC) patients using ReCIDE, we demonstrate a significant correlation between the prognosis of TNBC patients and the proportions of both T cell and perivascular-like cell subtypes. Built upon this discovery, we develop a prognostic assessment model for TNBC patients. Our contribution presents a novel framework for enhancing deconvolution accuracy, showcasing its effectiveness in medical research.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callSimilar Papers
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
