一种基于 PCA 空间中 DNA 甲基化的细胞去卷积新方法。

A novel method for cell deconvolution using DNA methylation in PCA space.

机构信息

Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.

出版信息

BMC Genomics. 2024 Aug 23;25(1):798. doi: 10.1186/s12864-024-10652-0.

DOI:10.1186/s12864-024-10652-0

PMID:39179972

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11344294/

Abstract

BACKGROUND

In this study, we present a novel method for reference-based cell deconvolution using data from DNA methylation arrays. Different from existing methods like IDOL-Ext, which operate on probe-level data, our approach represents features in the principal component analysis (PCA) space for cell type deconvolution.

RESULTS

Our method's accuracy in estimating cell compositions is validated across various public datasets, including blood samples from glioma patients. It demonstrates precision comparable to IDOL-Ext, with R values ranging from 0.73 to 0.99 for most cell types, while offering improved discrimination between similar cell types, particularly T cell subtypes in glioma patient samples (R 0.42-0.75 vs. 0.36-0.66 for IDOL-Ext). However, both methods showed lower accuracy for certain cell types, such as memory CD8 T cells in glioma patients (R 0.42 vs. 0.36 for IDOL-Ext), highlighting the challenges in distinguishing closely related cell populations. We have made this method available as an R package "BloodCellDecon" on GitHub.

CONCLUSIONS

Our study confirms the efficacy of cell type deconvolution in PCA space. The results indicate wide-ranging applicability and potential for adaptation to other forms of genomic data.

摘要

背景

在这项研究中，我们提出了一种新的基于参考的细胞去卷积方法，该方法使用 DNA 甲基化阵列的数据。与现有的方法（如 IDOL-Ext）不同，我们的方法在主成分分析（PCA）空间中表示细胞类型去卷积的特征。

结果

我们的方法在各种公共数据集（包括胶质母细胞瘤患者的血液样本）中对细胞成分的估计准确性得到了验证。它的精度与 IDOL-Ext 相当，大多数细胞类型的 R 值在 0.73 到 0.99 之间，而在区分相似细胞类型方面具有更好的性能，特别是在胶质母细胞瘤患者样本中的 T 细胞亚群（R 0.42-0.75 与 IDOL-Ext 的 0.36-0.66）。然而，这两种方法对某些细胞类型的准确性都较低，例如胶质母细胞瘤患者的记忆 CD8 T 细胞（IDOL-Ext 的 R 0.42 与 0.36），这突出了区分密切相关的细胞群体的挑战。我们已经在 GitHub 上提供了这个方法作为一个 R 包“BloodCellDecon”。