In recent years,computational methods for quantifying cell-type proportions from transcription data have gained significant attention,particu-larly those reference-based methods which have demonstrated high accuracy.H...In recent years,computational methods for quantifying cell-type proportions from transcription data have gained significant attention,particu-larly those reference-based methods which have demonstrated high accuracy.However,there is currently a lack of comprehensive evaluation and guidance for available reference-based deconvolution methods in cell-type deconvolution analysis.In this study,we introduce Deconvolution Evaluator(Deconer),a comprehensive toolkit for the evaluation of reference-based deconvolution methods.Deconer provides various simulated and real gene expression datasets,including both bulk and single-cell sequencing data,and offers multiple visualization inter-faces.By utilizing Deconer,we conducted systematic comparisons of 16 reference-based deconvolution methods from different perspectives,including method robustness,accuracy in deconvolving rare components,signature gene selection performance,and external reference con-struction capability.We also performed an in-depth analysis of the application scenarios and challenges in cell-type deconvolution methods.Finally,we provided constructive suggestions for users to select and develop cell-type deconvolution algorithms.This study provides novel insights for researchers,assisting them in choosing appropriate toolkits,applying solutions in clinical contexts,and advancing the development of deconvolution tools tailored to gene expression data.The tutorials,manual,source code,and demo data of Deconer are publicly available at https:/honchkrow.github.io/Deconer/andhttps://ngdc.cncb.ac.cn/biocode/tool/7577.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62303271,92374107,and 62373216)the National Key R&D Program of China(Grant No.2020YFA0712402)+2 种基金the Natural Science Foundation of Shandong Province,China(Grant No.ZR2023QF081)the Fundamental Research Funds for the Central Universities,China(Grant No.2022JC008)the National Institutes ofHealth,USA(Grant No.K99HG013661).
文摘In recent years,computational methods for quantifying cell-type proportions from transcription data have gained significant attention,particu-larly those reference-based methods which have demonstrated high accuracy.However,there is currently a lack of comprehensive evaluation and guidance for available reference-based deconvolution methods in cell-type deconvolution analysis.In this study,we introduce Deconvolution Evaluator(Deconer),a comprehensive toolkit for the evaluation of reference-based deconvolution methods.Deconer provides various simulated and real gene expression datasets,including both bulk and single-cell sequencing data,and offers multiple visualization inter-faces.By utilizing Deconer,we conducted systematic comparisons of 16 reference-based deconvolution methods from different perspectives,including method robustness,accuracy in deconvolving rare components,signature gene selection performance,and external reference con-struction capability.We also performed an in-depth analysis of the application scenarios and challenges in cell-type deconvolution methods.Finally,we provided constructive suggestions for users to select and develop cell-type deconvolution algorithms.This study provides novel insights for researchers,assisting them in choosing appropriate toolkits,applying solutions in clinical contexts,and advancing the development of deconvolution tools tailored to gene expression data.The tutorials,manual,source code,and demo data of Deconer are publicly available at https:/honchkrow.github.io/Deconer/andhttps://ngdc.cncb.ac.cn/biocode/tool/7577.
