Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease.

IF 4 Q1 GENETICS & HEREDITY
NAR Genomics and Bioinformatics Pub Date : 2024-08-06 eCollection Date: 2024-09-01 DOI:10.1093/nargab/lqae098
Yuanhang Liu, Robert A Vierkant, Aditya Bhagwate, William A Jons, Melody L Stallings-Mann, Bryan M McCauley, Jodi M Carter, Melissa T Stephens, Michael E Pfrender, Laurie E Littlepage, Derek C Radisky, Julie M Cunningham, Amy C Degnim, Stacey J Winham, Chen Wang
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引用次数: 0

Abstract

Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.

评价FFPE乳腺组织细胞型反褶积:在乳腺良性疾病中的应用。
利用RNA测序(RNA-seq)对散装福尔马林固定石蜡包埋(FFPE)组织块进行转录组分析是生物医学研究的标准方法。然而,当用于具有不同细胞类型组成的组织时,它产生平均基因表达谱,由于细胞比例的变化而使生物标志物鉴定复杂化。为了解决从大量FFPE样品中定义单个细胞类型组成的优化策略的需求,我们构建了乳腺组织的单细胞RNA-seq参考数据,并测试了细胞类型反褶积方法。初步仿真实验表明,多种常用的反褶积方法具有相似的性能。然而,FFPE伪影的引入显著影响了它们的性能,均方根误差(RMSE)在0.04到0.17之间。Scaden是一种基于深度学习的方法,其表现始终优于其他方法,证明了对FFPE伪像的鲁棒性。在我们的62个样本RNA-seq良性乳腺疾病队列中测试这些方法,其中使用数字病理学方法估计细胞类型组成,我们发现参考数据的预过滤提高了大多数方法的准确性,实现RMSE降低高达32%。为了支持该领域的进一步研究工作,我们推出了SCdeconR,这是一个设计用于流线型细胞类型反褶积评估和下游分析的R包。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
2.20%
发文量
95
审稿时长
15 weeks
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