Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.
Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition.
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使用约束矩阵分解对大规模转译组数据进行可解释、可伸缩和可转移的功能投影。
DOI：
10.3389/fgene.2021.719099
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发表时间：
2021
影响因子：
3.7
通讯作者：
Hong T
中科院分区：
文献类型：
--
作者：
Panchy N;Watanabe K;Hong T
大规模转录组数据，如单细胞rna测序数据，为在系统水平上研究生物过程提供了前所未有的资源。已经开发了许多降维方法来可视化和分析这些转录组数据。此外，一些现有的方法允许使用具有已知生物学功能的基因集来推断样品之间的功能变化。然而，用降维分析转录组仍然具有挑战性，这些降维可以根据维度的方向性进行解释，可转移到新的数据中，并直接揭示单个基因的贡献或关联。在这项研究中，我们使用基因集非负主成分分析（gsPCA）和非负矩阵分解（gsNMF）来分析大规模转录组数据集。我们发现这些方法以定量的方式提供了关于生物过程进展的低维信息，并且在区分多种细胞状态和多种条件下的样品方面，它们的性能与现有的功能变异分析方法相当。值得注意的是，在使用数据子集进行训练后，这些方法允许使用未暴露于模型的实验条件数据来预测功能空间中的位置。具体来说，我们的模型预测了上皮-间质转化（EMT）连续体中细胞的进展和逆转程度。这些方法揭示了多种类型的单细胞和肿瘤样本中保守的EMT程序。最后，我们证明了这种方法广泛适用于EMT以外的数据和基因集，并就两种线性方法和最佳算法参数之间的选择提供了一些建议。我们的方法表明，简单的约束矩阵分解可以在功能可解释和可转移的空间中产生低维信息，并且可以广泛用于分析大规模转录组数据。
Large-scale transcriptome data, such as single-cell RNA-sequencing data, have provided unprecedented resources for studying biological processes at the systems level. Numerous dimensionality reduction methods have been developed to visualize and analyze these transcriptome data. In addition, several existing methods allow inference of functional variations among samples using gene sets with known biological functions. However, it remains challenging to analyze transcriptomes with reduced dimensions that are interpretable in terms of dimensions’ directionalities, transferrable to new data, and directly expose the contribution or association of individual genes. In this study, we used gene set non-negative principal component analysis (gsPCA) and non-negative matrix factorization (gsNMF) to analyze large-scale transcriptome datasets. We found that these methods provide low-dimensional information about the progression of biological processes in a quantitative manner, and their performances are comparable to existing functional variation analysis methods in terms of distinguishing multiple cell states and samples from multiple conditions. Remarkably, upon training with a subset of data, these methods allow predictions of locations in the functional space using data from experimental conditions that are not exposed to the models. Specifically, our models predicted the extent of progression and reversion for cells in the epithelial-mesenchymal transition (EMT) continuum. These methods revealed conserved EMT program among multiple types of single cells and tumor samples. Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.
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