DeepAutoGlioma: a deep learning autoencoder-based multi-omics data integration and classification tools for glioma subtyping.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-11-15 DOI:10.1186/s13040-023-00349-7
Sana Munquad, Asim Bikas Das
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Abstract

Background and objective: The classification of glioma subtypes is essential for precision therapy. Due to the heterogeneity of gliomas, the subtype-specific molecular pattern can be captured by integrating and analyzing high-throughput omics data from different genomic layers. The development of a deep-learning framework enables the integration of multi-omics data to classify the glioma subtypes to support the clinical diagnosis.

Results: Transcriptome and methylome data of glioma patients were preprocessed, and differentially expressed features from both datasets were identified. Subsequently, a Cox regression analysis determined genes and CpGs associated with survival. Gene set enrichment analysis was carried out to examine the biological significance of the features. Further, we identified CpG and gene pairs by mapping them in the promoter region of corresponding genes. The methylation and gene expression levels of these CpGs and genes were embedded in a lower-dimensional space with an autoencoder. Next, ANN and CNN were used to classify subtypes using the latent features from embedding space. CNN performs better than ANN for subtyping lower-grade gliomas (LGG) and glioblastoma multiforme (GBM). The subtyping accuracy of CNN was 98.03% (± 0.06) and 94.07% (± 0.01) in LGG and GBM, respectively. The precision of the models was 97.67% in LGG and 90.40% in GBM. The model sensitivity was 96.96% in LGG and 91.18% in GBM. Additionally, we observed the superior performance of CNN with external datasets. The genes and CpGs pairs used to develop the model showed better performance than the random CpGs-gene pairs, preprocessed data, and single omics data.

Conclusions: The current study showed that a novel feature selection and data integration strategy led to the development of DeepAutoGlioma, an effective framework for diagnosing glioma subtypes.

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DeepAutoGlioma:一个基于深度学习自动编码器的多组学数据集成和分类工具,用于胶质瘤亚型分型。
背景与目的:胶质瘤亚型的分类是精确治疗的基础。由于胶质瘤的异质性,可以通过整合和分析来自不同基因组层的高通量组学数据来捕获亚型特异性分子模式。深度学习框架的开发使多组学数据的集成能够对胶质瘤亚型进行分类,以支持临床诊断。结果:对胶质瘤患者的转录组和甲基组数据进行预处理,并从两个数据集中识别出差异表达特征。随后,Cox回归分析确定了与生存相关的基因和CpGs。进行基因集富集分析以检验这些特征的生物学意义。此外,我们通过在相应基因的启动子区域定位CpG和基因对来鉴定它们。这些CpGs和基因的甲基化和基因表达水平通过自编码器嵌入到低维空间中。然后,利用嵌入空间的潜在特征,利用ANN和CNN对子类型进行分类。CNN对低级别胶质瘤(LGG)和多形性胶质母细胞瘤(GBM)的分型优于ANN。CNN在LGG和GBM的亚型分型准确率分别为98.03%(±0.06)和94.07%(±0.01)。模型在LGG和GBM中的精度分别为97.67%和90.40%。模型敏感性在LGG为96.96%,在GBM为91.18%。此外,我们观察到CNN在外部数据集上的优越性能。与随机CpGs-基因对、预处理数据和单组学数据相比,用于构建模型的基因和CpGs对具有更好的性能。结论:目前的研究表明,一种新的特征选择和数据整合策略导致了DeepAutoGlioma的发展,这是一种诊断胶质瘤亚型的有效框架。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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