ScInfoVAE: interpretable dimensional reduction of single cell transcription data with variational autoencoders and extended mutual information regularization.

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-06-10 DOI:10.1186/s13040-023-00333-1
Weiquan Pan, Faning Long, Jian Pan
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Abstract

Single-cell RNA-sequencing (scRNA-seq) data can serve as a good indicator of cell-to-cell heterogeneity and can aid in the study of cell growth by identifying cell types. Recently, advances in Variational Autoencoder (VAE) have demonstrated their ability to learn robust feature representations for scRNA-seq. However, it has been observed that VAEs tend to ignore the latent variables when combined with a decoding distribution that is too flexible. In this paper, we introduce ScInfoVAE, a dimensional reduction method based on the mutual information variational autoencoder (InfoVAE), which can more effectively identify various cell types in scRNA-seq data of complex tissues. A joint InfoVAE deep model and zero-inflated negative binomial distributed model design based on ScInfoVAE reconstructs the objective function to noise scRNA-seq data and learn an efficient low-dimensional representation of it. We use ScInfoVAE to analyze the clustering performance of 15 real scRNA-seq datasets and demonstrate that our method provides high clustering performance. In addition, we use simulated data to investigate the interpretability of feature extraction, and visualization results show that the low-dimensional representation learned by ScInfoVAE retains local and global neighborhood structure data well. In addition, our model can significantly improve the quality of the variational posterior.

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使用变分自编码器和扩展互信息正则化的单细胞转录数据的可解释降维。
单细胞rna测序(scRNA-seq)数据可以作为细胞间异质性的良好指标,并可以通过识别细胞类型来帮助研究细胞生长。最近,变分自编码器(VAE)的进展已经证明了它们能够学习scRNA-seq的鲁棒特征表示。然而,已经观察到,当与过于灵活的解码分布相结合时,VAEs倾向于忽略潜在变量。本文介绍了一种基于互信息变分自编码器(InfoVAE)的降维方法sciinfovae,该方法可以更有效地识别复杂组织scRNA-seq数据中的各种细胞类型。基于ScInfoVAE的联合InfoVAE深度模型和零膨胀负二项分布模型设计,对scRNA-seq数据重构目标函数,并学习其高效的低维表示。利用ScInfoVAE对15个真实scRNA-seq数据集的聚类性能进行了分析,结果表明该方法具有较高的聚类性能。此外,我们利用模拟数据研究了特征提取的可解释性,可视化结果表明,ScInfoVAE学习的低维表示能很好地保留局部和全局邻域结构数据。此外,我们的模型可以显著提高变分后验的质量。
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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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