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BEDwARS:一种具有稳健性的贝叶斯方法,可用于对具有噪声参考特征的批量基因表达解卷积。

BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures.

机构信息

Department of Computer Science, University of Illinois at Urbana-Champaign, Thomas M. Siebel Center, 201 N. Goodwin Ave., Urbana, IL, USA.

Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Gonda 19-476, 200 First St. SW, Rochester, MN, 55905, USA.

出版信息

Genome Biol. 2023 Aug 3;24(1):178. doi: 10.1186/s13059-023-03007-7.

Abstract

Differential gene expression in bulk transcriptomics data can reflect change of transcript abundance within a cell type and/or change in the proportions of cell types. Expression deconvolution methods can help differentiate these scenarios. BEDwARS is a Bayesian deconvolution method designed to address differences between reference signatures of cell types and corresponding true signatures underlying bulk transcriptomic profiles. BEDwARS is more robust to noisy reference signatures and outperforms leading in-class methods for estimating cell type proportions and signatures. Application of BEDwARS to dihydropyridine dehydrogenase deficiency identified the possible involvement of ciliopathy and impaired translational control in the etiology of the disorder.

摘要

批量转录组学数据中的差异基因表达可以反映细胞类型内转录本丰度的变化和/或细胞类型比例的变化。表达去卷积方法可以帮助区分这些情况。BEDwARS 是一种贝叶斯去卷积方法,旨在解决细胞类型参考特征与批量转录组特征下的真实特征之间的差异。BEDwARS 对嘈杂的参考特征更稳健,在估计细胞类型比例和特征方面优于同类领先方法。BEDwARS 在二氢吡啶脱氢酶缺乏症中的应用确定了纤毛病和翻译控制受损可能参与该疾病的发病机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0eb5/10399072/26212adac2e5/13059_2023_3007_Fig1_HTML.jpg

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