论文标题
用于推断细胞基因表达动力学的ODE的变化混合物
Variational Mixtures of ODEs for Inferring Cellular Gene Expression Dynamics
论文作者
论文摘要
计算生物学的一个关键问题是发现基因表达变化，该基因表达会调节细胞命运转变，其中一种细胞类型变成另一种细胞类型。但是，每个单个细胞都不能纵向跟踪，并且在同一时间内实时的细胞可能处于过渡过程的不同阶段。这可以看作是从未知的观察结果中学习动态系统行为的问题。另外，单个祖细胞类型通常会分叉成多种儿童细胞类型，从而使模拟动力学的问题变得复杂。为了解决这个问题，我们开发了一种称为普通微分方程的变分混合物的方法。通过使用基因表达的生物化学告知的简单odes家族来限制深层生成模型的可能性，我们可以同时推断每个细胞的潜在时间和潜在状态并预测其未来的基因表达状态。该模型可以解释为ODE的混合物，其参数在细胞状态的潜在空间中连续变化。与以前的方法相比，我们的方法极大地改善了单细胞基因表达数据的数据拟合，潜在时间推断和未来的细胞状态估计。
A key problem in computational biology is discovering the gene expression changes that regulate cell fate transitions, in which one cell type turns into another. However, each individual cell cannot be tracked longitudinally, and cells at the same point in real time may be at different stages of the transition process. This can be viewed as a problem of learning the behavior of a dynamical system from observations whose times are unknown. Additionally, a single progenitor cell type often bifurcates into multiple child cell types, further complicating the problem of modeling the dynamics. To address this problem, we developed an approach called variational mixtures of ordinary differential equations. By using a simple family of ODEs informed by the biochemistry of gene expression to constrain the likelihood of a deep generative model, we can simultaneously infer the latent time and latent state of each cell and predict its future gene expression state. The model can be interpreted as a mixture of ODEs whose parameters vary continuously across a latent space of cell states. Our approach dramatically improves data fit, latent time inference, and future cell state estimation of single-cell gene expression data compared to previous approaches.
